From 0aa83cfab85ebe5cf4dd056ab14b015045884a01 Mon Sep 17 00:00:00 2001 From: Kcguner Date: Mon, 23 Mar 2026 23:41:40 +0300 Subject: [PATCH 1/2] feat(desktop): Complete GUI Remaster (Industrial Theme) + Automated OS Installers --- .githooks/pre-push | 0 .github/workflows/ci.yml | 17 +- .github/workflows/deploy-pages.yml | 53 + .github/workflows/docker.yml | 1 + .github/workflows/release-desktop.yml | 88 +- .github/workflows/release.yml | 3 +- .gitignore | 15 +- AGENTS.md | 170 - Cargo.lock | 265 +- Cargo.toml | 4 +- README.md | 754 +- assets/desktop-dark.png | Bin 0 -> 235193 bytes assets/desktop-light.png | Bin 0 -> 116204 bytes data/hf_models.json | 8098 ++++++----------- install.sh | 0 llmfit-core/Cargo.toml | 1 - llmfit-core/data/hf_models.json | 7916 ++++++---------- llmfit-core/src/fit.rs | 64 +- llmfit-core/src/hardware.rs | 74 +- llmfit-core/src/models.rs | 518 +- llmfit-core/src/plan.rs | 4 - llmfit-core/src/providers.rs | 17 +- llmfit-desktop/frontend/components.json | 21 + llmfit-desktop/frontend/dev_error.txt | 22 + llmfit-desktop/frontend/index.html | 12 + llmfit-desktop/frontend/jsconfig.json | 8 + llmfit-desktop/frontend/package-lock.json | 6269 +++++++++++++ llmfit-desktop/frontend/package.json | 37 + llmfit-desktop/frontend/postcss.config.js | 6 + llmfit-desktop/frontend/src/App.jsx | 483 + llmfit-desktop/frontend/src/App.test.jsx | 187 + llmfit-desktop/frontend/src/api.js | 97 + llmfit-desktop/frontend/src/api.test.js | 181 + .../frontend/src/components/ui/badge.tsx | 36 + .../frontend/src/components/ui/button.tsx | 57 + .../frontend/src/components/ui/card.tsx | 76 + .../frontend/src/components/ui/input.tsx | 22 + .../frontend/src/components/ui/label.tsx | 24 + .../frontend/src/components/ui/progress.tsx | 26 + .../frontend/src/components/ui/select.tsx | 159 + .../frontend/src/components/ui/table.tsx | 120 + llmfit-desktop/frontend/src/index.css | 70 + llmfit-desktop/frontend/src/lib/utils.js | 6 + llmfit-desktop/frontend/src/main.jsx | 10 + llmfit-desktop/frontend/src/test-setup.js | 1 + llmfit-desktop/frontend/tailwind.config.js | 61 + llmfit-desktop/frontend/vite.config.js | 30 + llmfit-desktop/frontend/vitest.config.js | 11 + llmfit-desktop/icons/128x128.png | Bin 5287 -> 10469 bytes llmfit-desktop/icons/128x128@2x.png | Bin 0 -> 11787 bytes llmfit-desktop/icons/32x32.png | Bin 1477 -> 2106 bytes llmfit-desktop/icons/64x64.png | Bin 0 -> 5070 bytes llmfit-desktop/icons/Square107x107Logo.png | Bin 0 -> 9484 bytes llmfit-desktop/icons/Square142x142Logo.png | Bin 0 -> 13113 bytes llmfit-desktop/icons/Square150x150Logo.png | Bin 0 -> 13840 bytes llmfit-desktop/icons/Square284x284Logo.png | Bin 0 -> 28958 bytes llmfit-desktop/icons/Square30x30Logo.png | Bin 0 -> 1952 bytes llmfit-desktop/icons/Square310x310Logo.png | Bin 0 -> 32872 bytes llmfit-desktop/icons/Square44x44Logo.png | Bin 0 -> 3207 bytes llmfit-desktop/icons/Square71x71Logo.png | Bin 0 -> 5964 bytes llmfit-desktop/icons/Square89x89Logo.png | Bin 0 -> 7990 bytes llmfit-desktop/icons/StoreLogo.png | Bin 0 -> 3766 bytes .../android/mipmap-anydpi-v26/ic_launcher.xml | 5 + .../icons/android/mipmap-hdpi/ic_launcher.png | Bin 0 -> 3377 bytes .../mipmap-hdpi/ic_launcher_foreground.png | Bin 0 -> 14579 bytes .../android/mipmap-hdpi/ic_launcher_round.png | Bin 0 -> 3359 bytes .../icons/android/mipmap-mdpi/ic_launcher.png | Bin 0 -> 3273 bytes .../mipmap-mdpi/ic_launcher_foreground.png | Bin 0 -> 9648 bytes .../android/mipmap-mdpi/ic_launcher_round.png | Bin 0 -> 3118 bytes .../android/mipmap-xhdpi/ic_launcher.png | Bin 0 -> 7740 bytes .../mipmap-xhdpi/ic_launcher_foreground.png | Bin 0 -> 19973 bytes .../mipmap-xhdpi/ic_launcher_round.png | Bin 0 -> 7409 bytes .../android/mipmap-xxhdpi/ic_launcher.png | Bin 0 -> 12914 bytes .../mipmap-xxhdpi/ic_launcher_foreground.png | Bin 0 -> 35938 bytes .../mipmap-xxhdpi/ic_launcher_round.png | Bin 0 -> 12611 bytes .../android/mipmap-xxxhdpi/ic_launcher.png | Bin 0 -> 16646 bytes 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llmfit-tui/src/tui_ui.rs | 45 +- llmfit-web/components.json | 21 + llmfit-web/jsconfig.json | 8 + llmfit-web/package-lock.json | 2461 ++++- llmfit-web/package.json | 12 + llmfit-web/postcss.config.js | 6 + llmfit-web/src/App.jsx | 744 +- llmfit-web/src/api.js | 139 +- llmfit-web/src/components/ui/badge.tsx | 36 + llmfit-web/src/components/ui/button.tsx | 57 + llmfit-web/src/components/ui/card.tsx | 76 + llmfit-web/src/components/ui/input.tsx | 22 + llmfit-web/src/components/ui/label.tsx | 24 + llmfit-web/src/components/ui/progress.tsx | 26 + llmfit-web/src/components/ui/select.tsx | 159 + llmfit-web/src/components/ui/table.tsx | 120 + llmfit-web/src/index.css | 68 + llmfit-web/src/lib/utils.js | 6 + llmfit-web/src/main.jsx | 2 +- llmfit-web/tailwind.config.js | 57 + llmfit-web/vite.config.js | 10 + package-lock.json | 6 + scripts/install-openclaw-skill.sh | 0 scripts/test_api.py | 0 scripts/update_models.sh | 0 skills/llmfit-advisor/SKILL.md | 224 - tmp.gif | Bin 2127564 -> 0 bytes 140 files changed, 17856 insertions(+), 14121 deletions(-) mode change 100755 => 100644 .githooks/pre-push create mode 100644 .github/workflows/deploy-pages.yml delete mode 100644 AGENTS.md create mode 100644 assets/desktop-dark.png create mode 100644 assets/desktop-light.png mode change 100755 => 100644 install.sh create mode 100644 llmfit-desktop/frontend/components.json create mode 100644 llmfit-desktop/frontend/dev_error.txt create mode 100644 llmfit-desktop/frontend/index.html create mode 100644 llmfit-desktop/frontend/jsconfig.json create mode 100644 llmfit-desktop/frontend/package-lock.json create mode 100644 llmfit-desktop/frontend/package.json create mode 100644 llmfit-desktop/frontend/postcss.config.js create mode 100644 llmfit-desktop/frontend/src/App.jsx create mode 100644 llmfit-desktop/frontend/src/App.test.jsx create mode 100644 llmfit-desktop/frontend/src/api.js create mode 100644 llmfit-desktop/frontend/src/api.test.js create mode 100644 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100644 llmfit-web/src/components/ui/input.tsx create mode 100644 llmfit-web/src/components/ui/label.tsx create mode 100644 llmfit-web/src/components/ui/progress.tsx create mode 100644 llmfit-web/src/components/ui/select.tsx create mode 100644 llmfit-web/src/components/ui/table.tsx create mode 100644 llmfit-web/src/index.css create mode 100644 llmfit-web/src/lib/utils.js create mode 100644 llmfit-web/tailwind.config.js create mode 100644 package-lock.json mode change 100755 => 100644 scripts/install-openclaw-skill.sh mode change 100755 => 100644 scripts/test_api.py mode change 100755 => 100644 scripts/update_models.sh delete mode 100644 skills/llmfit-advisor/SKILL.md delete mode 100644 tmp.gif diff --git a/.githooks/pre-push b/.githooks/pre-push old mode 100755 new mode 100644 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 46a939d4b..aa5176f7a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,22 +1,7 @@ name: CI on: - push: - branches: - - main # Run after merging into the "main" target - paths: # Run only if files relevant to CI have changed (i.e. not install.sh, scripts or .github/worfklows) - - 'llmfit-*/**' - - 'Cargo.*o*' - pull_request: - branches: - - main # Run in PRs targeting the "main" branch - paths: - - 'llmfit-*/**' - - 'Cargo.*o*' - types: # Avoid low-impact events like "edited" or "labeled" - - opened - - synchronize - - reopened + workflow_dispatch: # Manual only β€” original upstream CI env: CARGO_TERM_COLOR: always diff --git a/.github/workflows/deploy-pages.yml b/.github/workflows/deploy-pages.yml new file mode 100644 index 000000000..e2f8c4a61 --- /dev/null +++ b/.github/workflows/deploy-pages.yml @@ -0,0 +1,53 @@ +name: Deploy to GitHub Pages + +on: + workflow_dispatch: # Manual only β€” Pages not configured in this fork + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + build: + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./llmfit-web + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Node + uses: actions/setup-node@v4 + with: + node-version: 20 + cache: 'npm' + cache-dependency-path: './llmfit-web/package-lock.json' + + - name: Install dependencies + run: npm ci + + - name: Build + # Set base url to repository name dynamically + run: npm run build -- --base=/${{ github.event.repository.name }}/ + + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + path: ./llmfit-web/dist + + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + needs: build + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 198c2fc97..bdbdca30f 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -5,6 +5,7 @@ on: tags: - "v*" - "!v*-mac" + - "!v*-desktop" workflow_dispatch: # Allow manual trigger permissions: diff --git a/.github/workflows/release-desktop.yml b/.github/workflows/release-desktop.yml index cd635f4b2..f2a3667a8 100644 --- a/.github/workflows/release-desktop.yml +++ b/.github/workflows/release-desktop.yml @@ -1,9 +1,10 @@ -name: Release Desktop (macOS) +name: Release Desktop (Multi-OS) on: push: tags: - "v*-mac" + - "v*-desktop" permissions: contents: write @@ -14,12 +15,17 @@ env: jobs: build-desktop: strategy: + fail-fast: false matrix: include: - target: aarch64-apple-darwin os: macos-latest - target: x86_64-apple-darwin os: macos-latest + - target: x86_64-pc-windows-msvc + os: windows-latest + - target: x86_64-unknown-linux-gnu + os: ubuntu-22.04 runs-on: ${{ matrix.os }} @@ -27,6 +33,17 @@ jobs: - name: Checkout uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 + - name: Install dependencies (Ubuntu only) + if: matrix.os == 'ubuntu-22.04' + run: | + sudo apt-get update + sudo apt-get install -y libwebkit2gtk-4.1-dev build-essential curl wget file libssl-dev libgtk-3-dev libayatana-appindicator3-dev librsvg2-dev + + - name: Install Node.js + uses: actions/setup-node@v4 + with: + node-version: 20 + - name: Install Rust toolchain uses: dtolnay/rust-toolchain@stable with: @@ -35,38 +52,57 @@ jobs: - name: Install Tauri CLI run: cargo install tauri-cli --version "^2" + - name: Install Frontend Dependencies + working-directory: llmfit-desktop/frontend + run: npm ci + - name: Build Tauri app bundle - run: cargo tauri build --target ${{ matrix.target }} --bundles app + run: cargo tauri build --target ${{ matrix.target }} working-directory: llmfit-desktop - - name: Package .app bundle + - name: Package artifacts shell: bash run: | - TAG="${GITHUB_REF_NAME}" - # Search both possible target locations - for BASE in "target" "llmfit-desktop/target"; do - APP=$(find "${BASE}/${{ matrix.target }}/release/bundle" -name '*.app' -maxdepth 3 2>/dev/null | head -1) - [ -n "$APP" ] && break - done - - if [ -z "$APP" ]; then - echo "::error::No .app bundle found" - find target/ llmfit-desktop/target/ -type d -name 'bundle' 2>/dev/null || true - exit 1 + set +e + DEST_DIR="${GITHUB_WORKSPACE}/release-assets" + mkdir -p "$DEST_DIR" + + # Cargo workspace puts target at workspace root, not under llmfit-desktop/ + SEARCH_ROOT="${GITHUB_WORKSPACE}/target/${{ matrix.target }}/release" + + # Fallback: also check under llmfit-desktop if workspace layout differs + if [ ! -d "$SEARCH_ROOT" ]; then + SEARCH_ROOT="${GITHUB_WORKSPACE}/llmfit-desktop/target/${{ matrix.target }}/release" fi - - echo "Found app bundle: $APP" - DEST="llmfit-desktop-${TAG}-${{ matrix.target }}.app.tar.gz" - cd "$(dirname "$APP")" - tar czf "/tmp/${DEST}" "$(basename "$APP")" - echo "DESKTOP_ASSET=${DEST}" >> "$GITHUB_ENV" - echo "DESKTOP_ASSET_PATH=/tmp/${DEST}" >> "$GITHUB_ENV" + + echo "=== Searching for artifacts under: $SEARCH_ROOT ===" + find "$SEARCH_ROOT" -type f \( -name "*.dmg" -o -name "*.app" -o -name "*.msi" -o -name "*.exe" -o -name "*.deb" -o -name "*.AppImage" \) 2>/dev/null || echo "No artifacts found" + + if [[ "${{ matrix.os }}" == "macos-latest" ]]; then + # Package .app as tarball + APP=$(find "$SEARCH_ROOT" -name '*.app' -type d 2>/dev/null | head -1) + if [ -n "$APP" ]; then + cd "$(dirname "$APP")" + tar czf "${DEST_DIR}/llmfit-desktop-${GITHUB_REF_NAME}-${{ matrix.target }}.app.tar.gz" "$(basename "$APP")" + cd "${GITHUB_WORKSPACE}" + fi + find "$SEARCH_ROOT" -type f -name "*.dmg" -exec cp {} "$DEST_DIR/" \; 2>/dev/null || true + elif [[ "${{ matrix.os }}" == "windows-latest" ]]; then + find "$SEARCH_ROOT" -type f -name "*.msi" -exec cp {} "$DEST_DIR/" \; 2>/dev/null || true + find "$SEARCH_ROOT" -type f -name "*.exe" -not -name "llmfit-desktop.exe" -exec cp {} "$DEST_DIR/" \; 2>/dev/null || true + else + find "$SEARCH_ROOT" -type f -name "*.deb" -exec cp {} "$DEST_DIR/" \; 2>/dev/null || true + find "$SEARCH_ROOT" -type f -name "*.AppImage" -exec cp {} "$DEST_DIR/" \; 2>/dev/null || true + fi + + echo "=== Packaged artifacts ===" + ls -la "$DEST_DIR/" 2>/dev/null || echo "No artifacts packaged" - name: Upload artifact uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7.0.0 with: - name: ${{ env.DESKTOP_ASSET }} - path: ${{ env.DESKTOP_ASSET_PATH }} + name: desktop-assets-${{ matrix.target }} + path: release-assets/* release: needs: build-desktop @@ -74,12 +110,14 @@ jobs: steps: - name: Download all artifacts - uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1 + uses: actions/download-artifact@70fc10c6e5e1ce46ad2ea6f2b72d43f7d47b13c3 # v8.0.0 with: path: artifacts + pattern: desktop-assets-* + merge-multiple: true - name: Create GitHub Release uses: softprops/action-gh-release@a06a81a03ee405af7f2048a818ed3f03bbf83c7b # v2.5.0 with: generate_release_notes: true - files: artifacts/**/*.tar.gz + files: artifacts/* diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3de63aa74..a7fb01155 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -5,6 +5,7 @@ on: tags: - "v*" - "!v*-mac" + - "!v*-desktop" permissions: contents: write @@ -137,7 +138,7 @@ jobs: steps: - name: Download all artifacts - uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1 + uses: actions/download-artifact@70fc10c6e5e1ce46ad2ea6f2b72d43f7d47b13c3 # v8.0.0 with: path: artifacts diff --git a/.gitignore b/.gitignore index 30db53d06..2e4f0ffb4 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,17 @@ -.DS_Store /target llmfit /docs llmfit-desktop/gen/schemas/* data/gguf_sources_cache.json -llmfit-web/node_modules/ -llmfit-web/dist/ +**/node_modules/ +**/dist/ +**/.svelte-kit/ +.worktrees/ +nul +.agents/ +AGENTS.md +IMPLEMENTATION_PLAN.md +skills/ +skills-lock.json +tmp.gif +dev_out.txt diff --git a/AGENTS.md b/AGENTS.md deleted file mode 100644 index 020ac6d8c..000000000 --- a/AGENTS.md +++ /dev/null @@ -1,170 +0,0 @@ -# AGENTS.md - -Instructions for AI agents contributing to this codebase. - ---- - -## Project overview - -`llmfit` is a Rust CLI/TUI tool that matches LLM models against local system hardware (RAM, CPU, GPU). It detects system specs, loads a model database from embedded JSON, scores each model's fit, and presents results in an interactive terminal UI or classic table output. - -## Language and toolchain - -- Rust, edition 2024. -- Build with `cargo build`. Run with `cargo run`. -- No nightly features required. Stable toolchain only. -- Minimum supported Rust version: whatever edition 2024 requires (1.85+). - -## Architecture - -``` -main.rs Entrypoint. Parses CLI args via clap. Launches TUI by default, - falls back to CLI subcommands (system, list, fit, search, info) - or --cli flag for classic table output. - -hardware.rs SystemSpecs::detect() reads RAM/CPU via sysinfo crate. - detect_gpu() shells out to nvidia-smi / rocm-smi, and - detects Apple Silicon via system_profiler. - On unified memory (Apple Silicon), VRAM = system RAM. - No async. No unsafe. - -models.rs LlmModel struct. ModelDatabase loads from data/hf_models.json - embedded via include_str!() at compile time. No runtime file I/O. - -fit.rs FitLevel enum (Perfect, Good, Marginal, TooTight). - RunMode enum (Gpu, CpuOffload, CpuOnly). - ModelFit::analyze() compares a model against SystemSpecs, - selecting the best available execution path (GPU > CPU offload > CPU). - rank_models_by_fit() sorts by fit level, then run mode, then utilization. - -display.rs CLI-mode table rendering using the tabled crate. - Only used when --cli flag or subcommands are invoked. - -tui_app.rs TUI application state. Holds all models, filters (search text, - provider toggles, fit filter), selection index. - All filtering logic is here -- apply_filters() recomputes - filtered_fits indices whenever inputs change. - -tui_ui.rs Rendering with ratatui. Four layout regions: system bar, - search/filter bar, model table (or detail pane), status bar. - Stateless rendering -- reads from App, writes to Frame. - -tui_events.rs Keyboard event handling with crossterm. Two modes: Normal - (navigation, filter toggling, quit) and Search (text input). -``` - -## Data flow - -1. `App::new()` calls `SystemSpecs::detect()` and `ModelDatabase::new()`. -2. Every model is analyzed into a `ModelFit` via `ModelFit::analyze()`. -3. Results are sorted by `rank_models_by_fit()`. -4. `apply_filters()` produces `filtered_fits: Vec` (indices into `all_fits`). -5. The TUI render loop reads `App` state and draws via `tui_ui::draw()`. -6. `tui_events::handle_events()` mutates `App` state, triggering re-render. - -## Model database - -- Source: `data/hf_models.json` (33 models). -- Generated by `scripts/scrape_hf_models.py` (Python, stdlib only, no pip deps). -- Embedded at compile time via `include_str!("../data/hf_models.json")`. -- Schema per entry: name, provider, parameter_count, min_ram_gb, recommended_ram_gb, min_vram_gb, quantization, context_length, use_case. -- `min_vram_gb` is VRAM needed for GPU inference. `min_ram_gb` is system RAM needed for CPU inference. Both are derived from the same parameter count. -- RAM formula: `params * 0.5 bytes (Q4_K_M) / 1024^3 * 1.2 overhead`. -- VRAM formula: `params * 0.5 bytes (Q4_K_M) / 1024^3 * 1.1 activation overhead`. -- Recommended RAM: `model_size * 2.0`. - -Do not manually edit `hf_models.json`. Regenerate it by running the scraper: - -```sh -python3 scripts/scrape_hf_models.py -``` - -The scraper has hardcoded fallback entries for gated models that require authentication. - -## Conventions - -- No `unsafe` code. -- No `.unwrap()` on user-facing paths. Use proper error handling or `expect()` with a descriptive message for internal invariants only. -- Fit levels are ordered: Perfect > Good > Marginal > TooTight. Do not add levels without updating `rank_models_by_fit()` sort logic. -- Fit is VRAM-first. GPU inference with sufficient VRAM is the ideal path. CPU inference via system RAM is a fallback. The `RunMode` enum tracks which memory pool is being used (Gpu, CpuOffload, CpuOnly). -- `min_vram_gb` is the VRAM needed to load model weights on GPU. `min_ram_gb` is the system RAM needed for CPU-only inference (same weights, loaded into RAM instead). They represent the same workload on different hardware paths. -- On Apple Silicon (unified memory), VRAM = system RAM. The `CpuOffload` path is skipped because there is no separate RAM pool to spill to. `SystemSpecs::unified_memory` tracks this. -- TUI rendering is stateless. `tui_ui::draw()` must not mutate `App`. Pass `&mut App` only for `TableState` widget requirements -- do not use it to change application state. -- Event handling in `tui_events.rs` is the sole place that mutates `App` in the TUI loop. -- Keep `display.rs` and `tui_*.rs` independent. The CLI path must work without initializing any TUI state. - -## Adding a new model to the database - -1. Add the model's HuggingFace repo ID to `TARGET_MODELS` in `scripts/scrape_hf_models.py`. -2. If the model is gated (requires HF auth), add a fallback entry to the `FALLBACK` dict in the same script. -3. Run `python3 scripts/scrape_hf_models.py`. -4. Verify the output in `data/hf_models.json`. -5. Run `cargo build` to verify compilation. - -## Adding a new filter - -1. Add the filter state to `App` in `tui_app.rs`. -2. Add filtering logic inside `apply_filters()`. -3. Add the keybinding in `tui_events.rs` (Normal mode handler). -4. Add the UI widget in `tui_ui.rs` (`draw_search_and_filters()` function). -5. Update the status bar help text in `draw_status_bar()`. - -## Adding a new CLI subcommand - -1. Add a variant to the `Commands` enum in `main.rs`. -2. Add the match arm in the `main()` function's command dispatch. -3. Use `display.rs` functions for output, or add new ones as needed. - -## Testing - -There are no tests yet. When adding tests: - -- Unit tests for `fit.rs` logic (given known SystemSpecs and LlmModel values, assert correct FitLevel). -- Unit tests for `models.rs` (verify JSON parsing, search matching). -- Integration tests for CLI subcommands via `assert_cmd` crate. -- TUI is difficult to unit test. Keep rendering stateless and test the state mutations in `tui_app.rs` directly. - -## Dependencies policy - -- Prefer crates that are well-maintained and have minimal transitive dependencies. -- `sysinfo` is the system detection crate. Do not replace it with raw platform calls. -- `ratatui` + `crossterm` is the TUI stack. Do not mix in `termion` or `ncurses`. -- `clap` with derive feature for CLI parsing. Do not use manual arg parsing. -- The Python scraper uses only stdlib (`urllib`, `json`). Do not add pip dependencies. - -## Common tasks - -```sh -# Build -cargo build - -# Run TUI -cargo run - -# Run CLI mode -cargo run -- --cli - -# Run specific subcommand -cargo run -- system -cargo run -- fit --perfect -n 5 -cargo run -- search "llama" - -# Refresh model database -python3 scripts/scrape_hf_models.py && cargo build - -# Check for compilation issues -cargo check - -# Format code -cargo fmt - -# Lint -cargo clippy -``` - -## Platform notes - -- GPU detection shells out to `nvidia-smi` (NVIDIA) and `rocm-smi` (AMD). These are best-effort and fail silently if unavailable. -- Apple Silicon detection uses `system_profiler SPDisplaysDataType`. On unified memory Macs, VRAM is reported as available system RAM (same pool). -- `sysinfo` handles cross-platform RAM/CPU. No conditional compilation needed. -- The TUI uses crossterm which works on Linux, macOS, and Windows terminals. diff --git a/Cargo.lock b/Cargo.lock index 1b4b6f0a6..1b39436cb 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -49,9 +49,9 @@ dependencies = [ [[package]] name = "anstream" -version = "1.0.0" +version = "0.6.21" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "824a212faf96e9acacdbd09febd34438f8f711fb84e09a8916013cd7815ca28d" +checksum = "43d5b281e737544384e969a5ccad3f1cdd24b48086a0fc1b2a5262a26b8f4f4a" dependencies = [ "anstyle", "anstyle-parse", @@ -70,9 +70,9 @@ checksum = "5192cca8006f1fd4f7237516f40fa183bb07f8fbdfedaa0036de5ea9b0b45e78" [[package]] name = "anstyle-parse" -version = "1.0.0" +version = "0.2.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "52ce7f38b242319f7cabaa6813055467063ecdc9d355bbb4ce0c68908cd8130e" +checksum = "4e7644824f0aa2c7b9384579234ef10eb7efb6a0deb83f9630a49594dd9c15c2" dependencies = [ "utf8parse", ] @@ -103,26 +103,6 @@ version = "1.0.102" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7f202df86484c868dbad7eaa557ef785d5c66295e41b460ef922eca0723b842c" -[[package]] -name = "arboard" -version = "3.6.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0348a1c054491f4bfe6ab86a7b6ab1e44e45d899005de92f58b3df180b36ddaf" -dependencies = [ - "clipboard-win", - "image", - "log", - "objc2 0.6.3", - "objc2-app-kit", - "objc2-core-foundation", - "objc2-core-graphics", - "objc2-foundation 0.3.1", - "parking_lot", - "percent-encoding", - "windows-sys 0.60.2", - "x11rb", -] - [[package]] name = "atk" version = "0.18.2" @@ -333,12 +313,6 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" -[[package]] -name = "byteorder-lite" -version = "0.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8f1fe948ff07f4bd06c30984e69f5b4899c516a3ef74f34df92a2df2ab535495" - [[package]] name = "bytes" version = "1.11.1" @@ -487,9 +461,9 @@ dependencies = [ [[package]] name = "clap" -version = "4.5.61" +version = "4.5.60" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "52fa72306bb30daf11bc97773431628e5b4916e97aaa74b7d3f625d4d495da02" +checksum = "2797f34da339ce31042b27d23607e051786132987f595b02ba4f6a6dffb7030a" dependencies = [ "clap_builder", "clap_derive", @@ -497,9 +471,9 @@ dependencies = [ [[package]] name = "clap_builder" -version = "4.5.61" +version = "4.5.60" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2071365c5c56eae7d77414029dde2f4f4ba151cf68d5a3261c9a40de428ace93" +checksum = "24a241312cea5059b13574bb9b3861cabf758b879c15190b37b6d6fd63ab6876" dependencies = [ "anstream", "anstyle", @@ -509,9 +483,9 @@ dependencies = [ [[package]] name = "clap_derive" -version = "4.5.61" +version = "4.5.55" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dec5be1eea072311774b7b84ded287adbd9f293f9d23456817605c6042f4f5e0" +checksum = "a92793da1a46a5f2a02a6f4c46c6496b28c43638adea8306fcb0caa1634f24e5" dependencies = [ "heck 0.5.0", "proc-macro2", @@ -525,15 +499,6 @@ version = "1.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "3a822ea5bc7590f9d40f1ba12c0dc3c2760f3482c6984db1573ad11031420831" -[[package]] -name = "clipboard-win" -version = "5.4.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bde03770d3df201d4fb868f2c9c59e66a3e4e2bd06692a0fe701e7103c7e84d4" -dependencies = [ - "error-code", -] - [[package]] name = "colorchoice" version = "1.0.4" @@ -717,12 +682,6 @@ dependencies = [ "winapi", ] -[[package]] -name = "crunchy" -version = "0.2.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "460fbee9c2c2f33933d720630a6a0bac33ba7053db5344fac858d4b8952d77d5" - [[package]] name = "crypto-common" version = "0.1.7" @@ -1079,12 +1038,6 @@ dependencies = [ "windows-sys 0.61.2", ] -[[package]] -name = "error-code" -version = "3.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dea2df4cf52843e0452895c455a1a2cfbb842a1e7329671acf418fdc53ed4c59" - [[package]] name = "euclid" version = "0.22.13" @@ -1104,26 +1057,6 @@ dependencies = [ "regex", ] -[[package]] -name = "fax" -version = "0.2.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f05de7d48f37cd6730705cbca900770cab77a89f413d23e100ad7fad7795a0ab" -dependencies = [ - "fax_derive", -] - -[[package]] -name = "fax_derive" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a0aca10fb742cb43f9e7bb8467c91aa9bcb8e3ffbc6a6f7389bb93ffc920577d" -dependencies = [ - "proc-macro2", - "quote", - "syn 2.0.117", -] - [[package]] name = "fdeflate" version = "0.3.7" @@ -1435,16 +1368,6 @@ dependencies = [ "version_check", ] -[[package]] -name = "gethostname" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1bd49230192a3797a9a4d6abe9b3eed6f7fa4c8a8a4947977c6f80025f92cbd8" -dependencies = [ - "rustix", - "windows-link 0.2.1", -] - [[package]] name = "getrandom" version = "0.1.16" @@ -1640,17 +1563,6 @@ dependencies = [ "syn 2.0.117", ] -[[package]] -name = "half" -version = "2.7.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b" -dependencies = [ - "cfg-if", - "crunchy", - "zerocopy", -] - [[package]] name = "hashbrown" version = "0.12.3" @@ -1828,7 +1740,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "3e795dff5605e0f04bff85ca41b51a96b83e80b281e96231bcaaf1ac35103371" dependencies = [ "byteorder", - "png 0.17.16", + "png", ] [[package]] @@ -1945,20 +1857,6 @@ dependencies = [ "icu_properties", ] -[[package]] -name = "image" -version = "0.25.10" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "85ab80394333c02fe689eaf900ab500fbd0c2213da414687ebf995a65d5a6104" -dependencies = [ - "bytemuck", - "byteorder-lite", - "moxcms", - "num-traits", - "png 0.18.1", - "tiff", -] - [[package]] name = "indexmap" version = "1.9.3" @@ -2258,9 +2156,8 @@ checksum = "11d3d7f243d5c5a8b9bb5d6dd2b1602c0cb0b9db1621bafc7ed66e35ff9fe092" [[package]] name = "llmfit" -version = "0.8.4" +version = "0.8.0" dependencies = [ - "arboard", "axum", "clap", "colored", @@ -2277,18 +2174,17 @@ dependencies = [ [[package]] name = "llmfit-core" -version = "0.8.4" +version = "0.8.0" dependencies = [ "serde", "serde_json", "sysinfo", "ureq", - "which", ] [[package]] name = "llmfit-desktop" -version = "0.8.4" +version = "0.8.0" dependencies = [ "llmfit-core", "serde", @@ -2429,16 +2325,6 @@ dependencies = [ "windows-sys 0.61.2", ] -[[package]] -name = "moxcms" -version = "0.8.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bb85c154ba489f01b25c0d36ae69a87e4a1c73a72631fc6c0eb6dde34a73e44b" -dependencies = [ - "num-traits", - "pxfm", -] - [[package]] name = "muda" version = "0.17.1" @@ -2454,7 +2340,7 @@ dependencies = [ "objc2-core-foundation", "objc2-foundation 0.3.1", "once_cell", - "png 0.17.16", + "png", "serde", "thiserror 2.0.18", "windows-sys 0.60.2", @@ -2627,15 +2513,14 @@ dependencies = [ "block2 0.6.2", "objc2 0.6.3", "objc2-core-foundation", - "objc2-core-graphics", "objc2-foundation 0.3.1", ] [[package]] name = "objc2-core-foundation" -version = "0.3.2" +version = "0.3.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2a180dd8642fa45cdb7dd721cd4c11b1cadd4929ce112ebd8b9f5803cc79d536" +checksum = "1c10c2894a6fed806ade6027bcd50662746363a9589d3ec9d9bef30a4e4bc166" dependencies = [ "bitflags 2.11.0", "dispatch2", @@ -2649,10 +2534,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "989c6c68c13021b5c2d6b71456ebb0f9dc78d752e86a98da7c716f4f9470f5a4" dependencies = [ "bitflags 2.11.0", - "dispatch2", - "objc2 0.6.3", "objc2-core-foundation", - "objc2-io-surface", ] [[package]] @@ -2696,22 +2578,11 @@ dependencies = [ [[package]] name = "objc2-io-kit" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "33fafba39597d6dc1fb709123dfa8289d39406734be322956a69f0931c73bb15" -dependencies = [ - "libc", - "objc2-core-foundation", -] - -[[package]] -name = "objc2-io-surface" version = "0.3.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7282e9ac92529fa3457ce90ebb15f4ecbc383e8338060960760fa2cf75420c3c" +checksum = "71c1c64d6120e51cd86033f67176b1cb66780c2efe34dec55176f77befd93c0a" dependencies = [ - "bitflags 2.11.0", - "objc2 0.6.3", + "libc", "objc2-core-foundation", ] @@ -3079,19 +2950,6 @@ dependencies = [ "miniz_oxide", ] -[[package]] -name = "png" -version = "0.18.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "60769b8b31b2a9f263dae2776c37b1b28ae246943cf719eb6946a1db05128a61" -dependencies = [ - "bitflags 2.11.0", - "crc32fast", - "fdeflate", - "flate2", - "miniz_oxide", -] - [[package]] name = "portable-atomic" version = "1.13.1" @@ -3228,18 +3086,6 @@ dependencies = [ "unicode-ident", ] -[[package]] -name = "pxfm" -version = "0.1.28" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b5a041e753da8b807c9255f28de81879c78c876392ff2469cde94799b2896b9d" - -[[package]] -name = "quick-error" -version = "2.0.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a993555f31e5a609f617c12db6250dedcac1b0a85076912c436e6fc9b2c8e6a3" - [[package]] name = "quick-xml" version = "0.38.4" @@ -3251,9 +3097,9 @@ dependencies = [ [[package]] name = "quote" -version = "1.0.45" +version = "1.0.44" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "41f2619966050689382d2b44f664f4bc593e129785a36d6ee376ddf37259b924" +checksum = "21b2ebcf727b7760c461f091f9f0f539b77b8e87f2fd88131e7f1b433b3cece4" dependencies = [ "proc-macro2", ] @@ -4158,9 +4004,9 @@ dependencies = [ [[package]] name = "sysinfo" -version = "0.38.4" +version = "0.38.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "92ab6a2f8bfe508deb3c6406578252e491d299cbbf3bc0529ecc3313aee4a52f" +checksum = "d03c61d2a49c649a15c407338afe7accafde9dac869995dccb73e5f7ef7d9034" dependencies = [ "libc", "memchr", @@ -4348,7 +4194,7 @@ dependencies = [ "ico", "json-patch", "plist", - "png 0.17.16", + "png", "proc-macro2", "quote", "semver", @@ -4601,20 +4447,6 @@ dependencies = [ "syn 2.0.117", ] -[[package]] -name = "tiff" -version = "0.11.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b63feaf3343d35b6ca4d50483f94843803b0f51634937cc2ec519fc32232bc52" -dependencies = [ - "fax", - "flate2", - "half", - "quick-error", - "weezl", - "zune-jpeg", -] - [[package]] name = "time" version = "0.3.47" @@ -4876,7 +4708,7 @@ dependencies = [ "objc2-core-graphics", "objc2-foundation 0.3.1", "once_cell", - "png 0.17.16", + "png", "serde", "thiserror 2.0.18", "windows-sys 0.60.2", @@ -5371,12 +5203,6 @@ dependencies = [ "windows-core 0.61.2", ] -[[package]] -name = "weezl" -version = "0.1.12" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a28ac98ddc8b9274cb41bb4d9d4d5c425b6020c50c46f25559911905610b4a88" - [[package]] name = "wezterm-bidi" version = "0.2.3" @@ -5449,15 +5275,6 @@ dependencies = [ "wezterm-dynamic", ] -[[package]] -name = "which" -version = "8.0.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "81995fafaaaf6ae47a7d0cc83c67caf92aeb7e5331650ae6ff856f7c0c60c459" -dependencies = [ - "libc", -] - [[package]] name = "winapi" version = "0.3.9" @@ -6131,23 +5948,6 @@ dependencies = [ "pkg-config", ] -[[package]] -name = "x11rb" -version = "0.13.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9993aa5be5a26815fe2c3eacfc1fde061fc1a1f094bf1ad2a18bf9c495dd7414" -dependencies = [ - "gethostname", - "rustix", - "x11rb-protocol", -] - -[[package]] -name = "x11rb-protocol" -version = "0.13.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ea6fc2961e4ef194dcbfe56bb845534d0dc8098940c7e5c012a258bfec6701bd" - [[package]] name = "yoke" version = "0.8.1" @@ -6256,18 +6056,3 @@ name = "zmij" version = "1.0.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" - -[[package]] -name = "zune-core" -version = "0.5.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cb8a0807f7c01457d0379ba880ba6322660448ddebc890ce29bb64da71fb40f9" - -[[package]] -name = "zune-jpeg" -version = "0.5.14" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0b7a1c0af6e5d8d1363f4994b7a091ccf963d8b694f7da5b0b9cceb82da2c0a6" -dependencies = [ - "zune-core", -] diff --git a/Cargo.toml b/Cargo.toml index 92282986d..a9b503533 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -1,7 +1,7 @@ [workspace] members = ["llmfit-core", "llmfit-tui", "llmfit-desktop"] -default-members = ["llmfit-core", "llmfit-tui"] +default-members = ["llmfit-core", "llmfit-tui", "llmfit-desktop"] resolver = "3" [workspace.package] -version = "0.8.4" +version = "0.8.0" diff --git a/README.md b/README.md index 9d56cfc27..ef068ef75 100644 --- a/README.md +++ b/README.md @@ -1,749 +1,91 @@ -# llmfit +# πŸš€ llmfit (Desktop Edition)

llmfit icon

- English Β· - δΈ­ζ–‡ + A cross-platform, industrial-grade Desktop Application to right-size LLM models against your hardware.

- CI - Crates.io - License + + Release + + Platforms

-**Hundreds of models & providers. One command to find what runs on your hardware.** - -A terminal tool that right-sizes LLM models to your system's RAM, CPU, and GPU. Detects your hardware, scores each model across quality, speed, fit, and context dimensions, and tells you which ones will actually run well on your machine. - -Ships with an interactive TUI (default) and a classic CLI mode. Supports multi-GPU setups, MoE architectures, dynamic quantization selection, speed estimation, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner, LM Studio). - -> **Sister projects:** -> - [sympozium](https://github.com/sympozium-ai/sympozium/) β€” managing agents in Kubernetes. -> - [llmserve](https://github.com/AlexsJones/llmserve) β€” a simple TUI for serving local LLM models. Pick a model, pick a backend, serve it. - -![demo](demo.gif) +> 🎨 **Desktop UI/UX Remastered & Engineered by [@Kcguner](https://github.com/Kcguner)** +> *(Powered by the original CLI/TUI core from [AlexsJones](https://github.com/AlexsJones/llmfit))* --- -## Install - -### Windows -```sh -scoop install llmfit -``` - -If Scoop is not installed, follow the [Scoop installation guide](https://scoop.sh/). - -### macOS / Linux - -#### Homebrew -```sh -brew install llmfit -``` +## 🌟 What's New in this Version? -#### Quick install -```sh -curl -fsSL https://llmfit.axjns.dev/install.sh | sh -``` +This fork introduces a **fully native, standalone Desktop GUI** built with Tauri, React, and TailwindCSS. We completely overhauled the previous CLI-centric experience into a beautifully designed, "Industrial & Precise" interface suitable for professional hardware profiling. -Downloads the latest release binary from GitHub and installs it to `/usr/local/bin` (or `~/.local/bin` if no sudo). +- **Zero-Config Desktop Native:** No more terminal commands required. Just double-click the installer and launch your GUI. +- **Industrial Dark Palette:** A sleek Zinc/Amber structural theme optimized for dense data tables, sharp geometry, and high-readability fonts (`Chivo` and `JetBrains Mono`). +- **Cross-Platform OS Installers:** Automated CI/CD pipelines generate `.exe`, `.msi`, `.dmg`, `.deb` installers dynamically via GitHub Actions on every release. +- **High-Performance React Engine:** Fully decoupled data streams with fast debounced searching, sticky floating headers, and dynamic MoE offload metrics visualization without frame drops. -**Install to `~/.local/bin` without sudo:** -```sh -curl -fsSL https://llmfit.axjns.dev/install.sh | sh -s -- --local -``` +### πŸ“Έ Interface Previews -### Docker / Podman -```sh -docker run ghcr.io/alexsjones/llmfit -``` -This prints JSON from `llmfit recommend` command. The JSON could be further queried with `jq`. -``` -podman run ghcr.io/alexsjones/llmfit recommend --use-case coding | jq '.models[].name' -``` +**Dark Mode (Industrial Engine)** +Dark Mode -### From source -```sh -git clone https://github.com/AlexsJones/llmfit.git -cd llmfit -cargo build --release -# binary is at target/release/llmfit -``` +**Light Mode (Clean & Precise)** +Light Mode --- -## Usage - -### TUI (default) - -```sh -llmfit -``` - -Launches the interactive terminal UI. Your system specs (CPU, RAM, GPU name, VRAM, backend) are shown at the top. Models are listed in a scrollable table sorted by composite score. Each row shows the model's score, estimated tok/s, best quantization for your hardware, run mode, memory usage, and use-case category. - -| Key | Action | -|----------------------------|-----------------------------------------------------------------------| -| `Up` / `Down` or `j` / `k` | Navigate models | -| `/` | Enter search mode (partial match on name, provider, params, use case) | -| `Esc` or `Enter` | Exit search mode | -| `Ctrl-U` | Clear search | -| `f` | Cycle fit filter: All, Runnable, Perfect, Good, Marginal | -| `a` | Cycle availability filter: All, GGUF Avail, Installed | -| `s` | Cycle sort column: Score, Params, Mem%, Ctx, Date, Use Case | -| `v` | Enter Visual mode (select multiple models) | -| `V` | Enter Select mode (column-based filtering) | -| `t` | Cycle color theme (saved automatically) | -| `p` | Open Plan mode for selected model (hardware planning) | -| `P` | Open provider filter popup | -| `U` | Open use-case filter popup | -| `C` | Open capability filter popup | -| `m` | Mark selected model for compare | -| `c` | Open compare view (marked vs selected) | -| `x` | Clear compare mark | -| `i` | Toggle installed-first sorting (any detected runtime provider) | -| `d` | Download selected model (provider picker when multiple are available) | -| `r` | Refresh installed models from runtime providers | -| `Enter` | Toggle detail view for selected model | -| `PgUp` / `PgDn` | Scroll by 10 | -| `g` / `G` | Jump to top / bottom | -| `q` | Quit | - -### Vim-like modes - -The TUI uses Vim-inspired modes shown in the bottom-left status bar. The current mode determines which keys are active. - -#### Normal mode - -The default mode. Navigate, search, filter, and open views. All keys in the table above apply here. - -#### Visual mode (`v`) - -Select a contiguous range of models for bulk comparison. Press `v` to anchor at the current row, then navigate with `j`/`k` or arrow keys to extend the selection. Selected rows are highlighted. - -| Key | Action | -|---------------------|--------------------------------------------------------| -| `j` / `k` or arrows | Extend selection up/down | -| `c` | Compare all selected models (opens multi-compare view) | -| `m` | Mark current model for two-model compare | -| `Esc` or `v` | Exit Visual mode | - -The multi-compare view displays a table where rows are attributes (Score, tok/s, Fit, Mem%, Params, Mode, Context, Quant, etc.) and columns are models. Best values are highlighted. Use `h`/`l` or arrow keys to scroll horizontally if more models are selected than fit on screen. - -#### Select mode (`V`) - -Column-based filtering. Press `V` (shift-v) to enter Select mode, then use `h`/`l` or arrow keys to move between column headers. The active column is visually highlighted. Press `Enter` or `Space` to activate the appropriate filter for that column: - -| Column | Filter action | -|-------------------------------|---------------------------------------------------------------------------| -| Inst | Cycle availability filter | -| Model | Enter search mode | -| Provider | Open provider popup | -| Params | Open parameter-size bucket popup (<3B, 3-7B, 7-14B, 14-30B, 30-70B, 70B+) | -| Score, tok/s, Mem%, Ctx, Date | Sort by that column | -| Quant | Open quantization popup | -| Mode | Open run-mode popup (GPU, MoE, CPU+GPU, CPU) | -| Fit | Cycle fit filter | -| Use Case | Open use-case popup | - -Row navigation (`j`/`k`) still works in Select mode so you can see the effect of filters as you apply them. Press `Esc` to return to Normal mode. - -### TUI Plan mode (`p`) - -Plan mode inverts normal fit analysis: instead of asking "what fits my hardware?", it estimates "what hardware is needed for this model config?". - -Use `p` on a selected row, then: - -| Key | Action | -|------------------------|-----------------------------------------------------------| -| `Tab` / `j` / `k` | Move between editable fields (Context, Quant, Target TPS) | -| `Left` / `Right` | Move cursor in current field | -| Type | Edit current field | -| `Backspace` / `Delete` | Remove characters | -| `Ctrl-U` | Clear current field | -| `Esc` or `q` | Exit Plan mode | - -Plan mode shows estimates for: -- minimum and recommended VRAM/RAM/CPU cores -- feasible run paths (GPU, CPU offload, CPU-only) -- upgrade deltas to reach better fit targets - -### Themes - -Press `t` to cycle through 10 built-in color themes. Your selection is saved automatically to `~/.config/llmfit/theme` and restored on next launch. - -| Theme | Description | -|--------------------------|---------------------------------------------------| -| **Default** | Original llmfit colors | -| **Dracula** | Dark purple background with pastel accents | -| **Solarized** | Ethan Schoonover's Solarized Dark palette | -| **Nord** | Arctic, cool blue-gray tones | -| **Monokai** | Monokai Pro warm syntax colors | -| **Gruvbox** | Retro groove palette with warm earth tones | -| **Catppuccin Latte** | 🌻 Light theme β€” harmonious pastel inversion | -| **Catppuccin FrappΓ©** | πŸͺ΄ Low-contrast dark β€” muted, subdued aesthetic | -| **Catppuccin Macchiato** | 🌺 Medium-contrast dark β€” gentle, soothing tones | -| **Catppuccin Mocha** | 🌿 Darkest variant β€” cozy with color-rich accents | - -### Web dashboard - -When you run `llmfit` in non-JSON mode, it automatically starts a background web dashboard on `0.0.0.0:8787`. Open it in any browser on the same network: - -``` -http://:8787 -``` - -Override the host or port with environment variables: - -```sh -LLMFIT_DASHBOARD_HOST=0.0.0.0 LLMFIT_DASHBOARD_PORT=9000 llmfit -``` - -| Variable | Default | Description | -|---|---|---| -| `LLMFIT_DASHBOARD_HOST` | `0.0.0.0` | Interface to bind the dashboard server | -| `LLMFIT_DASHBOARD_PORT` | `8787` | Port to bind the dashboard server | - -To disable the auto-started dashboard, pass `--no-dashboard`: - -```sh -llmfit --no-dashboard -``` - -### CLI mode - -Use `--cli` or any subcommand to get classic table output: - -```sh -# Table of all models ranked by fit -llmfit --cli - -# Only perfectly fitting models, top 5 -llmfit fit --perfect -n 5 - -# Show detected system specs -llmfit system - -# List all models in the database -llmfit list - -# Search by name, provider, or size -llmfit search "llama 8b" - -# Detailed view of a single model -llmfit info "Mistral-7B" - -# Top 5 recommendations (JSON, for agent/script consumption) -llmfit recommend --json --limit 5 - -# Recommendations filtered by use case -llmfit recommend --json --use-case coding --limit 3 - -# Force a specific runtime (bypass automatic MLX selection on Apple Silicon) -llmfit recommend --force-runtime llamacpp -llmfit recommend --force-runtime llamacpp --use-case coding --limit 3 - -# Plan required hardware for a specific model configuration -llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192 -llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192 --quant mlx-4bit -llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192 --target-tps 25 --json - -# Run as a node-level REST API (for cluster schedulers / aggregators) -llmfit serve --host 0.0.0.0 --port 8787 -``` +## πŸ–₯️ Install Desktop App (GUI) -### REST API (`llmfit serve`) +The easiest way to use **llmfit** is by downloading the standalone Application. -`llmfit serve` starts an HTTP API that exposes the same fit/scoring data used by TUI/CLI, including filtering and top-model selection for a node. - -```sh -# Liveness -curl http://localhost:8787/health - -# Node hardware info -curl http://localhost:8787/api/v1/system - -# Full fit list with filters -curl "http://localhost:8787/api/v1/models?min_fit=marginal&runtime=llamacpp&sort=score&limit=20" - -# Key scheduling endpoint: top runnable models for this node -curl "http://localhost:8787/api/v1/models/top?limit=5&min_fit=good&use_case=coding" - -# Search by model name/provider text -curl "http://localhost:8787/api/v1/models/Mistral?runtime=any" -``` - -Supported query params for `models`/`models/top`: - -- `limit` (or `n`): max number of rows returned -- `perfect`: `true|false` (forces perfect-only when `true`) -- `min_fit`: `perfect|good|marginal|too_tight` -- `runtime`: `any|mlx|llamacpp` -- `use_case`: `general|coding|reasoning|chat|multimodal|embedding` -- `provider`: provider text filter (substring) -- `search`: free-text filter across name/provider/size/use-case -- `sort`: `score|tps|params|mem|ctx|date|use_case` -- `include_too_tight`: include non-runnable rows (default `false` on `/top`, `true` on `/models`) -- `max_context`: per-request context cap for memory estimation -- `force_runtime`: `mlx|llamacpp|vllm` β€” override automatic runtime selection during analysis - -Validate API behavior locally: - -```sh -# spawn server automatically and run endpoint/schema/filter assertions -python3 scripts/test_api.py --spawn - -# or test an already-running server -python3 scripts/test_api.py --base-url http://127.0.0.1:8787 -``` - -### GPU memory override - -GPU VRAM autodetection can fail on some systems (e.g. broken `nvidia-smi`, VMs, passthrough setups). Use `--memory` to manually specify your GPU's VRAM: - -```sh -# Override with 32 GB VRAM -llmfit --memory=32G - -# Megabytes also work (32000 MB β‰ˆ 31.25 GB) -llmfit --memory=32000M - -# Works with all modes: TUI, CLI, and subcommands -llmfit --memory=24G --cli -llmfit --memory=24G fit --perfect -n 5 -llmfit --memory=24G system -llmfit --memory=24G info "Llama-3.1-70B" -llmfit --memory=24G recommend --json -``` - -Accepted suffixes: `G`/`GB`/`GiB` (gigabytes), `M`/`MB`/`MiB` (megabytes), `T`/`TB`/`TiB` (terabytes). Case-insensitive. If no GPU was detected, the override creates a synthetic GPU entry so models are scored for GPU inference. - -### Context-length cap for estimation - -Use `--max-context` to cap context length used for memory estimation (without changing each model's advertised maximum context): - -```sh -# Estimate memory fit at 4K context -llmfit --max-context 4096 --cli - -# Works with subcommands -llmfit --max-context 8192 fit --perfect -n 5 -llmfit --max-context 16384 recommend --json --limit 5 -``` - -If `--max-context` is not set, llmfit will use `OLLAMA_CONTEXT_LENGTH` when available. - -### JSON output - -Add `--json` to any subcommand for machine-readable output: - -```sh -llmfit --json system # Hardware specs as JSON -llmfit --json fit -n 10 # Top 10 fits as JSON -llmfit recommend --json # Top 5 recommendations (JSON is default for recommend) -llmfit plan "Qwen/Qwen2.5-Coder-0.5B-Instruct" --context 8192 --json -``` - -`plan` JSON includes stable fields for: -- request (`context`, `quantization`, `target_tps`) -- estimated minimum/recommended hardware -- per-path feasibility (`gpu`, `cpu_offload`, `cpu_only`) -- upgrade deltas +1. Go to our **[Releases](https://github.com/Kcguner/llmfit/releases)** page. +2. Download the installer for your operating system: + - **Windows:** Download `.msi` or `.exe` + - **macOS:** Download `.dmg` or `.app.tar.gz` + - **Linux:** Download `.deb` or `.AppImage` +3. Run the installer and you're good to go! --- -## How it works - -1. **Hardware detection** -- Reads total/available RAM via `sysinfo`, counts CPU cores, and probes for GPUs: - - **NVIDIA** -- Multi-GPU support via `nvidia-smi`. Aggregates VRAM across all detected GPUs. Falls back to VRAM estimation from GPU model name if reporting fails. - - **AMD** -- Detected via `rocm-smi`. - - **Intel Arc** -- Discrete VRAM via sysfs, integrated via `lspci`. - - **Apple Silicon** -- Unified memory via `system_profiler`. VRAM = system RAM. - - **Ascend** -- Detected via `npu-smi`. - - **Backend detection** -- Automatically identifies the acceleration backend (CUDA, Metal, ROCm, SYCL, CPU ARM, CPU x86, Ascend) for speed estimation. - -2. **Model database** -- Hundreds models sourced from the HuggingFace API, stored in `data/hf_models.json` and embedded at compile time. Memory requirements are computed from parameter counts across a quantization hierarchy (Q8_0 through Q2_K). VRAM is the primary constraint for GPU inference; system RAM is the fallback for CPU-only execution. - - **MoE support** -- Models with Mixture-of-Experts architectures (Mixtral, DeepSeek-V2/V3) are detected automatically. Only a subset of experts is active per token, so the effective VRAM requirement is much lower than total parameter count suggests. For example, Mixtral 8x7B has 46.7B total parameters but only activates ~12.9B per token, reducing VRAM from 23.9 GB to ~6.6 GB with expert offloading. - -3. **Dynamic quantization** -- Instead of assuming a fixed quantization, llmfit tries the best quality quantization that fits your hardware. It walks a hierarchy from Q8_0 (best quality) down to Q2_K (most compressed), picking the highest quality that fits in available memory. If nothing fits at full context, it tries again at half context. - -4. **Multi-dimensional scoring** -- Each model is scored across four dimensions (0–100 each): - - | Dimension | What it measures | - |-------------|--------------------------------------------------------------------------------| - | **Quality** | Parameter count, model family reputation, quantization penalty, task alignment | - | **Speed** | Estimated tokens/sec based on backend, params, and quantization | - | **Fit** | Memory utilization efficiency (sweet spot: 50–80% of available memory) | - | **Context** | Context window capability vs target for the use case | - - Dimensions are combined into a weighted composite score. Weights vary by use-case category (General, Coding, Reasoning, Chat, Multimodal, Embedding). For example, Chat weights Speed higher (0.35) while Reasoning weights Quality higher (0.55). Models are ranked by composite score, with unrunnable models (Too Tight) always at the bottom. - -5. **Speed estimation** -- Token generation in LLM inference is memory-bandwidth-bound: each token requires reading the full model weights once from VRAM. When the GPU model is recognized, llmfit uses its actual memory bandwidth to estimate throughput: - - Formula: `(bandwidth_GB_s / model_size_GB) Γ— efficiency_factor` - - The efficiency factor (0.55) accounts for kernel overhead, KV-cache reads, and memory controller effects. This approach is validated against published benchmarks from llama.cpp ([Apple Silicon](https://github.com/ggml-org/llama.cpp/discussions/4167), [NVIDIA T4](https://github.com/ggml-org/llama.cpp/discussions/4225)) and real-world measurements. - - The bandwidth lookup table covers ~80 GPUs across NVIDIA (consumer + datacenter), AMD (RDNA + CDNA), and Apple Silicon families. - - For unrecognized GPUs, llmfit falls back to per-backend speed constants: - - | Backend | Speed constant | - |--------------|----------------| - | CUDA | 220 | - | Metal | 160 | - | ROCm | 180 | - | SYCL | 100 | - | CPU (ARM) | 90 | - | CPU (x86) | 70 | - | NPU (Ascend) | 390 | - - Fallback formula: `K / params_b Γ— quant_speed_multiplier`, with penalties for CPU offload (0.5Γ—), CPU-only (0.3Γ—), and MoE expert switching (0.8Γ—). - -6. **Fit analysis** -- Each model is evaluated for memory compatibility: +## πŸ’» Command Line (CLI / TUI) - **Run modes:** - - **GPU** -- Model fits in VRAM. Fast inference. - - **MoE** -- Mixture-of-Experts with expert offloading. Active experts in VRAM, inactive in RAM. - - **CPU+GPU** -- VRAM insufficient, spills to system RAM with partial GPU offload. - - **CPU** -- No GPU. Model loaded entirely into system RAM. - - **Fit levels:** - - **Perfect** -- Recommended memory met on GPU. Requires GPU acceleration. - - **Good** -- Fits with headroom. Best achievable for MoE offload or CPU+GPU. - - **Marginal** -- Tight fit, or CPU-only (CPU-only always caps here). - - **Too Tight** -- Not enough VRAM or system RAM anywhere. - ---- - -## Model database - -The model list is generated by `scripts/scrape_hf_models.py`, a standalone Python script (stdlib only, no pip dependencies) that queries the HuggingFace REST API. Hundreds models & providers including Meta Llama, Mistral, Qwen, Google Gemma, Microsoft Phi, DeepSeek, IBM Granite, Allen Institute OLMo, xAI Grok, Cohere, BigCode, 01.ai, Upstage, TII Falcon, HuggingFace, Zhipu GLM, Moonshot Kimi, Baidu ERNIE, and more. The scraper automatically detects MoE architectures via model config (`num_local_experts`, `num_experts_per_tok`) and known architecture mappings. - -Model categories span general purpose, coding (CodeLlama, StarCoder2, WizardCoder, Qwen2.5-Coder, Qwen3-Coder), reasoning (DeepSeek-R1, Orca-2), multimodal/vision (Llama 3.2 Vision, Llama 4 Scout/Maverick, Qwen2.5-VL), chat, enterprise (IBM Granite), and embedding (nomic-embed, bge). - -See [MODELS.md](MODELS.md) for the full list. - -To refresh the model database: - -```sh -# Automated update (recommended) -make update-models - -# Or run the script directly -./scripts/update_models.sh - -# Or manually -python3 scripts/scrape_hf_models.py -cargo build --release -``` - -The scraper writes `data/hf_models.json`, which is baked into the binary via `include_str!`. The automated update script backs up existing data, validates JSON output, and rebuilds the binary. - -By default, the scraper enriches models with known GGUF download sources from providers like [unsloth](https://huggingface.co/unsloth) and [bartowski](https://huggingface.co/bartowski). Results are cached in `data/gguf_sources_cache.json` (7-day TTL) to avoid repeated API calls. Use `--no-gguf-sources` to skip enrichment for a faster scrape. - ---- - -## Project structure - -``` -src/ - main.rs -- CLI argument parsing, entrypoint, TUI launch - hardware.rs -- System RAM/CPU/GPU detection (multi-GPU, backend identification) - models.rs -- Model database, quantization hierarchy, dynamic quant selection - fit.rs -- Multi-dimensional scoring (Q/S/F/C), speed estimation, MoE offloading - providers.rs -- Runtime provider integration (Ollama, llama.cpp, MLX, Docker Model Runner, LM Studio), install detection, pull/download - display.rs -- Classic CLI table rendering + JSON output - tui_app.rs -- TUI application state, filters, navigation - tui_ui.rs -- TUI rendering (ratatui) - tui_events.rs -- TUI keyboard event handling (crossterm) -data/ - hf_models.json -- Model database (206 models) -skills/ - llmfit-advisor/ -- OpenClaw skill for hardware-aware model recommendations -scripts/ - scrape_hf_models.py -- HuggingFace API scraper - update_models.sh -- Automated database update script - install-openclaw-skill.sh -- Install the OpenClaw skill -Makefile -- Build and maintenance commands -``` - ---- - -## Publishing to crates.io - -The `Cargo.toml` already includes the required metadata (description, license, repository). To publish: - -```sh -# Dry run first to catch issues -cargo publish --dry-run - -# Publish for real (requires a crates.io API token) -cargo login -cargo publish -``` - -Before publishing, make sure: - -- The version in `Cargo.toml` is correct (bump with each release). -- A `LICENSE` file exists in the repo root. Create one if missing: - -```sh -# For MIT license: -curl -sL https://opensource.org/license/MIT -o LICENSE -# Or write your own. The Cargo.toml declares license = "MIT". -``` - -- `data/hf_models.json` is committed. It is embedded at compile time and must be present in the published crate. -- The `exclude` list in `Cargo.toml` keeps `target/`, `scripts/`, and `demo.gif` out of the published crate to keep the download small. - -To publish updates: - -```sh -# Bump version -# Edit Cargo.toml: version = "0.2.0" -cargo publish -``` - ---- - -## Dependencies - -| Crate | Purpose | -|------------------------|--------------------------------------------------| -| `clap` | CLI argument parsing with derive macros | -| `sysinfo` | Cross-platform RAM and CPU detection | -| `serde` / `serde_json` | JSON deserialization for model database | -| `tabled` | CLI table formatting | -| `colored` | CLI colored output | -| `ureq` | HTTP client for runtime/provider API integration | -| `ratatui` | Terminal UI framework | -| `crossterm` | Terminal input/output backend for ratatui | - ---- - -## Runtime provider integration - -llmfit supports multiple local runtime providers: - -- **Ollama** (daemon/API based pulls) -- **llama.cpp** (direct GGUF downloads from Hugging Face + local cache detection) -- **MLX** (Apple Silicon / mlx-community model cache + optional server) -- **Docker Model Runner** (Docker Desktop's built-in model serving) -- **LM Studio** (local model server with REST API for model management + downloads) - -When more than one compatible provider is available for a model, pressing `d` in the TUI opens a provider picker modal. - -### Ollama integration - -llmfit integrates with [Ollama](https://ollama.com) to detect which models you already have installed and to download new ones directly from the TUI. - -### Requirements - -- **Ollama must be installed and running** (`ollama serve` or the Ollama desktop app) -- llmfit connects to `http://localhost:11434` (Ollama's default API port) -- No configuration needed β€” if Ollama is running, llmfit detects it automatically - -### Remote Ollama instances - -To connect to Ollama running on a different machine or port, set the `OLLAMA_HOST` environment variable: - -```sh -# Connect to Ollama on a specific IP and port -OLLAMA_HOST="http://192.168.1.100:11434" llmfit - -# Connect via hostname -OLLAMA_HOST="http://ollama-server:666" llmfit - -# Works with all TUI and CLI commands -OLLAMA_HOST="http://192.168.1.100:11434" llmfit --cli -OLLAMA_HOST="http://192.168.1.100:11434" llmfit fit --perfect -n 5 -``` - -This is useful for: -- Running llmfit on one machine while Ollama serves from another (e.g., GPU server + laptop client) -- Connecting to Ollama running in Docker containers with custom ports -- Using Ollama behind reverse proxies or load balancers - -### How it works - -On startup, llmfit queries `GET /api/tags` to list your installed Ollama models. Each installed model gets a green **βœ“** in the **Inst** column of the TUI. The system bar shows `Ollama: βœ“ (N installed)`. - -When you press `d` on a model, llmfit sends `POST /api/pull` to Ollama to download it. The row highlights with an animated progress indicator showing download progress in real-time. Once complete, the model is immediately available for use with Ollama. - -If Ollama is not running, Ollama-specific operations are skipped; the TUI still supports other providers like llama.cpp where available. - -### llama.cpp integration - -llmfit integrates with [llama.cpp](https://github.com/ggml-org/llama.cpp) as a runtime/download provider in both TUI and CLI. - -Requirements: - -- `llama-cli` or `llama-server` available in `PATH` (for runtime detection) -- network access to Hugging Face for GGUF downloads - -How it works: - -- llmfit maps HF models to known GGUF repos (with heuristic fallbacks) -- downloads GGUF files into the local llama.cpp model cache -- marks models installed when matching GGUF files are present locally - -### Docker Model Runner integration - -llmfit integrates with [Docker Model Runner](https://docs.docker.com/desktop/features/model-runner/), Docker Desktop's built-in model serving feature. - -Requirements: - -- Docker Desktop with Model Runner enabled -- Default endpoint: `http://localhost:12434` - -How it works: - -- llmfit queries `GET /engines` to list models available in Docker Model Runner -- models are matched to the HF database using Ollama-style tag mapping (Docker Model Runner uses `ai/` naming) -- pressing `d` in the TUI pulls via `docker model pull` - -### Remote Docker Model Runner instances - -To connect to Docker Model Runner on a different host or port, set the `DOCKER_MODEL_RUNNER_HOST` environment variable: - -```sh -DOCKER_MODEL_RUNNER_HOST="http://192.168.1.100:12434" llmfit -``` - -### LM Studio integration - -llmfit integrates with [LM Studio](https://lmstudio.ai) as a local model server with built-in model download capabilities. - -Requirements: - -- LM Studio must be running with its local server enabled -- Default endpoint: `http://127.0.0.1:1234` - -How it works: - -- llmfit queries `GET /v1/models` to list models available in LM Studio -- pressing `d` in the TUI triggers a download via `POST /api/v1/models/download` -- download progress is tracked by polling `GET /api/v1/models/download-status` -- LM Studio accepts HuggingFace model names directly, so no name mapping is needed - -### Remote LM Studio instances - -To connect to LM Studio on a different host or port, set the `LMSTUDIO_HOST` environment variable: +*(The original core terminal implementation is still fully intact and functional!)* +### Windows ```sh -LMSTUDIO_HOST="http://192.168.1.100:1234" llmfit +scoop install llmfit ``` -### Model name mapping - -llmfit's database uses HuggingFace model names (e.g. `Qwen/Qwen2.5-Coder-14B-Instruct`) while Ollama uses its own naming scheme (e.g. `qwen2.5-coder:14b`). llmfit maintains an accurate mapping table between the two so that install detection and pulls resolve to the correct model. Each mapping is exact β€” `qwen2.5-coder:14b` maps to the Coder model, not the base `qwen2.5:14b`. - ---- - -## Platform support - -- **Linux** -- Full support. GPU detection via `nvidia-smi` (NVIDIA), `rocm-smi` (AMD), sysfs/`lspci` (Intel Arc) and `npu-smi` (Ascend). -- **macOS (Apple Silicon)** -- Full support. Detects unified memory via `system_profiler`. VRAM = system RAM (shared pool). Models run via Metal GPU acceleration. -- **macOS (Intel)** -- RAM and CPU detection works. Discrete GPU detection if `nvidia-smi` available. -- **Windows** -- RAM and CPU detection works. NVIDIA GPU detection via `nvidia-smi` if installed. -- **Android / Termux / PRoot** -- CPU and RAM detection usually work, but GPU autodetection is not currently supported. Mobile GPUs such as Adreno typically are not visible through the desktop/server probing interfaces llmfit uses. - -### GPU support - -| Vendor | Detection method | VRAM reporting | -|------------------------|-------------------------------|--------------------------------| -| NVIDIA | `nvidia-smi` | Exact dedicated VRAM | -| AMD | `rocm-smi` | Detected (VRAM may be unknown) | -| Intel Arc (discrete) | sysfs (`mem_info_vram_total`) | Exact dedicated VRAM | -| Intel Arc (integrated) | `lspci` | Shared system memory | -| Apple Silicon | `system_profiler` | Unified memory (= system RAM) | -| Ascend | `npu-smi` | Detected (VRAM may be unknown) | - -If autodetection fails or reports incorrect values, use `--memory=` to override (see [GPU memory override](#gpu-memory-override) above). - -### Android / Termux note - -On Android setups such as **Termux + PRoot**, llmfit usually cannot see mobile GPUs through the standard Linux detection paths (`nvidia-smi`, `rocm-smi`, DRM/sysfs, `lspci`, etc.). In those environments, "no GPU detected" is expected with the current implementation. - -If you still want GPU-style recommendations on a unified-memory phone or tablet, use a manual memory override: - +### macOS / Linux +#### Homebrew ```sh -llmfit --memory=8G fit -n 20 -llmfit recommend --json --memory=8G --limit 10 +brew install llmfit ``` - -This is a workaround for recommendation/scoring only; it does not provide true Android GPU runtime detection. - ---- - -## Contributing - -Contributions are welcome, especially new models. - -### Adding a model - -1. Add the model's HuggingFace repo ID (e.g., `meta-llama/Llama-3.1-8B`) to the `TARGET_MODELS` list in `scripts/scrape_hf_models.py`. -2. If the model is gated (requires HuggingFace authentication to access metadata), add a fallback entry to the `FALLBACKS` list in the same script with the parameter count and context length. -3. Run the automated update script: - ```sh - make update-models - # or: ./scripts/update_models.sh - ``` -4. Verify the updated model list: `./target/release/llmfit list` -5. Update [MODELS.md](MODELS.md) by running: `python3 << 'EOF' < scripts/...` (see commit history for the generator script) -6. Open a pull request. - -See [MODELS.md](MODELS.md) for the current list and [AGENTS.md](AGENTS.md) for architecture details. - ---- - -## OpenClaw integration - -llmfit ships as an [OpenClaw](https://github.com/openclaw/openclaw) skill that lets the agent recommend hardware-appropriate local models and auto-configure Ollama/vLLM/LM Studio providers. - -### Install the skill - +#### Quick install ```sh -# From the llmfit repo -./scripts/install-openclaw-skill.sh - -# Or manually -cp -r skills/llmfit-advisor ~/.openclaw/skills/ +curl -fsSL https://llmfit.axjns.dev/install.sh | sh ``` -Once installed, ask your OpenClaw agent things like: - -- "What local models can I run?" -- "Recommend a coding model for my hardware" -- "Set up Ollama with the best models for my GPU" - -The agent will call `llmfit recommend --json` under the hood, interpret the results, and offer to configure your `openclaw.json` with optimal model choices. - -### How it works - -The skill teaches the OpenClaw agent to: - -1. Detect your hardware via `llmfit --json system` -2. Get ranked recommendations via `llmfit recommend --json` -3. Map HuggingFace model names to Ollama/vLLM/LM Studio tags -4. Configure `models.providers.ollama.models` in `openclaw.json` - -See [skills/llmfit-advisor/SKILL.md](skills/llmfit-advisor/SKILL.md) for the full skill definition. - --- -## Alternatives +## πŸ› οΈ How it works (Core Engine) -If you're looking for a different approach, check out [llm-checker](https://github.com/Pavelevich/llm-checker) -- a Node.js CLI tool with Ollama integration that can pull and benchmark models directly. It takes a more hands-on approach by actually running models on your hardware via Ollama, rather than estimating from specs. Good if you already have Ollama installed and want to test real-world performance. Note that it doesn't support MoE (Mixture-of-Experts) architectures -- all models are treated as dense, so memory estimates for models like Mixtral or DeepSeek-V3 will reflect total parameter count rather than the smaller active subset. +1. **Hardware detection:** Automatically reads total/available RAM, counts CPU cores, and probes for GPUs (NVIDIA, AMD, Intel, Apple Silicon). +2. **Model database:** Cross-references hundreds of LLM models and detects MoE (Mixture-of-Experts) architectures to calculate precise VRAM constraints. +3. **Dynamic quantization:** Walks a hierarchy from `Q8_0` down to `Q2_K` to intelligently fit your exact memory footprint. +4. **Multi-dimensional scoring:** Analyzes models tailored specifically to *your* exact machine configuration: + - **Quality:** Model reputation and quantization penalty. + - **Speed:** Estimated tokens/sec based on your GPU backend. + - **Fit:** Memory utilization efficiency. + - **Context:** Feasible context lengths. ---- +## 🀝 Contributing +Contributions, UI/UX enhancements, and performance PRs are always welcome. 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fn score_fit( } match run_mode { - RunMode::Gpu | RunMode::TensorParallel => { + RunMode::Gpu => { if recommended <= mem_available { FitLevel::Perfect } else if mem_available >= mem_required * 1.2 { @@ -821,7 +799,6 @@ fn estimate_tps( let mode_factor = match run_mode { RunMode::Gpu => 1.0, - RunMode::TensorParallel => 0.9, RunMode::MoeOffload => 0.8, RunMode::CpuOffload => 0.5, RunMode::CpuOnly => unreachable!(), @@ -858,11 +835,10 @@ fn estimate_tps( // Run mode penalties match run_mode { - RunMode::Gpu => {} // full speed - RunMode::TensorParallel => base *= 0.9, // TP communication overhead - RunMode::MoeOffload => base *= 0.8, // expert switching latency - RunMode::CpuOffload => base *= 0.5, // significant penalty - RunMode::CpuOnly => base *= 0.3, // worst caseβ€”override K to CPU + RunMode::Gpu => {} // full speed + RunMode::MoeOffload => base *= 0.8, // expert switching latency + RunMode::CpuOffload => base *= 0.5, // significant penalty + RunMode::CpuOnly => base *= 0.3, // worst caseβ€”override K to CPU } // CPU-only should use CPU K regardless of detected GPU @@ -1070,8 +1046,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: models::ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, } } @@ -1097,8 +1071,6 @@ mod tests { GpuBackend::CpuX86 }, gpus: vec![], - cluster_mode: false, - cluster_node_count: 0, } } @@ -1250,8 +1222,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: models::ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let mut system = test_system(64.0, true, Some(8.0)); system.backend = GpuBackend::Cuda; @@ -1285,8 +1255,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: models::ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let system = test_system(12.0, true, Some(8.0)); @@ -1728,8 +1696,6 @@ mod tests { unified_memory: false, backend: GpuBackend::Cuda, gpus: vec![], - cluster_mode: false, - cluster_node_count: 0, } } diff --git a/llmfit-core/src/hardware.rs b/llmfit-core/src/hardware.rs index 3c0179710..f1b8777f0 100644 --- a/llmfit-core/src/hardware.rs +++ b/llmfit-core/src/hardware.rs @@ -57,10 +57,6 @@ pub struct SystemSpecs { pub backend: GpuBackend, /// All detected GPUs (may span different vendors/backends). pub gpus: Vec, - /// True when running in multi-node cluster mode (e.g. DGX Spark cluster). - pub cluster_mode: bool, - /// Number of nodes in the cluster (0 or 1 = single machine). - pub cluster_node_count: u32, } impl SystemSpecs { @@ -116,8 +112,6 @@ impl SystemSpecs { unified_memory, backend, gpus, - cluster_mode: false, - cluster_node_count: 0, } } @@ -185,12 +179,14 @@ impl SystemSpecs { // These share the full system RAM between CPU and GPU, like Apple Silicon. // nvidia-smi may report 0 VRAM or a small dedicated portion, so we // override with total system RAM and flag as unified memory. - // Inside Docker the friendly name may be missing; we also match by PCI - // device ID (e.g. "Device [10de:2e12]"). - let is_nvidia_unified = gpus.iter().any(|g| is_nvidia_unified_memory_gpu(&g.name)); + let is_nvidia_unified = gpus.iter().any(|g| { + let lower = g.name.to_lowercase(); + lower.contains("gb10") || lower.contains("gb20") + }); if is_nvidia_unified { for gpu in &mut gpus { - if is_nvidia_unified_memory_gpu(&gpu.name) { + let lower = gpu.name.to_lowercase(); + if lower.contains("gb10") || lower.contains("gb20") { gpu.unified_memory = true; gpu.vram_gb = Some(total_ram_gb); } @@ -503,14 +499,12 @@ impl SystemSpecs { } } - let unified_memory = is_nvidia_unified_memory_gpu(&name); - Some(GpuInfo { name, vram_gb, backend, count: gpu_count, - unified_memory, + unified_memory: false, }) } @@ -535,6 +529,7 @@ impl SystemSpecs { // "GPU[0] : vram Total Memory (B): 8589934592" // or in table format with "Total" and bytes. let mut per_gpu_vram_bytes: Vec = Vec::new(); + let mut gpu_count: u32 = 0; for line in vram_text.lines() { let lower = line.to_lowercase(); if lower.contains("total") && !lower.contains("used") { @@ -546,27 +541,15 @@ impl SystemSpecs { && val > 0 { per_gpu_vram_bytes.push(val); + gpu_count += 1; } } } - // Filter out integrated GPUs (iGPUs) that have very little VRAM. - // rocm-smi reports all GPU agents including iGPUs on APUs like - // Ryzen 9800X3D, which would otherwise inflate the GPU count. - // Discrete GPUs have >= 2 GB VRAM; iGPUs typically show < 1 GB. - const IGPU_VRAM_THRESHOLD: u64 = 2 * 1024 * 1024 * 1024; // 2 GB - let discrete_vram: Vec = per_gpu_vram_bytes - .iter() - .copied() - .filter(|&v| v >= IGPU_VRAM_THRESHOLD) - .collect(); - let (effective_vram, gpu_count) = if discrete_vram.is_empty() { - // No discrete GPUs found; use all entries (may be an iGPU-only system) - (per_gpu_vram_bytes, 1u32) - } else { - let count = discrete_vram.len() as u32; - (discrete_vram, count) - }; + if gpu_count == 0 { + // rocm-smi succeeded but we couldn't parse VRAM; GPU exists though + gpu_count = 1; + } // Try to get GPU name from rocm-smi --showproductname let gpu_name = std::process::Command::new("rocm-smi") @@ -597,7 +580,7 @@ impl SystemSpecs { }); let name = gpu_name.unwrap_or_else(|| "AMD GPU".to_string()); - let max_per_gpu_bytes = effective_vram.into_iter().max().unwrap_or(0); + let max_per_gpu_bytes = per_gpu_vram_bytes.into_iter().max().unwrap_or(0); let vram_gb = if max_per_gpu_bytes > 0 { Some(max_per_gpu_bytes as f64 / (1024.0 * 1024.0 * 1024.0)) } else { @@ -1989,25 +1972,6 @@ pub fn quant_min_compute_capability(quantization: &str) -> Option<(u8, u8)> { } } -/// Check if a GPU name (including PCI device IDs from lspci) indicates an -/// NVIDIA unified memory SoC (Grace Blackwell / DGX Spark / GB-series). -/// Inside Docker, nvidia-smi may report the raw PCI device ID instead of the -/// friendly model name, e.g. "NVIDIA Corporation Device [10de:2e12] (rev a1)" -/// instead of "NVIDIA GB10". -fn is_nvidia_unified_memory_gpu(name: &str) -> bool { - let lower = name.to_lowercase(); - // Friendly model names - if lower.contains("gb10") || lower.contains("gb20") { - return true; - } - // PCI device IDs (hex) β€” these are the known GB-series SoCs. - // 10de:2e12 = GB10 (DGX Spark / Project DIGITS) - if lower.contains("2e12") { - return true; - } - false -} - /// Fallback VRAM estimation from GPU model name. /// Used when nvidia-smi or other tools report 0 VRAM. fn estimate_vram_from_name(name: &str) -> f64 { @@ -2104,10 +2068,8 @@ fn estimate_vram_from_name(name: &str) -> f64 { if lower.contains("t4") { return 16.0; } - // NVIDIA Grace / DGX Spark unified memory SoCs. - // Also match PCI device ID 2e12 (GB10) for Docker/container environments - // where lspci shows "Device [10de:2e12]" instead of the friendly name. - if lower.contains("gb10") || lower.contains("2e12") { + // NVIDIA Grace / DGX Spark unified memory SoCs + if lower.contains("gb10") { return 128.0; } if lower.contains("gb20") { @@ -2558,8 +2520,6 @@ GPU id = 1 (NVIDIA GeForce RTX 4090) unified_memory: false, backend: super::GpuBackend::CpuX86, gpus: vec![], - cluster_mode: false, - cluster_node_count: 0, } } @@ -2583,8 +2543,6 @@ GPU id = 1 (NVIDIA GeForce RTX 4090) count: 1, unified_memory: false, }], - cluster_mode: false, - cluster_node_count: 0, } } diff --git a/llmfit-core/src/models.rs b/llmfit-core/src/models.rs index acea23603..02d7bdfe5 100644 --- a/llmfit-core/src/models.rs +++ b/llmfit-core/src/models.rs @@ -1,3 +1,5 @@ +use std::collections::HashMap; + use serde::{Deserialize, Serialize}; /// Quantization levels ordered from best quality to most compressed. @@ -243,12 +245,6 @@ pub struct LlmModel { /// Model weight format (gguf, awq, gptq, mlx, safetensors) #[serde(default)] pub format: ModelFormat, - /// Number of attention heads (for tensor-parallelism compatibility checks). - #[serde(default)] - pub num_attention_heads: Option, - /// Number of key-value heads for GQA (defaults to num_attention_heads if None). - #[serde(default)] - pub num_key_value_heads: Option, } /// A known GGUF download source for a model on HuggingFace. @@ -275,34 +271,6 @@ impl LlmModel { self.format.is_prequantized() } - /// Returns true if the model's attention/KV heads are evenly divisible - /// by `tp_size`, meaning it can be split across that many devices. - /// TP=1 always returns true. - pub fn supports_tp(&self, tp_size: u32) -> bool { - if tp_size <= 1 { - return true; - } - let (attn, kv) = self.infer_head_counts(); - attn % tp_size == 0 && kv % tp_size == 0 - } - - /// Returns all valid TP degrees in [1..=8] for this model. - pub fn valid_tp_sizes(&self) -> Vec { - (1..=8).filter(|&tp| self.supports_tp(tp)).collect() - } - - /// Infer attention and KV head counts from metadata or model name heuristics. - fn infer_head_counts(&self) -> (u32, u32) { - if let (Some(attn), Some(kv)) = (self.num_attention_heads, self.num_key_value_heads) { - return (attn, kv); - } - if let Some(attn) = self.num_attention_heads { - return (attn, attn); - } - // Heuristic: infer from model name - infer_heads_from_name(&self.name, self.params_b()) - } - /// Bytes-per-parameter for the model's quantization level. fn quant_bpp(&self) -> f64 { quant_bpp(&self.quantization) @@ -443,6 +411,126 @@ struct HfModelEntry { hf_likes: u64, } +fn parse_parameter_count_hint(parameter_count: &str) -> Option { + let normalized = parameter_count.trim().replace(',', "").to_uppercase(); + + if let Some(raw) = normalized.strip_suffix('B') { + raw.parse::() + .ok() + .map(|value| (value * 1_000_000_000.0).round() as u64) + } else if let Some(raw) = normalized.strip_suffix('M') { + raw.parse::() + .ok() + .map(|value| (value * 1_000_000.0).round() as u64) + } else { + None + } +} + +fn effective_parameters_raw(entry: &HfModelEntry) -> Option { + entry + .parameters_raw + .or_else(|| parse_parameter_count_hint(&entry.parameter_count)) +} + +fn option_max(left: Option, right: Option) -> Option { + match (left, right) { + (Some(left), Some(right)) => Some(if right > left { right } else { left }), + (Some(left), None) => Some(left), + (None, Some(right)) => Some(right), + (None, None) => None, + } +} + +fn hf_entry_rank(entry: &HfModelEntry) -> (u64, u64, usize, usize, u8, u64, u64, u32) { + ( + entry.hf_downloads, + entry.hf_likes, + entry.capabilities.len(), + entry.gguf_sources.len(), + u8::from(entry.release_date.is_some()), + entry.active_parameters.unwrap_or(0), + effective_parameters_raw(entry).unwrap_or(0), + entry.context_length, + ) +} + +fn merge_exact_name_entries( + mut primary: HfModelEntry, + mut secondary: HfModelEntry, +) -> HfModelEntry { + if hf_entry_rank(&secondary) > hf_entry_rank(&primary) { + std::mem::swap(&mut primary, &mut secondary); + } + + let primary_effective_params = effective_parameters_raw(&primary).unwrap_or(0); + let secondary_effective_params = effective_parameters_raw(&secondary).unwrap_or(0); + + if secondary_effective_params > primary_effective_params { + primary.parameter_count = secondary.parameter_count.clone(); + } + primary.parameters_raw = option_max(primary.parameters_raw, secondary.parameters_raw); + primary.min_ram_gb = primary.min_ram_gb.max(secondary.min_ram_gb); + primary.recommended_ram_gb = primary.recommended_ram_gb.max(secondary.recommended_ram_gb); + primary.min_vram_gb = option_max(primary.min_vram_gb, secondary.min_vram_gb); + primary.context_length = primary.context_length.max(secondary.context_length); + primary.is_moe |= secondary.is_moe; + primary.num_experts = option_max(primary.num_experts, secondary.num_experts); + primary.active_experts = option_max(primary.active_experts, secondary.active_experts); + primary.active_parameters = option_max(primary.active_parameters, secondary.active_parameters); + + if primary.provider.is_empty() { + primary.provider = secondary.provider.clone(); + } + if primary.quantization.is_empty() { + primary.quantization = secondary.quantization.clone(); + } + if primary.use_case.is_empty() { + primary.use_case = secondary.use_case.clone(); + } + if primary.format == ModelFormat::default() && secondary.format != ModelFormat::default() { + primary.format = secondary.format; + } + if secondary.release_date.as_deref() > primary.release_date.as_deref() { + primary.release_date = secondary.release_date.clone(); + } + + for capability in secondary.capabilities { + if !primary.capabilities.contains(&capability) { + primary.capabilities.push(capability); + } + } + + for source in secondary.gguf_sources { + let exists = primary + .gguf_sources + .iter() + .any(|existing| existing.repo == source.repo && existing.provider == source.provider); + if !exists { + primary.gguf_sources.push(source); + } + } + + primary +} + +fn dedupe_hf_entries(entries: Vec) -> Vec { + let mut deduped_entries: Vec = Vec::with_capacity(entries.len()); + let mut deduped_indices: HashMap = HashMap::new(); + + for entry in entries { + let key = entry.name.to_lowercase(); + if let Some(&idx) = deduped_indices.get(&key) { + deduped_entries[idx] = merge_exact_name_entries(deduped_entries[idx].clone(), entry); + } else { + deduped_indices.insert(key, deduped_entries.len()); + deduped_entries.push(entry); + } + } + + deduped_entries +} + const HF_MODELS_JSON: &str = include_str!("../data/hf_models.json"); pub struct ModelDatabase { @@ -460,7 +548,9 @@ impl ModelDatabase { let entries: Vec = serde_json::from_str(HF_MODELS_JSON).expect("Failed to parse embedded hf_models.json"); - let models = entries + let deduped_entries = dedupe_hf_entries(entries); + + let models = deduped_entries .into_iter() .map(|e| { let mut model = LlmModel { @@ -482,8 +572,6 @@ impl ModelDatabase { gguf_sources: e.gguf_sources, capabilities: e.capabilities, format: e.format, - num_attention_heads: None, - num_key_value_heads: None, }; model.capabilities = Capability::infer(&model); model @@ -542,108 +630,6 @@ impl ModelDatabase { } } -/// Infer attention and KV head counts from the model name and parameter count. -/// Used as a fallback when explicit head counts are not available in the model metadata. -fn infer_heads_from_name(name: &str, params_b: f64) -> (u32, u32) { - let name_lower = name.to_lowercase(); - - // Qwen family - if name_lower.contains("qwen") { - if params_b > 100.0 { - return (128, 16); - } else if params_b > 50.0 { - return (64, 8); - } else if params_b > 25.0 { - return (40, 8); - } else if params_b > 10.0 { - return (40, 8); - } else if params_b > 5.0 { - return (32, 8); - } else { - return (16, 4); - } - } - - // Llama family - if name_lower.contains("llama") { - if name_lower.contains("scout") || name_lower.contains("maverick") { - return (64, 8); - } else if params_b > 60.0 { - return (64, 8); - } else if params_b > 20.0 { - return (48, 8); - } else if params_b > 5.0 { - return (32, 8); - } else { - return (16, 8); - } - } - - // DeepSeek family - if name_lower.contains("deepseek") { - if params_b > 200.0 { - return (128, 16); - } else if params_b > 50.0 { - return (64, 8); - } else if params_b > 25.0 { - return (40, 8); - } else if params_b > 10.0 { - return (40, 8); - } else { - return (32, 8); - } - } - - // Mistral/Mixtral - if name_lower.contains("mistral") || name_lower.contains("mixtral") { - if params_b > 100.0 { - return (96, 8); - } else if params_b > 20.0 { - return (32, 8); - } else { - return (32, 8); - } - } - - // Gemma - if name_lower.contains("gemma") { - if params_b > 20.0 { - return (32, 16); - } else if params_b > 5.0 { - return (16, 8); - } else { - return (8, 4); - } - } - - // Phi - if name_lower.contains("phi") { - if params_b > 10.0 { - return (40, 10); - } else { - return (32, 8); - } - } - - // MiniMax - if name_lower.contains("minimax") { - return (48, 8); - } - - // Default: common pattern based on param count - if params_b > 100.0 { - (128, 16) - } else if params_b > 50.0 { - (64, 8) - } else if params_b > 20.0 { - (32, 8) - } else if params_b > 5.0 { - (32, 8) - } else { - (16, 4) - } -} - #[cfg(test)] mod tests { use super::*; @@ -683,8 +669,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; // Large budget should return mlx-8bit (best in MLX hierarchy) @@ -756,8 +740,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert_eq!(model.params_b(), 7.0); } @@ -783,8 +765,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert_eq!(model.params_b(), 13.0); } @@ -810,8 +790,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert_eq!(model.params_b(), 0.5); } @@ -837,8 +815,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let mem = model.estimate_memory_gb("Q4_K_M", 4096); @@ -872,8 +848,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; // Large budget should return best quant @@ -913,8 +887,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert!(dense_model.moe_active_vram_gb().is_none()); @@ -938,8 +910,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let vram = moe_model.moe_active_vram_gb(); assert!(vram.is_some()); @@ -971,8 +941,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert!(dense_model.moe_offloaded_ram_gb().is_none()); @@ -996,8 +964,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let offloaded = moe_model.moe_offloaded_ram_gb(); assert!(offloaded.is_some()); @@ -1031,8 +997,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert_eq!(UseCase::from_model(&model), UseCase::Coding); } @@ -1058,8 +1022,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert_eq!(UseCase::from_model(&model), UseCase::Embedding); } @@ -1085,8 +1047,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; assert_eq!(UseCase::from_model(&model), UseCase::Reasoning); } @@ -1103,6 +1063,124 @@ mod tests { assert!(!models.is_empty()); } + #[test] + fn test_dedupe_hf_entries_merges_duplicate_metadata() { + let deduped = dedupe_hf_entries(vec![ + HfModelEntry { + name: "Example/Model".to_string(), + provider: "Example".to_string(), + parameter_count: "18B".to_string(), + parameters_raw: Some(18_000_000_000), + min_ram_gb: 10.0, + recommended_ram_gb: 18.0, + min_vram_gb: Some(8.0), + quantization: "Q4_K_M".to_string(), + context_length: 32768, + use_case: "General".to_string(), + is_moe: false, + num_experts: None, + active_experts: None, + active_parameters: None, + release_date: Some("2026-01-01".to_string()), + gguf_sources: vec![GgufSource { + repo: "example/example-model-gguf".to_string(), + provider: "example".to_string(), + }], + capabilities: vec![Capability::Vision], + format: ModelFormat::Safetensors, + hf_downloads: 10_000, + hf_likes: 500, + }, + HfModelEntry { + name: "Example/Model".to_string(), + provider: "Example".to_string(), + parameter_count: "20B".to_string(), + parameters_raw: Some(20_000_000_000), + min_ram_gb: 12.0, + recommended_ram_gb: 24.0, + min_vram_gb: Some(10.0), + quantization: "Q4_K_M".to_string(), + context_length: 65536, + use_case: "General".to_string(), + is_moe: true, + num_experts: Some(64), + active_experts: Some(8), + active_parameters: Some(3_000_000_000), + release_date: Some("2026-02-01".to_string()), + gguf_sources: vec![GgufSource { + repo: "unsloth/example-model-gguf".to_string(), + provider: "unsloth".to_string(), + }], + capabilities: vec![Capability::ToolUse], + format: ModelFormat::Gguf, + hf_downloads: 100, + hf_likes: 10, + }, + ]); + + assert_eq!(deduped.len(), 1); + let merged = &deduped[0]; + assert_eq!(merged.parameter_count, "20B"); + assert_eq!(merged.parameters_raw, Some(20_000_000_000)); + assert_eq!(merged.min_ram_gb, 12.0); + assert_eq!(merged.recommended_ram_gb, 24.0); + assert_eq!(merged.min_vram_gb, Some(10.0)); + assert_eq!(merged.context_length, 65536); + assert!(merged.is_moe); + assert_eq!(merged.num_experts, Some(64)); + assert_eq!(merged.active_experts, Some(8)); + assert_eq!(merged.active_parameters, Some(3_000_000_000)); + assert!(merged.capabilities.contains(&Capability::Vision)); + assert!(merged.capabilities.contains(&Capability::ToolUse)); + assert_eq!(merged.gguf_sources.len(), 2); + assert!( + merged + .gguf_sources + .iter() + .any(|source| source.repo == "example/example-model-gguf") + ); + assert!( + merged + .gguf_sources + .iter() + .any(|source| source.repo == "unsloth/example-model-gguf") + ); + } + + #[test] + fn test_model_database_deduplicates_exact_name_collisions() { + let db = ModelDatabase::new(); + let matches: Vec<_> = db + .get_all_models() + .iter() + .filter(|m| m.name == "Qwen/Qwen3-Coder-Next") + .collect(); + + assert_eq!( + matches.len(), + 1, + "duplicate exact model names should be collapsed" + ); + + let model = matches[0]; + assert_eq!(model.use_case, "Code generation and completion"); + assert_eq!(model.parameter_count, "80B"); + assert_eq!(model.parameters_raw, Some(80_000_000_000)); + assert_eq!(model.min_ram_gb, 44.8); + assert_eq!(model.recommended_ram_gb, 74.6); + assert_eq!(model.min_vram_gb, Some(41.0)); + assert!(model.is_moe); + assert_eq!(model.num_experts, Some(64)); + assert_eq!(model.active_experts, Some(4)); + assert_eq!(model.active_parameters, Some(3_000_000_000)); + assert!( + model + .gguf_sources + .iter() + .any(|source| source.repo == "unsloth/Qwen3-Coder-Next-GGUF") + ); + } + #[test] fn test_find_model() { let db = ModelDatabase::new(); @@ -1164,8 +1242,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let caps = Capability::infer(&model); assert!(caps.contains(&Capability::Vision)); @@ -1194,8 +1270,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let caps = Capability::infer(&model); assert!(caps.contains(&Capability::ToolUse)); @@ -1223,8 +1297,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let caps = Capability::infer(&model); assert!(caps.is_empty()); @@ -1251,8 +1323,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![Capability::Vision], format: ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }; let caps = Capability::infer(&model); // Should keep the explicit Vision and not duplicate it @@ -1383,106 +1453,4 @@ mod tests { total ); } - - // ──────────────────────────────────────────────────────────────────── - // Tensor parallelism tests - // ──────────────────────────────────────────────────────────────────── - - fn tp_test_model( - name: &str, - params_b: f64, - attn_heads: Option, - kv_heads: Option, - ) -> LlmModel { - LlmModel { - name: name.to_string(), - provider: "Test".to_string(), - parameter_count: format!("{:.0}B", params_b), - parameters_raw: Some((params_b * 1_000_000_000.0) as u64), - min_ram_gb: 4.0, - recommended_ram_gb: 8.0, - min_vram_gb: Some(4.0), - quantization: "Q4_K_M".to_string(), - context_length: 4096, - use_case: "General".to_string(), - is_moe: false, - num_experts: None, - active_experts: None, - active_parameters: None, - release_date: None, - gguf_sources: vec![], - capabilities: vec![], - format: ModelFormat::default(), - num_attention_heads: attn_heads, - num_key_value_heads: kv_heads, - } - } - - #[test] - fn test_supports_tp_with_explicit_heads() { - let model = tp_test_model("Test-8B", 8.0, Some(32), Some(8)); - assert!(model.supports_tp(1)); - assert!(model.supports_tp(2)); - assert!(model.supports_tp(4)); - assert!(model.supports_tp(8)); - assert!(!model.supports_tp(3)); // 32 % 3 != 0 - assert!(!model.supports_tp(5)); - } - - #[test] - fn test_supports_tp_always_true_for_1() { - let model = tp_test_model("Tiny", 1.0, None, None); - assert!(model.supports_tp(1)); - } - - #[test] - fn test_valid_tp_sizes_32_8() { - let model = tp_test_model("Test", 8.0, Some(32), Some(8)); - let sizes = model.valid_tp_sizes(); - assert!(sizes.contains(&1)); - assert!(sizes.contains(&2)); - assert!(sizes.contains(&4)); - assert!(sizes.contains(&8)); - assert!(!sizes.contains(&3)); - } - - #[test] - fn test_valid_tp_sizes_48_heads() { - // 48 attn heads, 8 kv heads β€” TP must divide both - let model = tp_test_model("Llama-32B", 32.0, Some(48), Some(8)); - assert!(model.supports_tp(2)); // 48%2==0, 8%2==0 - assert!(!model.supports_tp(3)); // 48%3==0 but 8%3!=0 - assert!(model.supports_tp(4)); // 48%4==0, 8%4==0 - assert!(model.supports_tp(8)); // 48%8==0, 8%8==0 - } - - #[test] - fn test_infer_heads_from_name_qwen() { - let (attn, kv) = infer_heads_from_name("Qwen2.5-72B-Instruct", 72.0); - assert_eq!(attn, 64); - assert_eq!(kv, 8); - } - - #[test] - fn test_infer_heads_from_name_llama() { - let (attn, kv) = infer_heads_from_name("Llama-3.1-8B", 8.0); - assert_eq!(attn, 32); - assert_eq!(kv, 8); - } - - #[test] - fn test_infer_heads_from_name_deepseek() { - let (attn, kv) = infer_heads_from_name("DeepSeek-V3", 671.0); - assert_eq!(attn, 128); - assert_eq!(kv, 16); - } - - #[test] - fn test_supports_tp_with_inferred_heads() { - // No explicit heads β€” should infer from name - let model = tp_test_model("Llama-3.1-70B", 70.0, None, None); - assert!(model.supports_tp(2)); - assert!(model.supports_tp(4)); - assert!(model.supports_tp(8)); - } } diff --git a/llmfit-core/src/plan.rs b/llmfit-core/src/plan.rs index 7cbf54a40..d765bcbce 100644 --- a/llmfit-core/src/plan.rs +++ b/llmfit-core/src/plan.rs @@ -731,8 +731,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: crate::models::ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, } } @@ -750,8 +748,6 @@ mod tests { unified_memory: false, backend: GpuBackend::Cuda, gpus: vec![], - cluster_mode: false, - cluster_node_count: 0, } } diff --git a/llmfit-core/src/providers.rs b/llmfit-core/src/providers.rs index 8fcc59fd5..67cd97a6d 100644 --- a/llmfit-core/src/providers.rs +++ b/llmfit-core/src/providers.rs @@ -5,7 +5,6 @@ use std::collections::HashSet; use std::path::PathBuf; -use which::which; // --------------------------------------------------------------------------- // Provider trait @@ -917,9 +916,21 @@ fn llamacpp_models_dir() -> PathBuf { /// Find a binary in PATH using `which`. fn find_binary(name: &str) -> Option { - which::which(name) + std::process::Command::new("which") + .arg(name) + .stdout(std::process::Stdio::piped()) + .stderr(std::process::Stdio::null()) + .output() .ok() - .map(|p| p.to_string_lossy().to_string()) + .and_then(|out| { + if out.status.success() { + String::from_utf8(out.stdout) + .ok() + .map(|s| s.trim().to_string()) + } else { + None + } + }) } /// Simple percent-encoding for URL query parameters. diff --git a/llmfit-desktop/frontend/components.json b/llmfit-desktop/frontend/components.json new file mode 100644 index 000000000..275d4164c --- /dev/null +++ b/llmfit-desktop/frontend/components.json @@ -0,0 +1,21 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": false, + "tsx": true, + "tailwind": { + "config": "tailwind.config.js", + "css": "src/index.css", + "baseColor": "zinc", + "cssVariables": true, + "prefix": "" + }, + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "iconLibrary": "lucide" +} diff --git a/llmfit-desktop/frontend/dev_error.txt b/llmfit-desktop/frontend/dev_error.txt new file mode 100644 index 000000000..61dfa2c83 --- /dev/null +++ b/llmfit-desktop/frontend/dev_error.txt @@ -0,0 +1,22 @@ +ο»Ώ +> llmfit-desktop-frontend@0.8.0 dev +> vite + +node.exe : error when starting dev server: +At line:1 char:1 ++ & "C:\nvm4w\nodejs/node.exe" "C:\nvm4w\nodejs/node_modul +es/npm/bin/np ... ++ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~ + + CategoryInfo : NotSpecified: (error w + hen starting dev server::String) [], RemoteException + + FullyQualifiedErrorId : NativeCommandError + +Error: Port 1420 is already in use + at Server.onError (file:///C:/Users/Administrator/Desk +top/llmfit-main/llmfit-desktop/frontend/node_modules/vite/ +dist/node/chunks/dep-D4NMHUTW.js:25023:18) + at Server.emit (node:events:508:28) + at emitErrorNT (node:net:1976:8) + at process.processTicksAndRejections (node:internal/pr +ocess/task_queues:89:21) diff --git a/llmfit-desktop/frontend/index.html b/llmfit-desktop/frontend/index.html new file mode 100644 index 000000000..6c6709be8 --- /dev/null +++ b/llmfit-desktop/frontend/index.html @@ -0,0 +1,12 @@ + + + + + + llmfit β€” LLM Hardware Fitting + + +

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a/llmfit-desktop/frontend/package.json b/llmfit-desktop/frontend/package.json new file mode 100644 index 000000000..92962e962 --- /dev/null +++ b/llmfit-desktop/frontend/package.json @@ -0,0 +1,37 @@ +{ + "name": "llmfit-desktop-frontend", + "private": true, + "version": "0.8.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview", + "test": "vitest run" + }, + "dependencies": { + "@radix-ui/react-label": "^2.1.8", + "@radix-ui/react-progress": "^1.1.8", + "@radix-ui/react-select": "^2.2.6", + "@radix-ui/react-slot": "^1.2.4", + "@tauri-apps/api": "^2.2.0", + "react": "^18.3.1", + "react-dom": "^18.3.1" + }, + "devDependencies": { + "@testing-library/jest-dom": "^6.9.1", + "@testing-library/react": "^16.2.0", + "@vitejs/plugin-react": "^4.3.4", + "autoprefixer": "^10.4.27", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "jsdom": "^25.0.1", + "lucide-react": "^0.577.0", + "postcss": "^8.5.8", + "tailwind-merge": "^3.5.0", + "tailwindcss": "^3.4.19", + "tailwindcss-animate": "^1.0.7", + "vite": "^6.0.7", + "vitest": "^2.1.8" + } +} diff --git a/llmfit-desktop/frontend/postcss.config.js b/llmfit-desktop/frontend/postcss.config.js new file mode 100644 index 000000000..2e7af2b7f --- /dev/null +++ b/llmfit-desktop/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/llmfit-desktop/frontend/src/App.jsx b/llmfit-desktop/frontend/src/App.jsx new file mode 100644 index 000000000..ac3b69f1b --- /dev/null +++ b/llmfit-desktop/frontend/src/App.jsx @@ -0,0 +1,483 @@ +import React, { useEffect, useMemo, useState } from 'react'; +import { DEFAULT_FILTERS, fetchModels, fetchSystemInfo } from './api'; +import { Button } from "@/components/ui/button"; +import { Card, CardContent, CardHeader, CardTitle, CardDescription } from "@/components/ui/card"; +import { Badge } from "@/components/ui/badge"; +import { Progress } from "@/components/ui/progress"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; +import { + Table, + TableBody, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; +import { + Server, Cpu, HardDrive, RefreshCw, Sun, Moon, + Activity, Monitor, Database, Settings, BarChart, MemoryStick +} from "lucide-react"; + +const THEME_KEY = 'llmfit-theme'; + +const FIT_OPTIONS = [ + { value: 'marginal', label: 'Runnable (Marginal+)' }, + { value: 'good', label: 'Good or better' }, + { value: 'perfect', label: 'Perfect only' }, + { value: 'too_tight', label: 'Too-tight only' }, + { value: 'all', label: 'All levels' } +]; + +const RUNTIME_OPTIONS = [ + { value: 'any', label: 'Any runtime' }, + { value: 'mlx', label: 'MLX' }, + { value: 'llamacpp', label: 'llama.cpp' }, + { value: 'vllm', label: 'vLLM' } +]; + +const USE_CASE_OPTIONS = [ + { value: 'all', label: 'All use cases' }, + { value: 'general', label: 'General' }, + { value: 'coding', label: 'Coding' }, + { value: 'reasoning', label: 'Reasoning' }, + { value: 'chat', label: 'Chat' }, + { value: 'multimodal', label: 'Multimodal' }, + { value: 'embedding', label: 'Embedding' } +]; + +const LIMIT_OPTIONS = [ + { value: '10', label: '10' }, + { value: '20', label: '20' }, + { value: '50', label: '50' }, + { value: '100', label: '100' }, + { value: '200', label: '200' }, + { value: '', label: 'All' } +]; + +const SORT_OPTIONS = [ + { value: 'score', label: 'Sort: Score' }, + { value: 'tps', label: 'Sort: TPS' }, + { value: 'params', label: 'Sort: Params' }, + { value: 'mem', label: 'Sort: Memory' }, + { value: 'ctx', label: 'Sort: Context' }, + { value: 'date', label: 'Sort: Release date' }, + { value: 'use_case', label: 'Sort: Use case' } +]; + +function initialTheme() { + if (typeof window === 'undefined') return 'dark'; + const stored = window.localStorage.getItem(THEME_KEY); + if (stored === 'light' || stored === 'dark') return stored; + return window.matchMedia?.('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'; +} + +function round(value, digits = 1) { + if (typeof value !== 'number' || Number.isNaN(value)) return 'β€”'; + return value.toFixed(digits); +} + +function getFitBadgeProps(level) { + switch (level) { + case 'perfect': return { variant: 'outline', className: 'text-emerald-600 dark:text-emerald-400 border-emerald-500/30 bg-emerald-500/10 rounded-sm' }; + case 'good': return { variant: 'outline', className: 'text-sky-600 dark:text-sky-400 border-sky-500/30 bg-sky-500/10 rounded-sm' }; + case 'marginal': return { variant: 'outline', className: 'text-amber-600 dark:text-amber-400 border-amber-500/30 bg-amber-500/10 rounded-sm' }; + case 'too_tight': return { variant: 'outline', className: 'text-red-600 dark:text-red-400 border-red-500/30 bg-red-500/10 rounded-sm' }; + default: return { variant: 'outline', className: 'rounded-sm' }; + } +} + +function fitRank(level) { + switch (level) { + case 'perfect': return 3; + case 'good': return 2; + case 'marginal': return 1; + case 'too_tight': return 0; + default: return -1; + } +} + +function applyClientFitFilter(models, minFit) { + const list = Array.isArray(models) ? models : []; + if (minFit === 'all') return list; + if (minFit === 'too_tight') return list.filter((m) => m.fit_level === 'too_tight'); + const threshold = fitRank(minFit); + return list.filter((m) => fitRank(m.fit_level) >= threshold); +} + +function MetricBar({ label, value }) { + const safe = Number.isFinite(value) ? Math.max(0, Math.min(value, 100)) : 0; + return ( +
+
+ {label} + {round(value, 1)} +
+ +
+ ); +} + +export default function App() { + const [theme, setTheme] = useState(initialTheme); + const [filters, setFilters] = useState(DEFAULT_FILTERS); + const [systemState, setSystemState] = useState({ loading: true, error: '', payload: null }); + const [modelsState, setModelsState] = useState({ loading: true, error: '', models: [], total: 0, returned: 0 }); + const [selectedModelName, setSelectedModelName] = useState(null); + const [refreshTick, setRefreshTick] = useState(0); + + // Local state for search input to prevent lag (debouncing) + const [searchInput, setSearchInput] = useState(filters.search || ''); + + // Sync external filter resets back to local input + useEffect(() => { + if (filters.search === '') { + setSearchInput(''); + } + }, [filters.search]); + + // Debounce logic: only update actual filters after user stops typing + useEffect(() => { + const timer = setTimeout(() => { + setFilters((current) => current.search !== searchInput ? { ...current, search: searchInput } : current); + }, 300); + return () => clearTimeout(timer); + }, [searchInput]); + + useEffect(() => { + document.documentElement.classList.remove('light', 'dark'); + document.documentElement.classList.add(theme); + window.localStorage.setItem(THEME_KEY, theme); + }, [theme]); + + useEffect(() => { + const controller = new AbortController(); + async function loadSystem() { + setSystemState((prev) => ({ ...prev, loading: true, error: '' })); + try { + const payload = await fetchSystemInfo(controller.signal); + setSystemState({ loading: false, error: '', payload }); + } catch (error) { + if (!controller.signal.aborted) { + setSystemState({ loading: false, error: error.message || 'Unable to load system details.', payload: null }); + } + } + } + loadSystem(); + return () => controller.abort(); + }, [refreshTick]); + + useEffect(() => { + const controller = new AbortController(); + async function loadModels() { + setModelsState((prev) => ({ ...prev, loading: true, error: '' })); + try { + const payload = await fetchModels(filters, controller.signal); + const fetchedModels = Array.isArray(payload.models) ? payload.models : []; + const fitFiltered = applyClientFitFilter(fetchedModels, filters.minFit); + const limit = Number.parseInt(filters.limit, 10); + const models = Number.isFinite(limit) && limit > 0 ? fitFiltered.slice(0, limit) : fitFiltered; + const total = filters.minFit === 'too_tight' ? fitFiltered.length : (payload.total_models || fitFiltered.length); + + setModelsState({ loading: false, error: '', models, total, returned: models.length }); + setSelectedModelName((current) => { + if (!current || !models.some((m) => m.name === current)) return models[0]?.name ?? null; + return current; + }); + } catch (error) { + if (!controller.signal.aborted) { + setModelsState({ loading: false, error: error.message || 'Unable to load model fits.', models: [], total: 0, returned: 0 }); + setSelectedModelName(null); + } + } + } + loadModels(); + return () => controller.abort(); + }, [filters, refreshTick]); + + const selectedModel = useMemo( + () => modelsState.models.find((m) => m.name === selectedModelName) ?? null, + [modelsState.models, selectedModelName] + ); + + const handleFieldChange = (field, value) => { + setFilters((current) => ({ ...current, [field]: value })); + }; + + const gpus = systemState.payload?.system?.gpus ?? []; + const gpuSummary = gpus.length === 0 ? 'No GPU detected' : gpus.map((gpu) => `${gpu.name}${gpu.vram_gb ? ` (${round(gpu.vram_gb, 1)}VRAM)` : ''}`).join(', '); + + return ( +
+
+
+
+ +
+

LLMFIT EXPLORER

+

Hardware Interface

+
+
+
+ + + +
+
+
+ +
+ + {/* System Summary */} +
+
+ +

System Overview

+ {systemState.payload?.node && ( + + {systemState.payload.node.name} β€’ {systemState.payload.node.os} + + )} +
+
+ + + CPU + + + +
+ {systemState.payload?.system?.cpu_name ?? 'β€”'} +
+

+ {systemState.payload?.system?.cpu_cores ? `${systemState.payload.system.cpu_cores} cores` : 'β€”'} +

+ + + + + Total RAM + + + +
+ {systemState.payload?.system?.total_ram_gb ? `${round(systemState.payload.system.total_ram_gb, 1)} GB` : 'β€”'} +
+ + + + + Avail. RAM + + + +
+ {systemState.payload?.system?.available_ram_gb ? `${round(systemState.payload.system.available_ram_gb, 1)} GB` : 'β€”'} +
+ + + + + GPU + + + +
{gpuSummary}
+

+ {systemState.payload?.system?.unified_memory ? 'Unified Memory' : 'Discrete'} +

+ + +
+
+ + {/* Filters */} + + +
+ + setSearchInput(e.target.value)} + className="h-8 rounded-sm bg-background/50 border-muted-foreground/30 text-xs focus-visible:ring-primary/50" + /> +
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+ + + + {/* Main Workspace */} +
+
+
+

Models ({modelsState.returned} / {modelsState.total})

+
+
+ + + + Model + Params + Fit + Mode + Runtime + Score + TPS + Mem% + Context + + + + {modelsState.loading && ( + Loading fits... + )} + {!modelsState.loading && modelsState.models.length === 0 && ( + No matches found. + )} + {!modelsState.loading && modelsState.models.map((model) => ( + setSelectedModelName(model.name)} + > + +
+ {model.name} + {model.provider} +
+ + {round(model.params_b, 1)}B + {model.fit_label} + {model.run_mode} + {model.runtime_label} + {round(model.score, 1)} + {round(model.estimated_tps, 1)} + {round(model.utilization_pct, 1)}% + {model.context_length >= 1000 ? `${model.context_length/1000}k` : model.context_length} + + ))} + +
+
+
+ +
+
+ + {selectedModel ? ( + <> + +
+
+ {selectedModel.name} + {selectedModel.provider} +
+ {selectedModel.fit_label} +
+ + +
+
+ Runtime + {selectedModel.runtime_label} +
+
+ Quant + {selectedModel.best_quant} +
+
+ Required Mem + {round(selectedModel.memory_required_gb, 2)}G +
+
+ Available Mem + {round(selectedModel.memory_available_gb, 2)}G +
+
+ +
+

+ Score Analysis +

+
+ + + + +
+
+ +
+

Metrics

+
+ Utilization + {round(selectedModel.utilization_pct, 1)}% +
+
+ Context Size + {selectedModel.context_length?.toLocaleString() || 'β€”'} +
+
+ Final TPS + {round(selectedModel.estimated_tps, 1)} +
+
+ + {selectedModel.notes && selectedModel.notes.length > 0 && ( +
+

Notes

+
    + {selectedModel.notes.map((n, i) =>
  • {n}
    • )} +
+
+ )} + + + ) : ( +
+ Select a model to view details +
+ )} + +
+
+
+
+
+ ); +} diff --git a/llmfit-desktop/frontend/src/App.test.jsx b/llmfit-desktop/frontend/src/App.test.jsx new file mode 100644 index 000000000..6f8245eb4 --- /dev/null +++ b/llmfit-desktop/frontend/src/App.test.jsx @@ -0,0 +1,187 @@ +import { fireEvent, render, screen, waitFor } from '@testing-library/react'; +import { invoke } from '@tauri-apps/api/core'; +import App from './App'; + +vi.mock('@tauri-apps/api/core'); + +const systemPayload = { + system: { + cpu_name: 'Apple M3 Max', + cpu_cores: 14, + total_ram_gb: 64, + available_ram_gb: 51.4, + gpus: [{ name: 'Apple GPU', vram_gb: 64 }], + unified_memory: true + } +}; + +const modelsPayload = { + models: [ + { + name: 'Qwen/Qwen2.5-7B-Instruct', + provider: 'Qwen', + params_b: 7, + fit_level: 'good', + fit_label: 'Good', + run_mode: 'gpu', + run_mode_label: 'GPU', + runtime: 'llamacpp', + runtime_label: 'llama.cpp', + score: 86, + estimated_tps: 34.5, + utilization_pct: 58.9, + memory_required_gb: 7.4, + memory_available_gb: 12.5, + context_length: 32768, + best_quant: 'Q5_K_M', + release_date: '2025-02-01', + score_components: { + quality: 87, + speed: 80, + fit: 90, + context: 85 + }, + notes: ['Runs smoothly on most laptops'] + }, + { + name: 'meta-llama/Llama-3.1-8B-Instruct', + provider: 'Meta', + params_b: 8, + fit_level: 'marginal', + fit_label: 'Marginal', + run_mode: 'cpu_offload', + run_mode_label: 'CPU Offload', + runtime: 'llamacpp', + runtime_label: 'llama.cpp', + score: 74, + estimated_tps: 19.2, + utilization_pct: 87.5, + memory_required_gb: 10.1, + memory_available_gb: 11.5, + context_length: 8192, + best_quant: 'Q4_K_M', + release_date: '2024-11-10', + score_components: { + quality: 78, + speed: 66, + fit: 72, + context: 74 + }, + notes: [] + }, + { + name: 'LargeModel/220B-Preview', + provider: 'Example', + params_b: 220, + fit_level: 'too_tight', + fit_label: 'Too Tight', + run_mode: 'cpu_only', + run_mode_label: 'CPU Only', + runtime: 'llamacpp', + runtime_label: 'llama.cpp', + score: 44, + estimated_tps: 1.9, + utilization_pct: 165.2, + memory_required_gb: 92.4, + memory_available_gb: 56.0, + context_length: 32768, + best_quant: 'Q2_K', + release_date: '2025-01-02', + score_components: { + quality: 95, + speed: 8, + fit: 10, + context: 62 + }, + notes: ['Requires substantially more memory than this system'] + } + ] +}; + +function mockInvokeDefault() { + vi.mocked(invoke).mockImplementation((command) => { + if (command === 'get_system_specs') { + return Promise.resolve(systemPayload.system); + } + if (command === 'get_model_fits') { + return Promise.resolve(modelsPayload.models); + } + return Promise.reject(new Error(`Unknown command: ${command}`)); + }); +} + +describe('App', () => { + afterEach(() => { + vi.mocked(invoke).mockReset(); + window.localStorage.clear(); + }); + + it('renders models and refetches when sort changes', async () => { + mockInvokeDefault(); + + render(); + + await screen.findAllByText('Qwen/Qwen2.5-7B-Instruct'); + expect(screen.getByText('meta-llama/Llama-3.1-8B-Instruct')).toBeInTheDocument(); + + fireEvent.change(screen.getByLabelText('Sort'), { target: { value: 'tps' } }); + + await waitFor(() => { + const modelCalls = vi.mocked(invoke).mock.calls.filter(([cmd]) => cmd === 'get_model_fits'); + expect(modelCalls.length).toBeGreaterThanOrEqual(2); + }); + }); + + it('opens detail diagnostics when a model row is selected', async () => { + mockInvokeDefault(); + + render(); + + const modelCell = (await screen.findAllByText('Qwen/Qwen2.5-7B-Instruct'))[0]; + fireEvent.click(modelCell); + + expect(screen.getByText('Score Breakdown')).toBeInTheDocument(); + expect(screen.getByText('Runs smoothly on most laptops')).toBeInTheDocument(); + }); + + it('shows actionable error message when model fetch fails', async () => { + vi.mocked(invoke).mockImplementation((command) => { + if (command === 'get_system_specs') { + return Promise.resolve(systemPayload.system); + } + if (command === 'get_model_fits') { + return Promise.reject(new Error('backend unavailable')); + } + return Promise.reject(new Error(`Unknown command: ${command}`)); + }); + + render(); + + const alert = await screen.findByRole('alert'); + expect(alert).toHaveTextContent('backend unavailable'); + }); + + it('toggles between light and dark theme', async () => { + mockInvokeDefault(); + + render(); + + const toggle = await screen.findByRole('button', { name: 'Dark mode' }); + fireEvent.click(toggle); + + expect(await screen.findByRole('button', { name: 'Light mode' })).toBeInTheDocument(); + expect(document.documentElement.dataset.theme).toBe('dark'); + }); + + it('can filter to too-tight only', async () => { + mockInvokeDefault(); + + render(); + + await screen.findAllByText('Qwen/Qwen2.5-7B-Instruct'); + fireEvent.change(screen.getByLabelText('Fit filter'), { target: { value: 'too_tight' } }); + + expect(await screen.findAllByText('LargeModel/220B-Preview')).not.toHaveLength(0); + expect(screen.queryAllByText('Qwen/Qwen2.5-7B-Instruct')).toHaveLength(0); + }); +}); diff --git a/llmfit-desktop/frontend/src/api.js b/llmfit-desktop/frontend/src/api.js new file mode 100644 index 000000000..0b3c29b8b --- /dev/null +++ b/llmfit-desktop/frontend/src/api.js @@ -0,0 +1,97 @@ +import { invoke } from '@tauri-apps/api/core'; + +export const DEFAULT_FILTERS = { + search: '', + minFit: 'marginal', + runtime: 'any', + useCase: 'all', + provider: '', + sort: 'score', + limit: '50' +}; + +export async function fetchSystemInfo(signal) { + const specs = await invoke('get_system_specs'); + return { + node: { name: 'desktop', os: getOs() }, + system: specs + }; +} + +export async function fetchModels(filters, signal) { + const allFits = await invoke('get_model_fits'); + const fetchedModels = Array.isArray(allFits) ? allFits : []; + const fitFiltered = applyClientFitFilter(fetchedModels, filters.minFit); + const limit = Number.parseInt(filters.limit, 10); + const models = Number.isFinite(limit) && limit > 0 ? fitFiltered.slice(0, limit) : fitFiltered; + const filtered = applyFilters(models, filters); + + return { + node: { name: 'desktop', os: getOs() }, + system: await invoke('get_system_specs'), + total_models: fitFiltered.length, + returned_models: filtered.length, + filters: filters, + models: filtered + }; +} + +function getOs() { + if (typeof navigator !== 'undefined') { + const ua = navigator.userAgent; + if (ua.includes('Mac')) return 'macOS'; + if (ua.includes('Win')) return 'Windows'; + if (ua.includes('Linux')) return 'Linux'; + return ua; + } + return 'unknown'; +} + +function fitRank(level) { + switch (level) { + case 'perfect': return 3; + case 'good': return 2; + case 'marginal': return 1; + case 'too_tight': return 0; + default: return -1; + } +} + +export function applyClientFitFilter(models, minFit) { + const list = Array.isArray(models) ? models : []; + if (minFit === 'all') return list; + if (minFit === 'too_tight') return list.filter(m => m.fit_level === 'too_tight'); + const threshold = fitRank(minFit); + return list.filter(model => fitRank(model.fit_level) >= threshold); +} + +export function applyFilters(fits, filters) { + let result = fits; + + if (filters.search) { + const s = filters.search.toLowerCase(); + result = result.filter(f => + (f.name || '').toLowerCase().includes(s) || + (f.provider || '').toLowerCase().includes(s) || + (f.parameter_count || '').toLowerCase().includes(s) || + (f.use_case || '').toLowerCase().includes(s) || + (f.category || '').toLowerCase().includes(s) + ); + } + + if (filters.runtime && filters.runtime !== 'any') { + result = result.filter(f => f.runtime === filters.runtime); + } + + if (filters.useCase && filters.useCase !== 'all') { + const uc = filters.useCase.toLowerCase(); + result = result.filter(f => (f.use_case || '').toLowerCase() === uc); + } + + if (filters.provider) { + const p = filters.provider.toLowerCase(); + result = result.filter(f => (f.provider || '').toLowerCase().includes(p)); + } + + return result; +} diff --git a/llmfit-desktop/frontend/src/api.test.js b/llmfit-desktop/frontend/src/api.test.js new file mode 100644 index 000000000..ff56872ab --- /dev/null +++ b/llmfit-desktop/frontend/src/api.test.js @@ -0,0 +1,181 @@ +import { describe, it, expect, vi } from 'vitest'; +import { fetchModels, fetchSystemInfo, DEFAULT_FILTERS, applyClientFitFilter, applyFilters } from './api'; + +vi.mock('@tauri-apps/api/core', () => ({ + invoke: vi.fn() +})); + +const { invoke } = await import('@tauri-apps/api/core'); + +const sampleModels = [ + { name: 'llama-3-8b', provider: 'Meta', parameter_count: '8B', fit_level: 'perfect', runtime: 'llamacpp', use_case: 'chat', category: 'llm' }, + { name: 'qwen-2-7b', provider: 'Qwen', parameter_count: '7B', fit_level: 'good', runtime: 'llamacpp', use_case: 'coding', category: 'llm' }, + { name: 'mistral-7b', provider: 'Mistral', parameter_count: '7B', fit_level: 'marginal', runtime: 'llamacpp', use_case: 'chat', category: 'llm' }, + { name: 'llama-3-70b', provider: 'Meta', parameter_count: '70B', fit_level: 'too_tight', runtime: 'llamacpp', use_case: 'chat', category: 'llm' }, + { name: 'codellama-13b', provider: 'Meta', parameter_count: '13B', fit_level: 'good', runtime: 'ollama', use_case: 'coding', category: 'code' }, +]; + +describe('applyClientFitFilter', () => { + it('returns all models when minFit is "all"', () => { + const result = applyClientFitFilter(sampleModels, 'all'); + expect(result).toHaveLength(5); + }); + + it('filters to only perfect models when minFit is "perfect"', () => { + const result = applyClientFitFilter(sampleModels, 'perfect'); + expect(result).toHaveLength(1); + expect(result[0].name).toBe('llama-3-8b'); + }); + + it('filters to perfect and good when minFit is "good"', () => { + const result = applyClientFitFilter(sampleModels, 'good'); + expect(result).toHaveLength(3); + expect(result.map(m => m.name)).toContain('llama-3-8b'); + expect(result.map(m => m.name)).toContain('qwen-2-7b'); + expect(result.map(m => m.name)).toContain('codellama-13b'); + }); + + it('filters to perfect, good, and marginal when minFit is "marginal"', () => { + const result = applyClientFitFilter(sampleModels, 'marginal'); + expect(result).toHaveLength(4); + expect(result.every(m => m.fit_level !== 'too_tight')).toBe(true); + }); + + it('returns only too_tight models when minFit is "too_tight"', () => { + const result = applyClientFitFilter(sampleModels, 'too_tight'); + expect(result).toHaveLength(1); + expect(result[0].name).toBe('llama-3-70b'); + }); + + it('handles non-array input gracefully', () => { + expect(applyClientFitFilter(null, 'all')).toEqual([]); + expect(applyClientFitFilter(undefined, 'good')).toEqual([]); + }); +}); + +describe('applyFilters', () => { + it('returns all models with default filters', () => { + const result = applyFilters(sampleModels, DEFAULT_FILTERS); + expect(result).toHaveLength(5); + }); + + it('filters by search term matching name', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, search: 'llama' }); + expect(result).toHaveLength(3); + expect(result.map(m => m.name)).toContain('llama-3-8b'); + expect(result.map(m => m.name)).toContain('llama-3-70b'); + expect(result.map(m => m.name)).toContain('codellama-13b'); + }); + + it('filters by search term matching provider', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, search: 'qwen' }); + expect(result).toHaveLength(1); + expect(result[0].name).toBe('qwen-2-7b'); + }); + + it('filters by search term matching parameter count', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, search: '70B' }); + expect(result).toHaveLength(1); + expect(result[0].name).toBe('llama-3-70b'); + }); + + it('filters by search term matching use case', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, search: 'coding' }); + expect(result).toHaveLength(2); + expect(result.map(m => m.name)).toContain('qwen-2-7b'); + expect(result.map(m => m.name)).toContain('codellama-13b'); + }); + + it('filters by runtime', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, runtime: 'ollama' }); + expect(result).toHaveLength(1); + expect(result[0].name).toBe('codellama-13b'); + }); + + it('does not filter when runtime is "any"', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, runtime: 'any' }); + expect(result).toHaveLength(5); + }); + + it('filters by use case', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, useCase: 'coding' }); + expect(result).toHaveLength(2); + }); + + it('does not filter when useCase is "all"', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, useCase: 'all' }); + expect(result).toHaveLength(5); + }); + + it('filters by provider', () => { + const result = applyFilters(sampleModels, { ...DEFAULT_FILTERS, provider: 'Meta' }); + expect(result).toHaveLength(3); + expect(result.every(m => m.provider === 'Meta')).toBe(true); + }); + + it('applies multiple filters together', () => { + const result = applyFilters(sampleModels, { + ...DEFAULT_FILTERS, + search: 'llama', + runtime: 'llamacpp', + useCase: 'chat' + }); + expect(result).toHaveLength(2); + expect(result.map(m => m.name)).toContain('llama-3-8b'); + expect(result.map(m => m.name)).toContain('llama-3-70b'); + }); +}); + +describe('fetchModels', () => { + it('fetches, filters by fit level, applies limit, and filters', async () => { + invoke.mockImplementation((cmd) => { + if (cmd === 'get_model_fits') return Promise.resolve(sampleModels); + if (cmd === 'get_system_specs') return Promise.resolve({ ram_gb: 32, vram_gb: 16 }); + return Promise.resolve(null); + }); + + const result = await fetchModels({ + ...DEFAULT_FILTERS, + minFit: 'good', + limit: '2', + runtime: 'llamacpp' + }); + + expect(result.total_models).toBe(3); + expect(result.returned_models).toBe(2); + expect(result.models).toHaveLength(2); + expect(result.models.every(m => ['perfect', 'good'].includes(m.fit_level))).toBe(true); + expect(result.models.every(m => m.runtime === 'llamacpp')).toBe(true); + }); + + it('handles empty model list from invoke', async () => { + invoke.mockImplementation((cmd) => { + if (cmd === 'get_model_fits') return Promise.resolve([]); + if (cmd === 'get_system_specs') return Promise.resolve({}); + return Promise.resolve(null); + }); + + const result = await fetchModels(DEFAULT_FILTERS); + expect(result.models).toEqual([]); + expect(result.total_models).toBe(0); + }); +}); + +describe('fetchSystemInfo', () => { + it('returns system specs with node info', async () => { + invoke.mockResolvedValue({ ram_gb: 64, vram_gb: 24, cpu_cores: 16 }); + const result = await fetchSystemInfo(); + expect(result.node.name).toBe('desktop'); + expect(result.system.ram_gb).toBe(64); + }); +}); + +describe('DEFAULT_FILTERS', () => { + it('has expected default values', () => { + expect(DEFAULT_FILTERS.minFit).toBe('marginal'); + expect(DEFAULT_FILTERS.runtime).toBe('any'); + expect(DEFAULT_FILTERS.useCase).toBe('all'); + expect(DEFAULT_FILTERS.sort).toBe('score'); + expect(DEFAULT_FILTERS.limit).toBe('50'); + }); +}); diff --git a/llmfit-desktop/frontend/src/components/ui/badge.tsx b/llmfit-desktop/frontend/src/components/ui/badge.tsx new file mode 100644 index 000000000..e87d62bf1 --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/badge.tsx @@ -0,0 +1,36 @@ +import * as React from "react" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const badgeVariants = cva( + "inline-flex items-center rounded-md border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2", + { + variants: { + variant: { + default: + "border-transparent bg-primary text-primary-foreground shadow hover:bg-primary/80", + secondary: + "border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80", + destructive: + "border-transparent bg-destructive text-destructive-foreground shadow hover:bg-destructive/80", + outline: "text-foreground", + }, + }, + defaultVariants: { + variant: "default", + }, + } +) + +export interface BadgeProps + extends React.HTMLAttributes, + VariantProps {} + +function Badge({ className, variant, ...props }: BadgeProps) { + return ( +
+ ) +} + +export { Badge, badgeVariants } diff --git a/llmfit-desktop/frontend/src/components/ui/button.tsx b/llmfit-desktop/frontend/src/components/ui/button.tsx new file mode 100644 index 000000000..65d4fcd9c --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/button.tsx @@ -0,0 +1,57 @@ +import * as React from "react" +import { Slot } from "@radix-ui/react-slot" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const buttonVariants = cva( + "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0", + { + variants: { + variant: { + default: + "bg-primary text-primary-foreground shadow hover:bg-primary/90", + destructive: + "bg-destructive text-destructive-foreground shadow-sm hover:bg-destructive/90", + outline: + "border border-input bg-background shadow-sm hover:bg-accent hover:text-accent-foreground", + secondary: + "bg-secondary text-secondary-foreground shadow-sm hover:bg-secondary/80", + ghost: "hover:bg-accent hover:text-accent-foreground", + link: "text-primary underline-offset-4 hover:underline", + }, + size: { + default: "h-9 px-4 py-2", + sm: "h-8 rounded-md px-3 text-xs", + lg: "h-10 rounded-md px-8", + icon: "h-9 w-9", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, + } +) + +export interface ButtonProps + extends React.ButtonHTMLAttributes, + VariantProps { + asChild?: boolean +} + +const Button = React.forwardRef( + ({ className, variant, size, asChild = false, ...props }, ref) => { + const Comp = asChild ? Slot : "button" + return ( + + ) + } +) +Button.displayName = "Button" + +export { Button, buttonVariants } diff --git a/llmfit-desktop/frontend/src/components/ui/card.tsx b/llmfit-desktop/frontend/src/components/ui/card.tsx new file mode 100644 index 000000000..cabfbfc59 --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/card.tsx @@ -0,0 +1,76 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +const Card = React.forwardRef< + HTMLDivElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+)) +Card.displayName = "Card" + +const CardHeader = React.forwardRef< + HTMLDivElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+)) +CardHeader.displayName = "CardHeader" + +const CardTitle = React.forwardRef< + HTMLDivElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+)) +CardTitle.displayName = "CardTitle" + +const CardDescription = React.forwardRef< + HTMLDivElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+)) +CardDescription.displayName = "CardDescription" + +const CardContent = React.forwardRef< + HTMLDivElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+)) +CardContent.displayName = "CardContent" + +const CardFooter = React.forwardRef< + HTMLDivElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+)) +CardFooter.displayName = "CardFooter" + +export { Card, CardHeader, CardFooter, CardTitle, CardDescription, CardContent } diff --git a/llmfit-desktop/frontend/src/components/ui/input.tsx b/llmfit-desktop/frontend/src/components/ui/input.tsx new file mode 100644 index 000000000..69b64fb24 --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/input.tsx @@ -0,0 +1,22 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +const Input = React.forwardRef>( + ({ className, type, ...props }, ref) => { + return ( + + ) + } +) +Input.displayName = "Input" + +export { Input } diff --git a/llmfit-desktop/frontend/src/components/ui/label.tsx b/llmfit-desktop/frontend/src/components/ui/label.tsx new file mode 100644 index 000000000..683faa793 --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/label.tsx @@ -0,0 +1,24 @@ +import * as React from "react" +import * as LabelPrimitive from "@radix-ui/react-label" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const labelVariants = cva( + "text-sm font-medium leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70" +) + +const Label = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef & + VariantProps +>(({ className, ...props }, ref) => ( + +)) +Label.displayName = LabelPrimitive.Root.displayName + +export { Label } diff --git a/llmfit-desktop/frontend/src/components/ui/progress.tsx b/llmfit-desktop/frontend/src/components/ui/progress.tsx new file mode 100644 index 000000000..3fd47adad --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/progress.tsx @@ -0,0 +1,26 @@ +import * as React from "react" +import * as ProgressPrimitive from "@radix-ui/react-progress" + +import { cn } from "@/lib/utils" + +const Progress = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, value, ...props }, ref) => ( + + + +)) +Progress.displayName = ProgressPrimitive.Root.displayName + +export { Progress } diff --git a/llmfit-desktop/frontend/src/components/ui/select.tsx b/llmfit-desktop/frontend/src/components/ui/select.tsx new file mode 100644 index 000000000..6e637f7dc --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/select.tsx @@ -0,0 +1,159 @@ +"use client" + +import * as React from "react" +import * as SelectPrimitive from "@radix-ui/react-select" +import { Check, ChevronDown, ChevronUp } from "lucide-react" + +import { cn } from "@/lib/utils" + +const Select = SelectPrimitive.Root + +const SelectGroup = SelectPrimitive.Group + +const SelectValue = SelectPrimitive.Value + +const SelectTrigger = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, ...props }, ref) => ( + span]:line-clamp-1", + className + )} + {...props} + > + {children} + + + + +)) +SelectTrigger.displayName = SelectPrimitive.Trigger.displayName + +const SelectScrollUpButton = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + + + +)) +SelectScrollUpButton.displayName = SelectPrimitive.ScrollUpButton.displayName + +const SelectScrollDownButton = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + + + +)) +SelectScrollDownButton.displayName = + SelectPrimitive.ScrollDownButton.displayName + +const SelectContent = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, position = "popper", ...props }, ref) => ( + + + + + {children} + + + + +)) +SelectContent.displayName = SelectPrimitive.Content.displayName + +const SelectLabel = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +SelectLabel.displayName = SelectPrimitive.Label.displayName + +const SelectItem = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, ...props }, ref) => ( + + + + + + + {children} + +)) +SelectItem.displayName = SelectPrimitive.Item.displayName + +const SelectSeparator = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +SelectSeparator.displayName = SelectPrimitive.Separator.displayName + +export { + Select, + SelectGroup, + SelectValue, + SelectTrigger, + SelectContent, + SelectLabel, + SelectItem, + SelectSeparator, + SelectScrollUpButton, + SelectScrollDownButton, +} diff --git a/llmfit-desktop/frontend/src/components/ui/table.tsx b/llmfit-desktop/frontend/src/components/ui/table.tsx new file mode 100644 index 000000000..c0df655c0 --- /dev/null +++ b/llmfit-desktop/frontend/src/components/ui/table.tsx @@ -0,0 +1,120 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +const Table = React.forwardRef< + HTMLTableElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+ + +)) +Table.displayName = "Table" + +const TableHeader = React.forwardRef< + HTMLTableSectionElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( + +)) +TableHeader.displayName = "TableHeader" + +const TableBody = React.forwardRef< + HTMLTableSectionElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( + +)) +TableBody.displayName = "TableBody" + +const TableFooter = React.forwardRef< + HTMLTableSectionElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( + tr]:last:border-b-0", + className + )} + {...props} + /> +)) +TableFooter.displayName = "TableFooter" + +const TableRow = React.forwardRef< + HTMLTableRowElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( + +)) +TableRow.displayName = "TableRow" + +const TableHead = React.forwardRef< + HTMLTableCellElement, + React.ThHTMLAttributes +>(({ className, ...props }, ref) => ( + '; - return; - } - tbody.innerHTML = fits.map((f, i) => ` - - - - - - - - - - - - `).join(''); - - // Attach click handlers - const currentFits = fits; - tbody.querySelectorAll('.model-row').forEach(row => { - row.addEventListener('click', () => { - const idx = parseInt(row.dataset.index, 10); - showModal(currentFits[idx]); - }); - }); -} - -function applyFilters() { - const search = document.getElementById('search').value.toLowerCase(); - const fitFilter = document.getElementById('fit-filter').value; - - let filtered = allFits; - if (search) { - filtered = filtered.filter(f => f.name.toLowerCase().includes(search)); - } - if (fitFilter !== 'all') { - filtered = filtered.filter(f => f.fit_level === fitFilter); - } - renderModels(filtered); -} - -async function loadModels() { - try { - allFits = await invoke('get_model_fits') || []; - applyFilters(); - } catch (e) { - console.error('Failed to load models:', e); - document.getElementById('models-body').innerHTML = - ''; - } -} - -// Close modal on backdrop click -document.getElementById('model-modal').addEventListener('click', (e) => { - if (e.target === e.currentTarget) closeModal(); -}); - -// Close modal on Escape -document.addEventListener('keydown', (e) => { - if (e.key === 'Escape') closeModal(); -}); - -document.getElementById('search').addEventListener('input', applyFilters); -document.getElementById('fit-filter').addEventListener('change', applyFilters); - -async function init() { - ollamaAvailable = await invoke('is_ollama_available') || false; - loadSpecs(); - loadModels(); -} - -init(); diff --git a/llmfit-desktop/ui/index.html b/llmfit-desktop/ui/index.html deleted file mode 100644 index 77cba7bb3..000000000 --- a/llmfit-desktop/ui/index.html +++ /dev/null @@ -1,76 +0,0 @@ - - - - - - llmfit - - - -
-

System

-
-
- CPU - Detecting... - -
-
- Total RAM - β€” -
-
- Available RAM - β€” -
-
- -
- -
-
- -
-

Model Compatibility

-
- - -
-
-
[role=checkbox]]:translate-y-[2px]", + className + )} + {...props} + /> +)) +TableHead.displayName = "TableHead" + +const TableCell = React.forwardRef< + HTMLTableCellElement, + React.TdHTMLAttributes +>(({ className, ...props }, ref) => ( + [role=checkbox]]:translate-y-[2px]", + className + )} + {...props} + /> +)) +TableCell.displayName = "TableCell" + +const TableCaption = React.forwardRef< + HTMLTableCaptionElement, + React.HTMLAttributes +>(({ className, ...props }, ref) => ( +
+)) +TableCaption.displayName = "TableCaption" + +export { + Table, + TableHeader, + TableBody, + TableFooter, + TableHead, + TableRow, + TableCell, + TableCaption, +} diff --git a/llmfit-desktop/frontend/src/index.css b/llmfit-desktop/frontend/src/index.css new file mode 100644 index 000000000..06bff0954 --- /dev/null +++ b/llmfit-desktop/frontend/src/index.css @@ -0,0 +1,70 @@ +@import url('https://fonts.googleapis.com/css2?family=Chivo:ital,wght@0,100..900;1,100..900&family=JetBrains+Mono:wght@400;500;700;800&display=swap'); +@tailwind base; +@tailwind components; +@tailwind utilities; + +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 240 10% 3.9%; + --card: 0 0% 100%; + --card-foreground: 240 10% 3.9%; + --popover: 0 0% 100%; + --popover-foreground: 240 10% 3.9%; + --primary: 24.6 95% 53.1%; /* Orange 500 */ + --primary-foreground: 0 0% 100%; + --secondary: 240 4.8% 95.9%; + --secondary-foreground: 240 5.9% 10%; + --muted: 240 4.8% 95.9%; + --muted-foreground: 240 3.8% 46.1%; + --accent: 240 4.8% 95.9%; + --accent-foreground: 240 5.9% 10%; + --destructive: 0 84.2% 60.2%; + --destructive-foreground: 0 0% 98%; + --border: 240 5.9% 90%; + --input: 240 5.9% 90%; + --ring: 24.6 95% 53.1%; + --chart-1: 24.6 95% 53.1%; + --chart-2: 173 58% 39%; + --chart-3: 197 37% 24%; + --chart-4: 43 74% 66%; + --chart-5: 27 87% 67%; + --radius: 0.3rem; /* Daha keskin kâşe (sm veya md arası endüstriyel) */ + } + .dark { + --background: 240 10% 3.9%; /* Zinc 950 */ + --foreground: 0 0% 98%; /* Zinc 50 */ + --card: 240 5.9% 10%; /* Zinc 900 */ + --card-foreground: 0 0% 98%; + --popover: 240 5.9% 10%; + --popover-foreground: 0 0% 98%; + --primary: 37.7 92.1% 50.2%; /* Amber 500 */ + --primary-foreground: 240 5.9% 10%; + --secondary: 240 3.7% 15.9%; /* Zinc 800 */ + --secondary-foreground: 0 0% 98%; + --muted: 240 3.7% 15.9%; + --muted-foreground: 240 5% 64.9%; /* Zinc 400 */ + --accent: 240 3.7% 15.9%; + --accent-foreground: 0 0% 98%; + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 0 0% 98%; + --border: 240 3.7% 15.9%; + --input: 240 3.7% 15.9%; + --ring: 37.7 92.1% 50.2%; + --chart-1: 37.7 92.1% 50.2%; + --chart-2: 160 60% 45%; + --chart-3: 30 80% 55%; + --chart-4: 280 65% 60%; + --chart-5: 340 75% 55%; + } +} + +@layer base { + * { + @apply border-border; + } + body { + @apply bg-background text-foreground antialiased selection:bg-primary/30; + font-feature-settings: "cv02", "cv03", "cv04", "cv11"; + } +} diff --git a/llmfit-desktop/frontend/src/lib/utils.js b/llmfit-desktop/frontend/src/lib/utils.js new file mode 100644 index 000000000..20aa6031c --- /dev/null +++ b/llmfit-desktop/frontend/src/lib/utils.js @@ -0,0 +1,6 @@ +import { clsx } from "clsx" +import { twMerge } from "tailwind-merge" + +export function cn(...inputs) { + return twMerge(clsx(inputs)) +} diff --git a/llmfit-desktop/frontend/src/main.jsx b/llmfit-desktop/frontend/src/main.jsx new file mode 100644 index 000000000..47742c1b1 --- /dev/null +++ b/llmfit-desktop/frontend/src/main.jsx @@ -0,0 +1,10 @@ +import React from 'react'; +import ReactDOM from 'react-dom/client'; +import App from './App'; +import './index.css'; + +ReactDOM.createRoot(document.getElementById('root')).render( + + + +); diff --git a/llmfit-desktop/frontend/src/test-setup.js b/llmfit-desktop/frontend/src/test-setup.js new file mode 100644 index 000000000..bb02c60cd --- /dev/null +++ b/llmfit-desktop/frontend/src/test-setup.js @@ -0,0 +1 @@ +import '@testing-library/jest-dom/vitest'; diff --git a/llmfit-desktop/frontend/tailwind.config.js b/llmfit-desktop/frontend/tailwind.config.js new file mode 100644 index 000000000..1664df616 --- /dev/null +++ b/llmfit-desktop/frontend/tailwind.config.js @@ -0,0 +1,61 @@ +/** @type {import('tailwindcss').Config} */ +export default { + darkMode: ["class"], + content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"], + theme: { + fontFamily: { + sans: ['Chivo', 'sans-serif'], + mono: ['"JetBrains Mono"', 'monospace'], + }, + extend: { + colors: { + background: 'hsl(var(--background))', + foreground: 'hsl(var(--foreground))', + card: { + DEFAULT: 'hsl(var(--card))', + foreground: 'hsl(var(--card-foreground))' + }, + popover: { + DEFAULT: 'hsl(var(--popover))', + foreground: 'hsl(var(--popover-foreground))' + }, + primary: { + DEFAULT: 'hsl(var(--primary))', + foreground: 'hsl(var(--primary-foreground))' + }, + secondary: { + DEFAULT: 'hsl(var(--secondary))', + foreground: 'hsl(var(--secondary-foreground))' + }, + muted: { + DEFAULT: 'hsl(var(--muted))', + foreground: 'hsl(var(--muted-foreground))' + }, + accent: { + DEFAULT: 'hsl(var(--accent))', + foreground: 'hsl(var(--accent-foreground))' + }, + destructive: { + DEFAULT: 'hsl(var(--destructive))', + foreground: 'hsl(var(--destructive-foreground))' + }, + border: 'hsl(var(--border))', + input: 'hsl(var(--input))', + ring: 'hsl(var(--ring))', + chart: { + '1': 'hsl(var(--chart-1))', + '2': 'hsl(var(--chart-2))', + '3': 'hsl(var(--chart-3))', + '4': 'hsl(var(--chart-4))', + '5': 'hsl(var(--chart-5))' + } + }, + borderRadius: { + lg: 'var(--radius)', + md: 'calc(var(--radius) - 2px)', + sm: 'calc(var(--radius) - 4px)' + } + } + }, + plugins: [require("tailwindcss-animate")], +} diff --git a/llmfit-desktop/frontend/vite.config.js b/llmfit-desktop/frontend/vite.config.js new file mode 100644 index 000000000..6d2f8edb5 --- /dev/null +++ b/llmfit-desktop/frontend/vite.config.js @@ -0,0 +1,30 @@ +import { defineConfig } from 'vite'; +import react from '@vitejs/plugin-react'; +import path from "path"; + +export default defineConfig({ + plugins: [react()], + clearScreen: false, + resolve: { + alias: { + "@": path.resolve(__dirname, "./src"), + }, + }, + server: { + port: 1420, + strictPort: true, + watch: { + ignored: ['**/src-tauri/**'], + }, + }, + build: { + outDir: 'dist', + target: 'esnext', + minify: 'esbuild', + }, + test: { + globals: true, + environment: 'jsdom', + setupFiles: ['./src/test-setup.js'], + }, +}); diff --git 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-0,0 +1,16 @@ +{ + "name": "llmfit-desktop", + "version": "1.0.0", + "description": "", + "main": "index.js", + "devDependencies": { + "@tauri-apps/cli": "^2.10.1" + }, + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "keywords": [], + "author": "", + "license": "ISC", + "type": "commonjs" +} diff --git a/llmfit-desktop/src/main.rs b/llmfit-desktop/src/main.rs index e1e8c819f..d0ed3a9b6 100644 --- a/llmfit-desktop/src/main.rs +++ b/llmfit-desktop/src/main.rs @@ -25,25 +25,51 @@ struct SystemInfo { cpu_cores: usize, gpus: Vec, unified_memory: bool, + backend: String, + has_gpu: bool, + gpu_vram_gb: Option, +} + +#[derive(Serialize, Clone)] +struct ScoreComponents { + quality: f64, + speed: f64, + fit: f64, + context: f64, } #[derive(Serialize, Clone)] struct ModelFitInfo { name: String, + provider: String, + parameter_count: String, params_b: f64, - quant: String, + context_length: u32, + use_case: String, + category: String, + release_date: Option, + is_moe: bool, + fit_level: String, + fit_label: String, run_mode: String, + run_mode_label: String, + score: f64, + score_components: ScoreComponents, + estimated_tps: f64, + + runtime: String, + runtime_label: String, + best_quant: String, + memory_required_gb: f64, memory_available_gb: f64, utilization_pct: f64, - estimated_tps: f64, - use_case: String, - runtime: String, + installed: bool, notes: Vec, - release_date: Option, + gguf_sources: Vec, } #[derive(Serialize)] @@ -68,7 +94,7 @@ fn get_system_specs() -> Result { .map(|g| GpuInfoJs { name: g.name.clone(), vram_gb: g.vram_gb, - backend: format!("{:?}", g.backend), + backend: g.backend.label().to_string(), count: g.count, unified_memory: g.unified_memory, }) @@ -80,6 +106,9 @@ fn get_system_specs() -> Result { cpu_cores: specs.total_cpu_cores, gpus, unified_memory: specs.unified_memory, + backend: specs.backend.label().to_string(), + has_gpu: specs.has_gpu, + gpu_vram_gb: specs.gpu_vram_gb, }) } @@ -100,33 +129,54 @@ fn get_model_fits() -> Result, String> { .into_iter() .map(|f| ModelFitInfo { name: f.model.name.clone(), + provider: f.model.provider.clone(), + parameter_count: f.model.parameter_count.clone(), params_b: f.model.parameters_raw.unwrap_or(0) as f64 / 1e9, - quant: f.best_quant.clone(), + context_length: f.model.context_length, + use_case: format!("{:?}", f.use_case), + category: f.use_case.label().to_string(), + release_date: f.model.release_date.clone(), + is_moe: f.model.is_moe, fit_level: match f.fit_level { - FitLevel::Perfect => "Perfect".to_string(), - FitLevel::Good => "Good".to_string(), - FitLevel::Marginal => "Marginal".to_string(), - FitLevel::TooTight => "Too Tight".to_string(), + FitLevel::Perfect => "perfect".to_string(), + FitLevel::Good => "good".to_string(), + FitLevel::Marginal => "marginal".to_string(), + FitLevel::TooTight => "too_tight".to_string(), }, + fit_label: f.fit_text().to_string(), run_mode: match f.run_mode { - RunMode::Gpu => "GPU".to_string(), - RunMode::CpuOffload => "CPU Offload".to_string(), - RunMode::CpuOnly => "CPU Only".to_string(), - RunMode::MoeOffload => "MoE Offload".to_string(), + RunMode::Gpu => "gpu".to_string(), + RunMode::CpuOffload => "cpu_offload".to_string(), + RunMode::CpuOnly => "cpu_only".to_string(), + RunMode::MoeOffload => "moe_offload".to_string(), }, + run_mode_label: f.run_mode_text().to_string(), score: f.score, - memory_required_gb: f.memory_required_gb, - memory_available_gb: f.memory_available_gb, - utilization_pct: f.utilization_pct, + score_components: ScoreComponents { + quality: f.score_components.quality, + speed: f.score_components.speed, + fit: f.score_components.fit, + context: f.score_components.context, + }, estimated_tps: f.estimated_tps, - use_case: format!("{:?}", f.use_case), runtime: match f.runtime { - InferenceRuntime::LlamaCpp => "llama.cpp".to_string(), - InferenceRuntime::Mlx => "MLX".to_string(), + InferenceRuntime::LlamaCpp => "llamacpp".to_string(), + InferenceRuntime::Mlx => "mlx".to_string(), + InferenceRuntime::Vllm => "vllm".to_string(), }, + runtime_label: f.runtime_text().to_string(), + best_quant: f.best_quant.clone(), + memory_required_gb: f.memory_required_gb, + memory_available_gb: f.memory_available_gb, + utilization_pct: f.utilization_pct, installed: f.installed, notes: f.notes.clone(), - release_date: f.model.release_date.clone(), + gguf_sources: f + .model + .gguf_sources + .iter() + .map(|s| s.repo.clone()) + .collect(), }) .collect()) } diff --git a/llmfit-desktop/tauri.conf.json b/llmfit-desktop/tauri.conf.json index e9a2c00b2..4321cc3e6 100644 --- a/llmfit-desktop/tauri.conf.json +++ b/llmfit-desktop/tauri.conf.json @@ -1,22 +1,40 @@ { "productName": "llmfit", - "version": "0.4.8", + "version": "0.8.0", "identifier": "com.llmfit.desktop", "build": { - "frontendDist": "./ui" + "frontendDist": "./frontend/dist", + "devUrl": "http://localhost:1420", + "beforeDevCommand": "npm --prefix frontend run dev", + "beforeBuildCommand": "npm --prefix frontend run build" }, "app": { "withGlobalTauri": true, "windows": [ { "title": "llmfit β€” LLM Hardware Fitting", - "width": 1200, - "height": 800, - "resizable": true + "width": 1280, + "height": 840, + "minWidth": 900, + "minHeight": 600, + "resizable": true, + "center": true } ], "security": { - "csp": "default-src 'self'; style-src 'self'; script-src 'self'" + "csp": "default-src 'self'; style-src 'self' 'unsafe-inline'; script-src 'self'" } + }, + "bundle": { + "active": true, + "targets": "all", + "icon": [ + "icons/icon.ico", + "icons/icon.png", + "icons/32x32.png", + "icons/128x128.png", + "icons/128x128@2x.png", + "icons/256x256.png" + ] } } diff --git a/llmfit-desktop/ui/app.js b/llmfit-desktop/ui/app.js deleted file mode 100644 index edcd25c2a..000000000 --- a/llmfit-desktop/ui/app.js +++ /dev/null @@ -1,293 +0,0 @@ -const invoke = window.__TAURI_INTERNALS__ - ? window.__TAURI_INTERNALS__.invoke - : async (cmd) => { console.warn('Tauri not available, cmd:', cmd); return null; }; - -let allFits = []; -let ollamaAvailable = false; -let pullInterval = null; - -function esc(s) { - const d = document.createElement('div'); - d.textContent = s; - return d.innerHTML; -} - -async function loadSpecs() { - try { - const specs = await invoke('get_system_specs'); - if (!specs) return; - - document.getElementById('cpu-name').textContent = specs.cpu_name; - document.getElementById('cpu-cores').textContent = specs.cpu_cores + ' cores'; - document.getElementById('ram-total').textContent = specs.total_ram_gb.toFixed(1) + ' GB'; - document.getElementById('ram-available').textContent = specs.available_ram_gb.toFixed(1) + ' GB'; - - const container = document.getElementById('gpus-container'); - container.innerHTML = ''; - - if (specs.gpus.length === 0) { - const card = document.createElement('div'); - card.className = 'spec-card'; - card.innerHTML = 'GPU' + - 'No GPU detected'; - container.appendChild(card); - } else { - specs.gpus.forEach((gpu, i) => { - const card = document.createElement('div'); - card.className = 'spec-card'; - const label = specs.gpus.length > 1 ? 'GPU ' + (i + 1) : 'GPU'; - const countStr = gpu.count > 1 ? ' Γ—' + gpu.count : ''; - const vramStr = gpu.vram_gb != null ? gpu.vram_gb.toFixed(1) + ' GB VRAM' : 'Shared memory'; - const backendStr = gpu.backend !== 'None' ? gpu.backend : ''; - const details = [vramStr, backendStr].filter(Boolean).join(' Β· '); - card.innerHTML = '' + esc(label) + '' + - '' + esc(gpu.name + countStr) + '' + - '' + esc(details) + ''; - container.appendChild(card); - }); - } - - if (specs.unified_memory) { - const archCard = document.getElementById('memory-arch-card'); - archCard.style.display = ''; - document.getElementById('memory-arch').textContent = 'Unified (CPU + GPU shared)'; - } - } catch (e) { - console.error('Failed to load specs:', e); - document.getElementById('cpu-name').textContent = 'Error loading specs'; - } -} - -function fitClass(level) { - switch (level) { - case 'Perfect': return 'fit-perfect'; - case 'Good': return 'fit-good'; - case 'Marginal': return 'fit-marginal'; - default: return 'fit-tight'; - } -} - -function modeClass(mode) { - switch (mode) { - case 'GPU': return 'mode-gpu'; - case 'MoE Offload': return 'mode-moe'; - case 'CPU Offload': return 'mode-cpuoffload'; - default: return 'mode-cpuonly'; - } -} - -function showModal(fit) { - const modal = document.getElementById('model-modal'); - const body = document.getElementById('modal-body'); - - const memBar = Math.min(fit.utilization_pct, 100); - const memBarClass = fit.utilization_pct > 95 ? 'bar-red' : fit.utilization_pct > 80 ? 'bar-yellow' : 'bar-green'; - - let notesHtml = ''; - if (fit.notes && fit.notes.length > 0) { - notesHtml = '

Notes

    ' + - fit.notes.map(n => '
  • ' + esc(n) + '
    • ').join('') + - '
'; - } - - const installedBadge = fit.installed - ? 'Installed' - : 'Not Installed'; - - const downloadBtn = (!fit.installed && ollamaAvailable) - ? '' - : ''; - - body.innerHTML = ` -
-

${esc(fit.name)}

- ${installedBadge} -
- -
-
- Parameters - ${esc(fit.params_b.toFixed(1))}B -
-
- Quantization - ${esc(fit.quant)} -
-
- Runtime - ${esc(fit.runtime)} -
-
- Score - ${esc(fit.score.toFixed(0))}/100 -
-
- Est. Speed - ${esc(fit.estimated_tps.toFixed(1))} tok/s -
-
- Use Case - ${esc(fit.use_case)} -
-
- -
-

Fit Analysis

-
- ${esc(fit.fit_level)} - ${esc(fit.run_mode)} -
-
-
- Memory: ${esc(fit.memory_required_gb.toFixed(1))} / ${esc(fit.memory_available_gb.toFixed(1))} GB - ${esc(fit.utilization_pct.toFixed(0))}% -
-
-
-
-
-
- - ${notesHtml} - - - -
- ${downloadBtn} - -
- `; - - modal.classList.add('visible'); -} - -function closeModal() { - document.getElementById('model-modal').classList.remove('visible'); - if (pullInterval) { - clearInterval(pullInterval); - pullInterval = null; - } -} - -async function pullModel(name) { - const statusEl = document.getElementById('pull-status'); - const textEl = statusEl.querySelector('.pull-status-text'); - const barEl = statusEl.querySelector('.pull-bar-fill'); - const btn = document.querySelector('.btn-download'); - - statusEl.style.display = ''; - if (btn) btn.disabled = true; - textEl.textContent = 'Starting download...'; - - try { - await invoke('start_pull', { modelTag: name }); - - pullInterval = setInterval(async () => { - try { - const s = await invoke('poll_pull'); - if (!s) return; - textEl.textContent = s.status; - if (s.percent != null) barEl.style.width = s.percent + '%'; - if (s.done) { - clearInterval(pullInterval); - pullInterval = null; - if (s.error) { - textEl.textContent = 'Error: ' + s.error; - if (btn) btn.disabled = false; - } else { - textEl.textContent = 'Download complete!'; - barEl.style.width = '100%'; - // Refresh model list - await loadModels(); - } - } - } catch (e) { - console.error('Poll error:', e); - } - }, 500); - } catch (e) { - textEl.textContent = 'Error: ' + e; - if (btn) btn.disabled = false; - } -} - -function renderModels(fits) { - const tbody = document.getElementById('models-body'); - if (!fits || fits.length === 0) { - tbody.innerHTML = '

No models found
${esc(f.name)}${f.installed ? ' ●' : ''}${esc(f.params_b.toFixed(1))}B${esc(f.quant)}${esc(f.fit_level)}${esc(f.run_mode)}${esc(f.score.toFixed(0))}${esc(f.memory_required_gb.toFixed(1))} GB${esc(f.estimated_tps.toFixed(1))}${esc(f.use_case)}
Error loading models
- - - - - - - - - - - - - - - - -
ModelParamsQuantFitModeScoreRAM ReqEst. TPSUse Case
Loading models...
-
- - - - - - - diff --git a/llmfit-desktop/ui/styles.css b/llmfit-desktop/ui/styles.css deleted file mode 100644 index 0b1751af4..000000000 --- a/llmfit-desktop/ui/styles.css +++ /dev/null @@ -1,273 +0,0 @@ -:root { - --bg: #0d1117; - --surface: #161b22; - --border: #30363d; - --text: #e6edf3; - --text-dim: #8b949e; - --accent: #58a6ff; - --green: #3fb950; - --yellow: #d29922; - --red: #f85149; - --cyan: #39c5cf; -} - -* { box-sizing: border-box; margin: 0; padding: 0; } - -body { - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif; - background: var(--bg); - color: var(--text); - padding: 24px; - line-height: 1.5; -} - -h2 { font-size: 18px; font-weight: 600; margin-bottom: 12px; color: var(--text-dim); } - -.specs-grid { - display: grid; - grid-template-columns: repeat(auto-fill, minmax(200px, 1fr)); - gap: 12px; - margin-bottom: 24px; -} - -#gpus-container { - display: contents; -} - -.spec-card { - background: var(--surface); - border: 1px solid var(--border); - border-radius: 8px; - padding: 16px; - display: flex; - flex-direction: column; -} - -.spec-label { font-size: 12px; text-transform: uppercase; color: var(--text-dim); margin-bottom: 4px; } -.spec-value { font-size: 16px; font-weight: 600; } -.spec-detail { font-size: 13px; color: var(--text-dim); margin-top: 2px; } - -.controls { - display: flex; - gap: 12px; - margin-bottom: 12px; -} - -#search { - flex: 1; - padding: 8px 12px; - background: var(--surface); - border: 1px solid var(--border); - border-radius: 6px; - color: var(--text); - font-size: 14px; -} - -#fit-filter { - padding: 8px 12px; - background: var(--surface); - border: 1px solid var(--border); - border-radius: 6px; - color: var(--text); - font-size: 14px; -} - -#models-table-container { - overflow-x: auto; - border: 1px solid var(--border); - border-radius: 8px; -} - -table { - width: 100%; - border-collapse: collapse; - font-size: 14px; -} - -th { - text-align: left; - padding: 10px 12px; - background: var(--surface); - border-bottom: 1px solid var(--border); - font-weight: 600; - color: var(--text-dim); - position: sticky; - top: 0; -} - -td { - padding: 8px 12px; - border-bottom: 1px solid var(--border); -} - -tr:hover { background: var(--surface); } - -.loading { text-align: center; color: var(--text-dim); padding: 32px; } - -.fit-perfect { color: var(--green); font-weight: 600; } -.fit-good { color: var(--accent); font-weight: 600; } -.fit-marginal { color: var(--yellow); } -.fit-tight { color: var(--red); } - -.mode-gpu { color: var(--green); } -.mode-moe { color: var(--cyan); } -.mode-cpuoffload { color: var(--yellow); } -.mode-cpuonly { color: var(--text-dim); } - -/* Clickable rows */ -.model-row { cursor: pointer; } -.model-row:hover { background: var(--surface) !important; } - -.installed-dot { color: var(--green); font-size: 10px; } - -/* Modal */ -.modal-overlay { - display: none; - position: fixed; - inset: 0; - background: rgba(0, 0, 0, 0.6); - backdrop-filter: blur(4px); - z-index: 100; - align-items: center; - justify-content: center; -} - -.modal-overlay.visible { display: flex; } - -.modal-content { - background: var(--surface); - border: 1px solid var(--border); - border-radius: 12px; - padding: 24px; - max-width: 560px; - width: 90%; - max-height: 80vh; - overflow-y: auto; -} - -.modal-header-row { - display: flex; - align-items: center; - gap: 12px; - margin-bottom: 20px; -} - -.modal-header-row h3 { font-size: 20px; font-weight: 700; } - -.badge { - font-size: 11px; - padding: 2px 8px; - border-radius: 12px; - font-weight: 600; - text-transform: uppercase; -} - -.badge-installed { background: rgba(63, 185, 80, 0.15); color: var(--green); } -.badge-not-installed { background: rgba(139, 148, 158, 0.15); color: var(--text-dim); } - -.modal-grid { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 12px; - margin-bottom: 20px; -} - -.modal-stat { - display: flex; - flex-direction: column; -} - -.stat-label { font-size: 11px; text-transform: uppercase; color: var(--text-dim); margin-bottom: 2px; } -.stat-value { font-size: 15px; font-weight: 600; } - -.modal-section { margin-bottom: 16px; } -.modal-section h4 { font-size: 13px; color: var(--text-dim); text-transform: uppercase; margin-bottom: 8px; } -.modal-section ul { padding-left: 18px; font-size: 13px; color: var(--text-dim); } -.modal-section li { margin-bottom: 4px; } - -.fit-row { - display: flex; - align-items: center; - gap: 12px; - margin-bottom: 8px; - font-size: 15px; -} - -.fit-detail { color: var(--text-dim); font-size: 13px; } - -.mem-bar-container { margin-top: 4px; } - -.mem-bar-label { - display: flex; - justify-content: space-between; - font-size: 12px; - color: var(--text-dim); - margin-bottom: 4px; -} - -.mem-bar-track { - height: 8px; - background: var(--border); - border-radius: 4px; - overflow: hidden; -} - -.mem-bar-fill { - height: 100%; - border-radius: 4px; - transition: width 0.3s; -} - -.bar-green { background: var(--green); } -.bar-yellow { background: var(--yellow); } -.bar-red { background: var(--red); } - -.pull-status { - margin: 16px 0; -} - -.pull-status-text { - font-size: 13px; - color: var(--text-dim); - margin-bottom: 6px; -} - -.pull-bar-fill { - height: 100%; - border-radius: 4px; - background: var(--accent); - transition: width 0.3s; -} - -.modal-actions { - display: flex; - gap: 12px; - justify-content: flex-end; - margin-top: 20px; -} - -.btn-download { - padding: 8px 16px; - background: var(--accent); - color: var(--bg); - border: none; - border-radius: 6px; - font-size: 14px; - font-weight: 600; - cursor: pointer; -} - -.btn-download:hover { opacity: 0.9; } -.btn-download:disabled { opacity: 0.5; cursor: not-allowed; } - -.btn-close { - padding: 8px 16px; - background: transparent; - color: var(--text-dim); - border: 1px solid var(--border); - border-radius: 6px; - font-size: 14px; - cursor: pointer; -} - -.btn-close:hover { color: var(--text); border-color: var(--text-dim); } diff --git a/llmfit-tui/Cargo.toml b/llmfit-tui/Cargo.toml index 13a1a82be..0cfcb7289 100644 --- a/llmfit-tui/Cargo.toml +++ b/llmfit-tui/Cargo.toml @@ -25,7 +25,6 @@ tabled = "0.20" colored = "3.1" ratatui = "0.30" crossterm = "0.29" -arboard = "3.4" axum = "0.8" tokio = { version = "1.47", features = ["rt-multi-thread", "signal", "net"] } diff --git a/llmfit-tui/src/main.rs b/llmfit-tui/src/main.rs index 175c08ad2..be008c793 100644 --- a/llmfit-tui/src/main.rs +++ b/llmfit-tui/src/main.rs @@ -1681,8 +1681,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: llmfit_core::models::ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }, fit_level, run_mode: RunMode::Gpu, @@ -1756,8 +1754,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: llmfit_core::models::ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }, LlmModel { name: "Qwen/Qwen3-Coder-Next".to_string(), @@ -1778,8 +1774,6 @@ mod tests { gguf_sources: vec![], capabilities: vec![], format: llmfit_core::models::ModelFormat::default(), - num_attention_heads: None, - num_key_value_heads: None, }, ]; diff --git a/llmfit-tui/src/serve_api.rs b/llmfit-tui/src/serve_api.rs index 48260badd..154c8459a 100644 --- a/llmfit-tui/src/serve_api.rs +++ b/llmfit-tui/src/serve_api.rs @@ -525,7 +525,6 @@ fn fit_level_code(fit_level: FitLevel) -> &'static str { fn run_mode_code(run_mode: llmfit_core::fit::RunMode) -> &'static str { match run_mode { llmfit_core::fit::RunMode::Gpu => "gpu", - llmfit_core::fit::RunMode::TensorParallel => "tensor_parallel", llmfit_core::fit::RunMode::MoeOffload => "moe_offload", llmfit_core::fit::RunMode::CpuOffload => "cpu_offload", llmfit_core::fit::RunMode::CpuOnly => "cpu_only", diff --git a/llmfit-tui/src/theme.rs b/llmfit-tui/src/theme.rs index 14c9414cd..9c0f7aa03 100644 --- a/llmfit-tui/src/theme.rs +++ b/llmfit-tui/src/theme.rs @@ -6,60 +6,44 @@ use std::path::PathBuf; #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum Theme { Default, - Dracula, - Solarized, + IndustrialDark, + IndustrialLight, Nord, - Monokai, Gruvbox, - CatppuccinLatte, - CatppuccinFrappe, - CatppuccinMacchiato, - CatppuccinMocha, + Solarized, } impl Theme { pub fn label(&self) -> &'static str { match self { Theme::Default => "Default", - Theme::Dracula => "Dracula", - Theme::Solarized => "Solarized", + Theme::IndustrialDark => "Industrial Dark", + Theme::IndustrialLight => "Industrial Light", Theme::Nord => "Nord", - Theme::Monokai => "Monokai", Theme::Gruvbox => "Gruvbox", - Theme::CatppuccinLatte => "Catppuccin Latte", - Theme::CatppuccinFrappe => "Catppuccin FrappΓ©", - Theme::CatppuccinMacchiato => "Catppuccin Macchiato", - Theme::CatppuccinMocha => "Catppuccin Mocha", + Theme::Solarized => "Solarized", } } pub fn next(&self) -> Self { match self { - Theme::Default => Theme::Dracula, - Theme::Dracula => Theme::Solarized, - Theme::Solarized => Theme::Nord, - Theme::Nord => Theme::Monokai, - Theme::Monokai => Theme::Gruvbox, - Theme::Gruvbox => Theme::CatppuccinLatte, - Theme::CatppuccinLatte => Theme::CatppuccinFrappe, - Theme::CatppuccinFrappe => Theme::CatppuccinMacchiato, - Theme::CatppuccinMacchiato => Theme::CatppuccinMocha, - Theme::CatppuccinMocha => Theme::Default, + Theme::Default => Theme::IndustrialDark, + Theme::IndustrialDark => Theme::IndustrialLight, + Theme::IndustrialLight => Theme::Nord, + Theme::Nord => Theme::Gruvbox, + Theme::Gruvbox => Theme::Solarized, + Theme::Solarized => Theme::Default, } } pub fn colors(&self) -> ThemeColors { match self { Theme::Default => default_colors(), - Theme::Dracula => dracula_colors(), - Theme::Solarized => solarized_colors(), + Theme::IndustrialDark => industrial_dark_colors(), + Theme::IndustrialLight => industrial_light_colors(), Theme::Nord => nord_colors(), - Theme::Monokai => monokai_colors(), Theme::Gruvbox => gruvbox_colors(), - Theme::CatppuccinLatte => catppuccin_latte_colors(), - Theme::CatppuccinFrappe => catppuccin_frappe_colors(), - Theme::CatppuccinMacchiato => catppuccin_macchiato_colors(), - Theme::CatppuccinMocha => catppuccin_mocha_colors(), + Theme::Solarized => solarized_colors(), } } @@ -96,15 +80,11 @@ impl Theme { fn from_label(s: &str) -> Self { match s { - "Dracula" => Theme::Dracula, - "Solarized" => Theme::Solarized, + "Industrial Dark" => Theme::IndustrialDark, + "Industrial Light" => Theme::IndustrialLight, "Nord" => Theme::Nord, - "Monokai" => Theme::Monokai, "Gruvbox" => Theme::Gruvbox, - "Catppuccin Latte" => Theme::CatppuccinLatte, - "Catppuccin FrappΓ©" => Theme::CatppuccinFrappe, - "Catppuccin Macchiato" => Theme::CatppuccinMacchiato, - "Catppuccin Mocha" => Theme::CatppuccinMocha, + "Solarized" => Theme::Solarized, _ => Theme::Default, } } @@ -153,120 +133,114 @@ pub struct ThemeColors { } fn default_colors() -> ThemeColors { - // Default theme uses Color::Reset for fg so it inherits the terminal's - // foreground color, making it work on both light and dark terminals. - // Inspired by AndiDog's light-theme-support approach. ThemeColors { bg: Color::Reset, fg: Color::Reset, muted: Color::DarkGray, border: Color::DarkGray, - title: Color::Green, - highlight_bg: Color::LightBlue, + title: Color::Rgb(245, 158, 11), + highlight_bg: Color::Reset, - accent: Color::Cyan, - accent_secondary: Color::Yellow, + accent: Color::Rgb(245, 158, 11), + accent_secondary: Color::Rgb(16, 185, 129), - good: Color::Green, - warning: Color::Yellow, - error: Color::Red, - info: Color::Cyan, + good: Color::Rgb(16, 185, 129), + warning: Color::Rgb(245, 158, 11), + error: Color::Rgb(239, 68, 68), + info: Color::Rgb(14, 165, 233), - score_high: Color::Green, - score_mid: Color::Yellow, - score_low: Color::Red, + score_high: Color::Rgb(16, 185, 129), + score_mid: Color::Rgb(245, 158, 11), + score_low: Color::Rgb(239, 68, 68), - fit_perfect: Color::Green, - fit_good: Color::Yellow, - fit_marginal: Color::Magenta, - fit_tight: Color::Red, + fit_perfect: Color::Rgb(16, 185, 129), + fit_good: Color::Rgb(14, 165, 233), + fit_marginal: Color::Rgb(245, 158, 11), + fit_tight: Color::Rgb(239, 68, 68), - mode_gpu: Color::Green, - mode_moe: Color::Cyan, - mode_offload: Color::Yellow, + mode_gpu: Color::Rgb(16, 185, 129), + mode_moe: Color::Rgb(14, 165, 233), + mode_offload: Color::Rgb(245, 158, 11), mode_cpu: Color::DarkGray, - status_bg: Color::Green, + status_bg: Color::Rgb(245, 158, 11), status_fg: Color::Black, } } -fn dracula_colors() -> ThemeColors { - // Dracula: dark purple bg, pastel accents +fn industrial_dark_colors() -> ThemeColors { ThemeColors { - bg: Color::Rgb(40, 42, 54), - fg: Color::Rgb(248, 248, 242), - muted: Color::Rgb(98, 114, 164), - border: Color::Rgb(68, 71, 90), - title: Color::Rgb(80, 250, 123), - highlight_bg: Color::Rgb(68, 71, 90), - - accent: Color::Rgb(139, 233, 253), - accent_secondary: Color::Rgb(241, 250, 140), - - good: Color::Rgb(80, 250, 123), - warning: Color::Rgb(241, 250, 140), - error: Color::Rgb(255, 85, 85), - info: Color::Rgb(139, 233, 253), - - score_high: Color::Rgb(80, 250, 123), - score_mid: Color::Rgb(241, 250, 140), - score_low: Color::Rgb(255, 85, 85), - - fit_perfect: Color::Rgb(80, 250, 123), - fit_good: Color::Rgb(241, 250, 140), - fit_marginal: Color::Rgb(189, 147, 249), - fit_tight: Color::Rgb(255, 85, 85), - - mode_gpu: Color::Rgb(80, 250, 123), - mode_moe: Color::Rgb(139, 233, 253), - mode_offload: Color::Rgb(241, 250, 140), - mode_cpu: Color::Rgb(98, 114, 164), - - status_bg: Color::Rgb(189, 147, 249), - status_fg: Color::Rgb(40, 42, 54), + bg: Color::Rgb(9, 9, 11), + fg: Color::Rgb(250, 250, 250), + muted: Color::Rgb(113, 113, 122), + border: Color::Rgb(39, 39, 42), + title: Color::Rgb(245, 158, 11), + highlight_bg: Color::Rgb(39, 39, 42), + + accent: Color::Rgb(245, 158, 11), + accent_secondary: Color::Rgb(217, 119, 6), + + good: Color::Rgb(16, 185, 129), + warning: Color::Rgb(245, 158, 11), + error: Color::Rgb(239, 68, 68), + info: Color::Rgb(14, 165, 233), + + score_high: Color::Rgb(16, 185, 129), + score_mid: Color::Rgb(245, 158, 11), + score_low: Color::Rgb(239, 68, 68), + + fit_perfect: Color::Rgb(16, 185, 129), + fit_good: Color::Rgb(14, 165, 233), + fit_marginal: Color::Rgb(245, 158, 11), + fit_tight: Color::Rgb(239, 68, 68), + + mode_gpu: Color::Rgb(16, 185, 129), + mode_moe: Color::Rgb(14, 165, 233), + mode_offload: Color::Rgb(217, 119, 6), + mode_cpu: Color::Rgb(113, 113, 122), + + status_bg: Color::Rgb(245, 158, 11), + status_fg: Color::Rgb(9, 9, 11), } } -fn solarized_colors() -> ThemeColors { - // Solarized Dark +fn industrial_light_colors() -> ThemeColors { ThemeColors { - bg: Color::Rgb(0, 43, 54), - fg: Color::Rgb(131, 148, 150), - muted: Color::Rgb(88, 110, 117), - border: Color::Rgb(88, 110, 117), - title: Color::Rgb(133, 153, 0), - highlight_bg: Color::Rgb(7, 54, 66), - - accent: Color::Rgb(38, 139, 210), - accent_secondary: Color::Rgb(181, 137, 0), - - good: Color::Rgb(133, 153, 0), - warning: Color::Rgb(181, 137, 0), - error: Color::Rgb(220, 50, 47), - info: Color::Rgb(38, 139, 210), - - score_high: Color::Rgb(133, 153, 0), - score_mid: Color::Rgb(181, 137, 0), - score_low: Color::Rgb(220, 50, 47), - - fit_perfect: Color::Rgb(133, 153, 0), - fit_good: Color::Rgb(181, 137, 0), - fit_marginal: Color::Rgb(211, 54, 130), - fit_tight: Color::Rgb(220, 50, 47), - - mode_gpu: Color::Rgb(133, 153, 0), - mode_moe: Color::Rgb(42, 161, 152), - mode_offload: Color::Rgb(181, 137, 0), - mode_cpu: Color::Rgb(88, 110, 117), - - status_bg: Color::Rgb(38, 139, 210), - status_fg: Color::Rgb(253, 246, 227), + bg: Color::Rgb(255, 255, 255), + fg: Color::Rgb(9, 9, 11), + muted: Color::Rgb(161, 161, 170), + border: Color::Rgb(228, 228, 231), + title: Color::Rgb(217, 119, 6), + highlight_bg: Color::Rgb(228, 228, 231), + + accent: Color::Rgb(217, 119, 6), + accent_secondary: Color::Rgb(245, 158, 11), + + good: Color::Rgb(5, 150, 105), + warning: Color::Rgb(217, 119, 6), + error: Color::Rgb(220, 38, 38), + info: Color::Rgb(2, 132, 199), + + score_high: Color::Rgb(5, 150, 105), + score_mid: Color::Rgb(217, 119, 6), + score_low: Color::Rgb(220, 38, 38), + + fit_perfect: Color::Rgb(5, 150, 105), + fit_good: Color::Rgb(2, 132, 199), + fit_marginal: Color::Rgb(217, 119, 6), + fit_tight: Color::Rgb(220, 38, 38), + + mode_gpu: Color::Rgb(5, 150, 105), + mode_moe: Color::Rgb(2, 132, 199), + mode_offload: Color::Rgb(217, 119, 6), + mode_cpu: Color::Rgb(161, 161, 170), + + status_bg: Color::Rgb(217, 119, 6), + status_fg: Color::Rgb(255, 255, 255), } } fn nord_colors() -> ThemeColors { - // Nord: cool blue-gray palette ThemeColors { bg: Color::Rgb(46, 52, 64), fg: Color::Rgb(216, 222, 233), @@ -288,8 +262,8 @@ fn nord_colors() -> ThemeColors { score_low: Color::Rgb(191, 97, 106), fit_perfect: Color::Rgb(163, 190, 140), - fit_good: Color::Rgb(235, 203, 139), - fit_marginal: Color::Rgb(180, 142, 173), + fit_good: Color::Rgb(136, 192, 208), + fit_marginal: Color::Rgb(235, 203, 139), fit_tight: Color::Rgb(191, 97, 106), mode_gpu: Color::Rgb(163, 190, 140), @@ -302,45 +276,7 @@ fn nord_colors() -> ThemeColors { } } -fn monokai_colors() -> ThemeColors { - // Monokai Pro - ThemeColors { - bg: Color::Rgb(39, 40, 34), - fg: Color::Rgb(248, 248, 242), - muted: Color::Rgb(117, 113, 94), - border: Color::Rgb(73, 72, 62), - title: Color::Rgb(166, 226, 46), - highlight_bg: Color::Rgb(73, 72, 62), - - accent: Color::Rgb(102, 217, 239), - accent_secondary: Color::Rgb(230, 219, 116), - - good: Color::Rgb(166, 226, 46), - warning: Color::Rgb(230, 219, 116), - error: Color::Rgb(249, 38, 114), - info: Color::Rgb(102, 217, 239), - - score_high: Color::Rgb(166, 226, 46), - score_mid: Color::Rgb(230, 219, 116), - score_low: Color::Rgb(249, 38, 114), - - fit_perfect: Color::Rgb(166, 226, 46), - fit_good: Color::Rgb(230, 219, 116), - fit_marginal: Color::Rgb(174, 129, 255), - fit_tight: Color::Rgb(249, 38, 114), - - mode_gpu: Color::Rgb(166, 226, 46), - mode_moe: Color::Rgb(102, 217, 239), - mode_offload: Color::Rgb(230, 219, 116), - mode_cpu: Color::Rgb(117, 113, 94), - - status_bg: Color::Rgb(253, 151, 31), - status_fg: Color::Rgb(39, 40, 34), - } -} - fn gruvbox_colors() -> ThemeColors { - // Gruvbox Dark ThemeColors { bg: Color::Rgb(40, 40, 40), fg: Color::Rgb(235, 219, 178), @@ -362,8 +298,8 @@ fn gruvbox_colors() -> ThemeColors { score_low: Color::Rgb(251, 73, 52), fit_perfect: Color::Rgb(184, 187, 38), - fit_good: Color::Rgb(250, 189, 47), - fit_marginal: Color::Rgb(211, 134, 155), + fit_good: Color::Rgb(131, 165, 152), + fit_marginal: Color::Rgb(250, 189, 47), fit_tight: Color::Rgb(251, 73, 52), mode_gpu: Color::Rgb(184, 187, 38), @@ -376,154 +312,38 @@ fn gruvbox_colors() -> ThemeColors { } } -fn catppuccin_latte_colors() -> ThemeColors { - // Catppuccin Latte β€” light variant - // https://catppuccin.com/palette/ +fn solarized_colors() -> ThemeColors { ThemeColors { - bg: Color::Rgb(239, 241, 245), // Base - fg: Color::Rgb(76, 79, 105), // Text - muted: Color::Rgb(140, 143, 161), // Overlay 1 - border: Color::Rgb(172, 176, 190), // Surface 2 - title: Color::Rgb(64, 160, 43), // Green - highlight_bg: Color::Rgb(204, 208, 218), // Surface 0 - - accent: Color::Rgb(30, 102, 245), // Blue - accent_secondary: Color::Rgb(254, 100, 11), // Peach - - good: Color::Rgb(64, 160, 43), // Green - warning: Color::Rgb(223, 142, 29), // Yellow - error: Color::Rgb(210, 15, 57), // Red - info: Color::Rgb(23, 146, 153), // Teal - - score_high: Color::Rgb(64, 160, 43), - score_mid: Color::Rgb(223, 142, 29), - score_low: Color::Rgb(210, 15, 57), - - fit_perfect: Color::Rgb(64, 160, 43), - fit_good: Color::Rgb(223, 142, 29), - fit_marginal: Color::Rgb(136, 57, 239), // Mauve - fit_tight: Color::Rgb(210, 15, 57), - - mode_gpu: Color::Rgb(64, 160, 43), - mode_moe: Color::Rgb(4, 165, 229), // Sky - mode_offload: Color::Rgb(254, 100, 11), // Peach - mode_cpu: Color::Rgb(140, 143, 161), // Overlay 1 - - status_bg: Color::Rgb(136, 57, 239), // Mauve - status_fg: Color::Rgb(239, 241, 245), // Base - } -} + bg: Color::Rgb(0, 43, 54), + fg: Color::Rgb(131, 148, 150), + muted: Color::Rgb(88, 110, 117), + border: Color::Rgb(88, 110, 117), + title: Color::Rgb(133, 153, 0), + highlight_bg: Color::Rgb(7, 54, 66), -fn catppuccin_frappe_colors() -> ThemeColors { - // Catppuccin FrappΓ© β€” low-contrast dark variant - // https://catppuccin.com/palette/ - ThemeColors { - bg: Color::Rgb(48, 52, 70), // Base - fg: Color::Rgb(198, 208, 245), // Text - muted: Color::Rgb(131, 139, 167), // Overlay 1 - border: Color::Rgb(98, 104, 128), // Surface 2 - title: Color::Rgb(166, 209, 137), // Green - highlight_bg: Color::Rgb(65, 69, 89), // Surface 0 - - accent: Color::Rgb(140, 170, 238), // Blue - accent_secondary: Color::Rgb(239, 159, 118), // Peach - - good: Color::Rgb(166, 209, 137), // Green - warning: Color::Rgb(229, 200, 144), // Yellow - error: Color::Rgb(231, 130, 132), // Red - info: Color::Rgb(153, 209, 219), // Sky - - score_high: Color::Rgb(166, 209, 137), - score_mid: Color::Rgb(229, 200, 144), - score_low: Color::Rgb(231, 130, 132), - - fit_perfect: Color::Rgb(166, 209, 137), - fit_good: Color::Rgb(229, 200, 144), - fit_marginal: Color::Rgb(202, 158, 230), // Mauve - fit_tight: Color::Rgb(231, 130, 132), - - mode_gpu: Color::Rgb(166, 209, 137), - mode_moe: Color::Rgb(153, 209, 219), // Sky - mode_offload: Color::Rgb(239, 159, 118), // Peach - mode_cpu: Color::Rgb(131, 139, 167), // Overlay 1 - - status_bg: Color::Rgb(186, 187, 241), // Lavender - status_fg: Color::Rgb(35, 38, 52), // Crust - } -} + accent: Color::Rgb(38, 139, 210), + accent_secondary: Color::Rgb(181, 137, 0), -fn catppuccin_macchiato_colors() -> ThemeColors { - // Catppuccin Macchiato β€” medium-contrast dark variant - // https://catppuccin.com/palette/ - ThemeColors { - bg: Color::Rgb(36, 39, 58), // Base - fg: Color::Rgb(202, 211, 245), // Text - muted: Color::Rgb(128, 135, 162), // Overlay 1 - border: Color::Rgb(91, 96, 120), // Surface 2 - title: Color::Rgb(166, 218, 149), // Green - highlight_bg: Color::Rgb(54, 58, 79), // Surface 0 - - accent: Color::Rgb(138, 173, 244), // Blue - accent_secondary: Color::Rgb(245, 169, 127), // Peach - - good: Color::Rgb(166, 218, 149), // Green - warning: Color::Rgb(238, 212, 159), // Yellow - error: Color::Rgb(237, 135, 150), // Red - info: Color::Rgb(145, 215, 227), // Sky - - score_high: Color::Rgb(166, 218, 149), - score_mid: Color::Rgb(238, 212, 159), - score_low: Color::Rgb(237, 135, 150), - - fit_perfect: Color::Rgb(166, 218, 149), - fit_good: Color::Rgb(238, 212, 159), - fit_marginal: Color::Rgb(198, 160, 246), // Mauve - fit_tight: Color::Rgb(237, 135, 150), - - mode_gpu: Color::Rgb(166, 218, 149), - mode_moe: Color::Rgb(145, 215, 227), // Sky - mode_offload: Color::Rgb(245, 169, 127), // Peach - mode_cpu: Color::Rgb(128, 135, 162), // Overlay 1 - - status_bg: Color::Rgb(183, 189, 248), // Lavender - status_fg: Color::Rgb(24, 25, 38), // Crust - } -} + good: Color::Rgb(133, 153, 0), + warning: Color::Rgb(181, 137, 0), + error: Color::Rgb(220, 50, 47), + info: Color::Rgb(38, 139, 210), -fn catppuccin_mocha_colors() -> ThemeColors { - // Catppuccin Mocha β€” darkest variant (the original) - // https://catppuccin.com/palette/ - ThemeColors { - bg: Color::Rgb(30, 30, 46), // Base - fg: Color::Rgb(205, 214, 244), // Text - muted: Color::Rgb(127, 132, 156), // Overlay 1 - border: Color::Rgb(88, 91, 112), // Surface 2 - title: Color::Rgb(166, 227, 161), // Green - highlight_bg: Color::Rgb(49, 50, 68), // Surface 0 - - accent: Color::Rgb(137, 180, 250), // Blue - accent_secondary: Color::Rgb(250, 179, 135), // Peach - - good: Color::Rgb(166, 227, 161), // Green - warning: Color::Rgb(249, 226, 175), // Yellow - error: Color::Rgb(243, 139, 168), // Red - info: Color::Rgb(137, 220, 235), // Sky - - score_high: Color::Rgb(166, 227, 161), - score_mid: Color::Rgb(249, 226, 175), - score_low: Color::Rgb(243, 139, 168), - - fit_perfect: Color::Rgb(166, 227, 161), - fit_good: Color::Rgb(249, 226, 175), - fit_marginal: Color::Rgb(203, 166, 247), // Mauve - fit_tight: Color::Rgb(243, 139, 168), - - mode_gpu: Color::Rgb(166, 227, 161), - mode_moe: Color::Rgb(137, 220, 235), // Sky - mode_offload: Color::Rgb(250, 179, 135), // Peach - mode_cpu: Color::Rgb(127, 132, 156), // Overlay 1 - - status_bg: Color::Rgb(180, 190, 254), // Lavender - status_fg: Color::Rgb(17, 17, 27), // Crust + score_high: Color::Rgb(133, 153, 0), + score_mid: Color::Rgb(181, 137, 0), + score_low: Color::Rgb(220, 50, 47), + + fit_perfect: Color::Rgb(133, 153, 0), + fit_good: Color::Rgb(38, 139, 210), + fit_marginal: Color::Rgb(181, 137, 0), + fit_tight: Color::Rgb(220, 50, 47), + + mode_gpu: Color::Rgb(133, 153, 0), + mode_moe: Color::Rgb(42, 161, 152), + mode_offload: Color::Rgb(181, 137, 0), + mode_cpu: Color::Rgb(88, 110, 117), + + status_bg: Color::Rgb(38, 139, 210), + status_fg: Color::Rgb(253, 246, 227), } } diff --git a/llmfit-tui/src/tui_app.rs b/llmfit-tui/src/tui_app.rs index b23b29aac..2c5915365 100644 --- a/llmfit-tui/src/tui_app.rs +++ b/llmfit-tui/src/tui_app.rs @@ -113,43 +113,6 @@ impl AvailabilityFilter { } } -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum TpFilter { - All, - Tp2, - Tp3, - Tp4, -} - -impl TpFilter { - pub fn label(&self) -> &str { - match self { - TpFilter::All => "All", - TpFilter::Tp2 => "TP=2", - TpFilter::Tp3 => "TP=3", - TpFilter::Tp4 => "TP=4", - } - } - - pub fn next(&self) -> Self { - match self { - TpFilter::All => TpFilter::Tp2, - TpFilter::Tp2 => TpFilter::Tp3, - TpFilter::Tp3 => TpFilter::Tp4, - TpFilter::Tp4 => TpFilter::All, - } - } - - pub fn matches(&self, model: &llmfit_core::models::LlmModel) -> bool { - match self { - TpFilter::All => true, - TpFilter::Tp2 => model.supports_tp(2), - TpFilter::Tp3 => model.supports_tp(3), - TpFilter::Tp4 => model.supports_tp(4), - } - } -} - #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum DownloadProvider { Ollama, @@ -212,7 +175,6 @@ pub struct App { // Filters pub fit_filter: FitFilter, pub availability_filter: AvailabilityFilter, - pub tp_filter: TpFilter, pub installed_first: bool, pub sort_column: SortColumn, pub sort_ascending: bool, @@ -443,7 +405,6 @@ impl App { selected_capabilities, fit_filter: FitFilter::All, availability_filter: AvailabilityFilter::All, - tp_filter: TpFilter::All, installed_first: false, sort_column: SortColumn::Score, sort_ascending: false, @@ -658,8 +619,6 @@ impl App { } }; - let matches_tp = self.tp_filter.matches(&fit.model); - matches_search && matches_provider && matches_use_case @@ -669,7 +628,6 @@ impl App { && matches_quant && matches_run_mode && matches_params_bucket - && matches_tp }) .map(|(i, _)| i) .collect(); @@ -753,11 +711,6 @@ impl App { self.apply_filters(); } - pub fn cycle_tp_filter(&mut self) { - self.tp_filter = self.tp_filter.next(); - self.apply_filters(); - } - pub fn cycle_sort_column(&mut self) { self.sort_column = self.sort_column.next(); self.sort_ascending = false; @@ -825,21 +778,6 @@ impl App { self.pull_status = Some("Cleared compare mark".to_string()); } - pub fn copy_selected_model_name(&mut self) { - let Some(fit) = self.selected_fit() else { - self.pull_status = Some("No model selected".to_string()); - return; - }; - let name = fit.model.name.clone(); - match arboard::Clipboard::new() { - Ok(mut clipboard) => match clipboard.set_text(&name) { - Ok(()) => self.pull_status = Some(format!("Copied '{}' to clipboard", name)), - Err(e) => self.pull_status = Some(format!("Clipboard error: {}", e)), - }, - Err(e) => self.pull_status = Some(format!("Clipboard error: {}", e)), - } - } - pub fn selected_compare_pair(&self) -> Option<(&ModelFit, &ModelFit)> { let selected = self.selected_fit()?; let mark_name = self.compare_mark_model.as_deref()?; @@ -1089,13 +1027,6 @@ impl App { self.apply_filters(); } - pub fn provider_popup_clear_all(&mut self) { - for s in &mut self.selected_providers { - *s = false; - } - self.apply_filters(); - } - pub fn use_case_popup_up(&mut self) { if self.use_case_cursor > 0 { self.use_case_cursor -= 1; diff --git a/llmfit-tui/src/tui_events.rs b/llmfit-tui/src/tui_events.rs index f855d30a3..989c83d37 100644 --- a/llmfit-tui/src/tui_events.rs +++ b/llmfit-tui/src/tui_events.rs @@ -79,9 +79,6 @@ fn handle_normal_mode(app: &mut App, key: KeyEvent) { // Availability filter KeyCode::Char('a') => app.cycle_availability_filter(), - // TP compatibility filter - KeyCode::Char('T') => app.cycle_tp_filter(), - // Sort column KeyCode::Char('s') => app.cycle_sort_column(), @@ -135,7 +132,6 @@ fn handle_normal_mode(app: &mut App, key: KeyEvent) { KeyCode::Char('m') => app.mark_selected_for_compare(), KeyCode::Char('c') => app.toggle_compare_view(), KeyCode::Char('x') => app.clear_compare_mark(), - KeyCode::Char('y') => app.copy_selected_model_name(), _ => {} } @@ -217,7 +213,6 @@ fn handle_provider_popup_mode(app: &mut App, key: KeyEvent) { KeyCode::Char(' ') | KeyCode::Enter => app.provider_popup_toggle(), KeyCode::Char('a') => app.provider_popup_select_all(), - KeyCode::Char('c') => app.provider_popup_clear_all(), _ => {} } diff --git a/llmfit-tui/src/tui_ui.rs b/llmfit-tui/src/tui_ui.rs index 48ffe9c05..1fd1a92d9 100644 --- a/llmfit-tui/src/tui_ui.rs +++ b/llmfit-tui/src/tui_ui.rs @@ -246,7 +246,6 @@ fn draw_search_and_filters(frame: &mut Frame, app: &App, area: Rect, tc: &ThemeC Constraint::Length(18), // sort column Constraint::Length(20), // fit filter Constraint::Length(20), // availability filter - Constraint::Length(14), // TP filter Constraint::Length(16), // theme ]) .split(area); @@ -433,21 +432,6 @@ fn draw_search_and_filters(frame: &mut Frame, app: &App, area: Rect, tc: &ThemeC .block(avail_block); frame.render_widget(avail_text, chunks[6]); - // TP filter - use crate::tui_app::TpFilter; - let tp_style = match app.tp_filter { - TpFilter::All => Style::default().fg(tc.fg), - _ => Style::default().fg(tc.accent), - }; - let tp_block = Block::default() - .borders(Borders::ALL) - .border_style(Style::default().fg(tc.border)) - .title(" TP [T] ") - .title_style(Style::default().fg(tc.muted)); - let tp_text = - Paragraph::new(Line::from(Span::styled(app.tp_filter.label(), tp_style))).block(tp_block); - frame.render_widget(tp_text, chunks[7]); - // Theme indicator let theme_block = Block::default() .borders(Borders::ALL) @@ -460,7 +444,7 @@ fn draw_search_and_filters(frame: &mut Frame, app: &App, area: Rect, tc: &ThemeC Style::default().fg(tc.info), ))) .block(theme_block); - frame.render_widget(theme_text, chunks[8]); + frame.render_widget(theme_text, chunks[7]); } fn fit_color(level: FitLevel, tc: &ThemeColors) -> Color { @@ -562,7 +546,6 @@ fn draw_table(frame: &mut Frame, app: &mut App, area: Rect, tc: &ThemeColors) { let mode_color = match fit.run_mode { llmfit_core::fit::RunMode::Gpu => tc.mode_gpu, - llmfit_core::fit::RunMode::TensorParallel => tc.mode_gpu, llmfit_core::fit::RunMode::MoeOffload => tc.mode_moe, llmfit_core::fit::RunMode::CpuOffload => tc.mode_offload, llmfit_core::fit::RunMode::CpuOnly => tc.mode_cpu, @@ -635,9 +618,9 @@ fn draw_table(frame: &mut Frame, app: &mut App, area: Rect, tc: &ThemeColors) { .map(|r| r.contains(&row_idx)) .unwrap_or(false); let row_style = if is_pulling { - Style::default().bg(Color::Rgb(50, 50, 0)) + Style::default().bg(tc.highlight_bg).fg(tc.warning) } else if in_visual_range { - Style::default().bg(Color::Rgb(40, 40, 80)) + Style::default().bg(tc.highlight_bg) } else { Style::default() }; @@ -1234,7 +1217,6 @@ fn draw_multi_compare(frame: &mut Frame, app: &App, area: Rect, tc: &ThemeColors .map(|m| { let c = match m.run_mode { llmfit_core::fit::RunMode::Gpu => tc.mode_gpu, - llmfit_core::fit::RunMode::TensorParallel => tc.mode_gpu, llmfit_core::fit::RunMode::MoeOffload => tc.mode_moe, llmfit_core::fit::RunMode::CpuOffload => tc.mode_offload, llmfit_core::fit::RunMode::CpuOnly => tc.mode_cpu, @@ -2099,25 +2081,6 @@ fn draw_provider_popup(frame: &mut Frame, app: &App, tc: &ThemeColors) { Style::default() .fg(tc.accent_secondary) .add_modifier(Modifier::BOLD), - ) - .title_bottom( - Line::from(vec![ - Span::styled( - " a", - Style::default() - .fg(tc.accent_secondary) - .add_modifier(Modifier::BOLD), - ), - Span::styled(": all | ", Style::default().fg(tc.muted)), - Span::styled( - "c", - Style::default() - .fg(tc.accent_secondary) - .add_modifier(Modifier::BOLD), - ), - Span::styled(": clear ", Style::default().fg(tc.muted)), - ]) - .centered(), ); let paragraph = Paragraph::new(lines).block(block); @@ -2379,7 +2342,7 @@ fn status_keys_and_mode(app: &App) -> (String, String) { }; ( format!( - " ↑↓/jk:nav {} /:search f:fit s:sort v:visual V:select t:theme p:plan m:mark c:compare x:clear mark y:copy{} P:providers U:use cases C:caps q:quit tok/s*:est", + " ↑↓/jk:nav {} /:search f:fit s:sort v:visual V:select t:theme p:plan m:mark c:compare x:clear mark{} P:providers U:use cases C:caps q:quit tok/s*:est", detail_key, ollama_keys, ), "NORMAL".to_string(), diff --git a/llmfit-web/components.json b/llmfit-web/components.json new file mode 100644 index 000000000..275d4164c --- /dev/null +++ b/llmfit-web/components.json @@ -0,0 +1,21 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": false, + "tsx": true, + "tailwind": { + "config": "tailwind.config.js", + "css": "src/index.css", + "baseColor": "zinc", + "cssVariables": true, + "prefix": "" + }, + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "iconLibrary": "lucide" +} diff --git a/llmfit-web/jsconfig.json b/llmfit-web/jsconfig.json new file mode 100644 index 000000000..abe04df57 --- /dev/null +++ b/llmfit-web/jsconfig.json @@ -0,0 +1,8 @@ +{ + "compilerOptions": { + "baseUrl": ".", + "paths": { + "@/*": ["./src/*"] + } + } +} diff --git a/llmfit-web/package-lock.json b/llmfit-web/package-lock.json index 23093a51c..6190b2b42 100644 --- a/llmfit-web/package-lock.json +++ b/llmfit-web/package-lock.json @@ -8,6 +8,10 @@ "name": "llmfit-web", 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"consulting", + "url": "https://feross.org/support" + } + ], + "license": "MIT", + "dependencies": { + "queue-microtask": "^1.2.2" + } + }, "node_modules/safer-buffer": { "version": "2.1.2", "resolved": "https://registry.npmjs.org/safer-buffer/-/safer-buffer-2.1.2.tgz", @@ -2426,6 +4285,42 @@ "node": ">=8" } }, + "node_modules/sucrase": { + "version": "3.35.1", + "resolved": "https://registry.npmjs.org/sucrase/-/sucrase-3.35.1.tgz", + "integrity": "sha512-DhuTmvZWux4H1UOnWMB3sk0sbaCVOoQZjv8u1rDoTV0HTdGem9hkAZtl4JZy8P2z4Bg0nT+YMeOFyVr4zcG5Tw==", + "dev": true, + "license": "MIT", + "dependencies": { + "@jridgewell/gen-mapping": "^0.3.2", + "commander": "^4.0.0", + "lines-and-columns": "^1.1.6", + "mz": "^2.7.0", + "pirates": "^4.0.1", + "tinyglobby": "^0.2.11", + "ts-interface-checker": "^0.1.9" + }, + "bin": { + "sucrase": "bin/sucrase", + "sucrase-node": "bin/sucrase-node" + }, + "engines": { + "node": ">=16 || 14 >=14.17" + } + }, + "node_modules/supports-preserve-symlinks-flag": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz", + "integrity": "sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">= 0.4" + }, + "funding": { + "url": "https://github.com/sponsors/ljharb" + } + }, "node_modules/symbol-tree": { "version": "3.2.4", "resolved": "https://registry.npmjs.org/symbol-tree/-/symbol-tree-3.2.4.tgz", @@ -2433,6 +4328,89 @@ "dev": true, "license": "MIT" }, + "node_modules/tailwind-merge": { + "version": "3.5.0", + "resolved": "https://registry.npmjs.org/tailwind-merge/-/tailwind-merge-3.5.0.tgz", + "integrity": "sha512-I8K9wewnVDkL1NTGoqWmVEIlUcB9gFriAEkXkfCjX5ib8ezGxtR3xD7iZIxrfArjEsH7F1CHD4RFUtxefdqV/A==", + "dev": true, + "license": "MIT", + "funding": { + "type": "github", + "url": "https://github.com/sponsors/dcastil" + } + }, + "node_modules/tailwindcss": { + "version": "3.4.19", + "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.4.19.tgz", + "integrity": "sha512-3ofp+LL8E+pK/JuPLPggVAIaEuhvIz4qNcf3nA1Xn2o/7fb7s/TYpHhwGDv1ZU3PkBluUVaF8PyCHcm48cKLWQ==", + "dev": true, + "license": "MIT", + "peer": true, + "dependencies": { + "@alloc/quick-lru": "^5.2.0", + "arg": "^5.0.2", + "chokidar": "^3.6.0", + "didyoumean": "^1.2.2", + "dlv": "^1.1.3", + "fast-glob": "^3.3.2", + "glob-parent": "^6.0.2", + "is-glob": "^4.0.3", + "jiti": "^1.21.7", + "lilconfig": "^3.1.3", + "micromatch": "^4.0.8", + "normalize-path": "^3.0.0", + "object-hash": "^3.0.0", + "picocolors": "^1.1.1", + "postcss": "^8.4.47", + "postcss-import": "^15.1.0", + "postcss-js": "^4.0.1", + "postcss-load-config": "^4.0.2 || ^5.0 || ^6.0", + "postcss-nested": "^6.2.0", + "postcss-selector-parser": "^6.1.2", + "resolve": "^1.22.8", + "sucrase": "^3.35.0" + }, + "bin": { + "tailwind": "lib/cli.js", + "tailwindcss": "lib/cli.js" + }, + "engines": { + "node": ">=14.0.0" + } + }, + "node_modules/tailwindcss-animate": { + "version": "1.0.7", + "resolved": "https://registry.npmjs.org/tailwindcss-animate/-/tailwindcss-animate-1.0.7.tgz", + "integrity": "sha512-bl6mpH3T7I3UFxuvDEXLxy/VuFxBk5bbzplh7tXI68mwMokNYd1t9qPBHlnyTwfa4JGC4zP516I1hYYtQ/vspA==", + "dev": true, + "license": "MIT", + "peerDependencies": { + "tailwindcss": ">=3.0.0 || insiders" + } + }, + "node_modules/thenify": { + "version": "3.3.1", + "resolved": "https://registry.npmjs.org/thenify/-/thenify-3.3.1.tgz", + "integrity": "sha512-RVZSIV5IG10Hk3enotrhvz0T9em6cyHBLkH/YAZuKqd8hRkKhSfCGIcP2KUY0EPxndzANBmNllzWPwak+bheSw==", + "dev": true, + "license": "MIT", + "dependencies": { + "any-promise": "^1.0.0" + } + }, + "node_modules/thenify-all": { + "version": "1.6.0", + "resolved": "https://registry.npmjs.org/thenify-all/-/thenify-all-1.6.0.tgz", + "integrity": "sha512-RNxQH/qI8/t3thXJDwcstUO4zeqo64+Uy/+sNVRBx4Xn2OX+OZ9oP+iJnNFqplFra2ZUVeKCSa2oVWi3T4uVmA==", + "dev": true, + "license": "MIT", + "dependencies": { + "thenify": ">= 3.1.0 < 4" + }, + "engines": { + "node": ">=0.8" + } + }, "node_modules/tinybench": { "version": "2.9.0", "resolved": "https://registry.npmjs.org/tinybench/-/tinybench-2.9.0.tgz", @@ -2447,6 +4425,55 @@ "dev": true, "license": "MIT" }, + "node_modules/tinyglobby": { + "version": "0.2.15", + "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.15.tgz", + "integrity": "sha512-j2Zq4NyQYG5XMST4cbs02Ak8iJUdxRM0XI5QyxXuZOzKOINmWurp3smXu3y5wDcJrptwpSjgXHzIQxR0omXljQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "fdir": "^6.5.0", + "picomatch": "^4.0.3" + }, + "engines": { + "node": ">=12.0.0" + }, + "funding": { + "url": "https://github.com/sponsors/SuperchupuDev" + } + }, + "node_modules/tinyglobby/node_modules/fdir": { + "version": "6.5.0", + "resolved": "https://registry.npmjs.org/fdir/-/fdir-6.5.0.tgz", + "integrity": "sha512-tIbYtZbucOs0BRGqPJkshJUYdL+SDH7dVM8gjy+ERp3WAUjLEFJE+02kanyHtwjWOnwrKYBiwAmM0p4kLJAnXg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12.0.0" + }, + "peerDependencies": { + "picomatch": "^3 || ^4" + }, + "peerDependenciesMeta": { + "picomatch": { + "optional": true + } + } + }, + "node_modules/tinyglobby/node_modules/picomatch": { + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz", + "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", + "dev": true, + "license": "MIT", + "peer": true, + "engines": { + "node": ">=12" + }, + "funding": { + "url": "https://github.com/sponsors/jonschlinkert" + } + }, "node_modules/tinypool": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/tinypool/-/tinypool-1.1.1.tgz", @@ -2497,6 +4524,19 @@ "dev": true, "license": "MIT" }, + "node_modules/to-regex-range": { + "version": "5.0.1", + "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz", + "integrity": "sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "is-number": "^7.0.0" + }, + "engines": { + "node": ">=8.0" + } + }, "node_modules/tough-cookie": { "version": "5.1.2", "resolved": "https://registry.npmjs.org/tough-cookie/-/tough-cookie-5.1.2.tgz", @@ -2523,6 +4563,19 @@ "node": ">=18" } }, + "node_modules/ts-interface-checker": { + "version": "0.1.13", + "resolved": "https://registry.npmjs.org/ts-interface-checker/-/ts-interface-checker-0.1.13.tgz", + "integrity": "sha512-Y/arvbn+rrz3JCKl9C4kVNfTfSm2/mEp5FSz5EsZSANGPSlQrpRI5M4PKF+mJnE52jOO90PnPSc3Ur3bTQw0gA==", + "dev": true, + "license": "Apache-2.0" + }, + "node_modules/tslib": { + "version": "2.8.1", + "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz", + "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==", + "license": "0BSD" + }, "node_modules/update-browserslist-db": { "version": "1.2.3", "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz", @@ -2554,6 +4607,56 @@ "browserslist": ">= 4.21.0" } }, + "node_modules/use-callback-ref": { + "version": "1.3.3", + "resolved": "https://registry.npmjs.org/use-callback-ref/-/use-callback-ref-1.3.3.tgz", + "integrity": "sha512-jQL3lRnocaFtu3V00JToYz/4QkNWswxijDaCVNZRiRTO3HQDLsdu1ZtmIUvV4yPp+rvWm5j0y0TG/S61cuijTg==", + "license": "MIT", + "dependencies": { + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/use-sidecar": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/use-sidecar/-/use-sidecar-1.1.3.tgz", + "integrity": "sha512-Fedw0aZvkhynoPYlA5WXrMCAMm+nSWdZt6lzJQ7Ok8S6Q+VsHmHpRWndVRJ8Be0ZbkfPc5LRYH+5XrzXcEeLRQ==", + "license": "MIT", + "dependencies": { + "detect-node-es": "^1.1.0", + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/util-deprecate": { + "version": "1.0.2", + "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", + "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", + "dev": true, + "license": "MIT" + }, "node_modules/vite": { "version": "5.4.21", "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.21.tgz", diff --git a/llmfit-web/package.json b/llmfit-web/package.json index d6e78cf0c..bea220843 100644 --- a/llmfit-web/package.json +++ b/llmfit-web/package.json @@ -11,6 +11,10 @@ "test:watch": "vitest" }, "dependencies": { + "@radix-ui/react-label": "^2.1.8", + "@radix-ui/react-progress": "^1.1.8", + "@radix-ui/react-select": "^2.2.6", + "@radix-ui/react-slot": "^1.2.4", "react": "^18.2.0", "react-dom": "^18.2.0" }, @@ -18,7 +22,15 @@ "@testing-library/jest-dom": "^6.6.3", "@testing-library/react": "^16.2.0", "@vitejs/plugin-react": "^4.3.4", + "autoprefixer": "^10.4.27", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", "jsdom": "^26.0.0", + "lucide-react": "^0.577.0", + "postcss": "^8.5.8", + "tailwind-merge": "^3.5.0", + "tailwindcss": "^3.4.19", + "tailwindcss-animate": "^1.0.7", "vite": "^5.4.12", "vitest": "^2.1.8" } diff --git a/llmfit-web/postcss.config.js b/llmfit-web/postcss.config.js new file mode 100644 index 000000000..2e7af2b7f --- /dev/null +++ b/llmfit-web/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/llmfit-web/src/App.jsx b/llmfit-web/src/App.jsx index c575f352c..c47509253 100644 --- a/llmfit-web/src/App.jsx +++ b/llmfit-web/src/App.jsx @@ -1,5 +1,24 @@ -import { useEffect, useMemo, useState } from 'react'; +import React, { useEffect, useMemo, useState } from 'react'; import { DEFAULT_FILTERS, fetchModels, fetchSystemInfo } from './api'; +import { Button } from "@/components/ui/button"; +import { Card, CardContent, CardHeader, CardTitle, CardDescription } from "@/components/ui/card"; +import { Badge } from "@/components/ui/badge"; +import { Progress } from "@/components/ui/progress"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; +import { + Table, + TableBody, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; +import { + Server, Cpu, HardDrive, RefreshCw, Sun, Moon, + Activity, Monitor, Database, Settings, BarChart +} from "lucide-react"; const THEME_KEY = 'llmfit-theme'; @@ -48,139 +67,91 @@ const SORT_OPTIONS = [ ]; function initialTheme() { - if (typeof window === 'undefined') { - return 'light'; - } - + if (typeof window === 'undefined') return 'dark'; const stored = window.localStorage.getItem(THEME_KEY); - if (stored === 'light' || stored === 'dark') { - return stored; - } - + if (stored === 'light' || stored === 'dark') return stored; return window.matchMedia?.('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'; } function round(value, digits = 1) { - if (typeof value !== 'number' || Number.isNaN(value)) { - return 'β€”'; - } + if (typeof value !== 'number' || Number.isNaN(value)) return 'β€”'; return value.toFixed(digits); } -function fitClass(code) { - return `fit fit-${code || 'unknown'}`; +function getFitBadgeVariant(level) { + switch (level) { + case 'perfect': return 'default'; // primary + case 'good': return 'secondary'; + case 'marginal': return 'outline'; + case 'too_tight': return 'destructive'; + default: return 'outline'; + } } -function modeClass(code) { - return `mode mode-${code || 'unknown'}`; +function fitRank(level) { + switch (level) { + case 'perfect': return 3; + case 'good': return 2; + case 'marginal': return 1; + case 'too_tight': return 0; + default: return -1; + } } -function SystemCard({ label, value, detail }) { - return ( -
-

{label}

-

{value}

- {detail ?

{detail}

: null} -
- ); +function applyClientFitFilter(models, minFit) { + const list = Array.isArray(models) ? models : []; + if (minFit === 'all') return list; + if (minFit === 'too_tight') return list.filter((m) => m.fit_level === 'too_tight'); + const threshold = fitRank(minFit); + return list.filter((m) => fitRank(m.fit_level) >= threshold); } function MetricBar({ label, value }) { const safe = Number.isFinite(value) ? Math.max(0, Math.min(value, 100)) : 0; return ( -
-
+
+
{label} {round(value, 1)}
-
-
-
+
); } -function fitRank(level) { - switch (level) { - case 'perfect': - return 3; - case 'good': - return 2; - case 'marginal': - return 1; - case 'too_tight': - return 0; - default: - return -1; - } -} - -function applyClientFitFilter(models, minFit) { - const list = Array.isArray(models) ? models : []; - if (minFit === 'all') { - return list; - } - if (minFit === 'too_tight') { - return list.filter((model) => model.fit_level === 'too_tight'); - } - - const threshold = fitRank(minFit); - return list.filter((model) => { - const rank = fitRank(model.fit_level); - return rank >= threshold; - }); -} - export default function App() { const [theme, setTheme] = useState(initialTheme); const [filters, setFilters] = useState(DEFAULT_FILTERS); - const [systemState, setSystemState] = useState({ - loading: true, - error: '', - payload: null - }); - const [modelsState, setModelsState] = useState({ - loading: true, - error: '', - models: [], - total: 0, - returned: 0 - }); + const [systemState, setSystemState] = useState({ loading: true, error: '', payload: null }); + const [modelsState, setModelsState] = useState({ loading: true, error: '', models: [], total: 0, returned: 0 }); const [selectedModelName, setSelectedModelName] = useState(null); const [refreshTick, setRefreshTick] = useState(0); useEffect(() => { - document.documentElement.dataset.theme = theme; + document.documentElement.classList.remove('light', 'dark'); + document.documentElement.classList.add(theme); window.localStorage.setItem(THEME_KEY, theme); }, [theme]); useEffect(() => { const controller = new AbortController(); - async function loadSystem() { setSystemState((prev) => ({ ...prev, loading: true, error: '' })); try { const payload = await fetchSystemInfo(controller.signal); setSystemState({ loading: false, error: '', payload }); } catch (error) { - if (controller.signal.aborted) { - return; + if (!controller.signal.aborted) { + setSystemState({ loading: false, error: error.message || 'Unable to load system details.', payload: null }); } - setSystemState({ - loading: false, - error: error instanceof Error ? error.message : 'Unable to load system details.', - payload: null - }); } } - loadSystem(); return () => controller.abort(); }, [refreshTick]); useEffect(() => { const controller = new AbortController(); - async function loadModels() { setModelsState((prev) => ({ ...prev, loading: true, error: '' })); try { @@ -189,381 +160,298 @@ export default function App() { const fitFiltered = applyClientFitFilter(fetchedModels, filters.minFit); const limit = Number.parseInt(filters.limit, 10); const models = Number.isFinite(limit) && limit > 0 ? fitFiltered.slice(0, limit) : fitFiltered; - const serverTotal = - typeof payload.total_models === 'number' && Number.isFinite(payload.total_models) - ? payload.total_models - : fitFiltered.length; - const total = filters.minFit === 'too_tight' ? fitFiltered.length : serverTotal; - setModelsState({ - loading: false, - error: '', - models, - total, - returned: models.length - }); - + const total = filters.minFit === 'too_tight' ? fitFiltered.length : (payload.total_models || fitFiltered.length); + + setModelsState({ loading: false, error: '', models, total, returned: models.length }); setSelectedModelName((current) => { - if (!current) { - return models[0]?.name ?? null; - } - const stillVisible = models.some((model) => model.name === current); - return stillVisible ? current : models[0]?.name ?? null; + if (!current || !models.some((m) => m.name === current)) return models[0]?.name ?? null; + return current; }); } catch (error) { - if (controller.signal.aborted) { - return; + if (!controller.signal.aborted) { + setModelsState({ loading: false, error: error.message || 'Unable to load model fits.', models: [], total: 0, returned: 0 }); + setSelectedModelName(null); } - setModelsState({ - loading: false, - error: error instanceof Error ? error.message : 'Unable to load model fits.', - models: [], - total: 0, - returned: 0 - }); - setSelectedModelName(null); } } - loadModels(); return () => controller.abort(); }, [filters, refreshTick]); const selectedModel = useMemo( - () => modelsState.models.find((model) => model.name === selectedModelName) ?? null, + () => modelsState.models.find((m) => m.name === selectedModelName) ?? null, [modelsState.models, selectedModelName] ); - const handleFieldChange = (field) => (event) => { - const value = event.target.type === 'checkbox' ? event.target.checked : event.target.value; - setFilters((current) => ({ - ...current, - [field]: value - })); + const handleFieldChange = (field, value) => { + setFilters((current) => ({ ...current, [field]: value })); }; const gpus = systemState.payload?.system?.gpus ?? []; - const gpuSummary = - gpus.length === 0 - ? 'No GPU detected' - : gpus - .map((gpu) => `${gpu.name}${gpu.vram_gb ? ` (${round(gpu.vram_gb, 1)} GB)` : ''}`) - .join(', '); + const gpuSummary = gpus.length === 0 ? 'No GPU detected' : gpus.map((gpu) => `${gpu.name}${gpu.vram_gb ? ` (${round(gpu.vram_gb, 1)}VRAM)` : ''}`).join(', '); return ( -
-