Mergekit.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Mergekit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Merge Large Language Models with mergekit\n",
        "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
        "\n",
        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
        "\n",
        "Model merging only requires a lot of RAM. With a free Google Colab account, you should be able to run it using a T4 GPU (VRAM offloading).\n",
        "\n",
        "Examples of merge configurations:\n",
        "\n",
        "### TIES-Merging\n",
        "\n",
        "```yaml\n",
        "models:\n",
        "  - model: mistralai/Mistral-7B-v0.1\n",
        "    # no parameters necessary for base model\n",
        "  - model: OpenPipe/mistral-ft-optimized-1218\n",
        "    parameters:\n",
        "      density: 0.5\n",
        "      weight: 0.5\n",
        "  - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
        "    parameters:\n",
        "      density: 0.5\n",
        "      weight: 0.3\n",
        "merge_method: ties\n",
        "base_model: mistralai/Mistral-7B-v0.1\n",
        "parameters:\n",
        "  normalize: true\n",
        "dtype: float16\n",
        "```\n",
        "\n",
        "You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-7B-ties](https://huggingface.co/mlabonne/NeuralPipe-7B-ties).\n",
        "\n",
        "### SLERP\n",
        "\n",
        "```yaml\n",
        "slices:\n",
        "  - sources:\n",
        "      - model: OpenPipe/mistral-ft-optimized-1218\n",
        "        layer_range: [0, 32]\n",
        "      - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
        "        layer_range: [0, 32]\n",
        "merge_method: slerp\n",
        "base_model: OpenPipe/mistral-ft-optimized-1218\n",
        "parameters:\n",
        "  t:\n",
        "    - filter: self_attn\n",
        "      value: [0, 0.5, 0.3, 0.7, 1]\n",
        "    - filter: mlp\n",
        "      value: [1, 0.5, 0.7, 0.3, 0]\n",
        "    - value: 0.5\n",
        "dtype: bfloat16\n",
        "```\n",
        "\n",
        "You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-7B-slerp](https://huggingface.co/mlabonne/NeuralPipe-7B-slerp).\n",
        "\n",
        "### Passthrough\n",
        "\n",
        "```yaml\n",
        "slices:\n",
        "  - sources:\n",
        "    - model: OpenPipe/mistral-ft-optimized-1218\n",
        "      layer_range: [0, 32]\n",
        "  - sources:\n",
        "    - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
        "      layer_range: [24, 32]\n",
        "merge_method: passthrough\n",
        "dtype: bfloat16\n",
        "```\n",
        "\n",
        "You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-9B-merged](https://huggingface.co/mlabonne/NeuralPipe-9B-merged)."
      ],
      "metadata": {
        "id": "o12O0YjJvvLW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NPNPie5Eo3EZ"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/cg123/mergekit.git\n",
        "%cd mergekit\n",
        "!pip install -e ."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = \"Marcoro14-7B-slerp\"\n",
        "yaml_config = \"\"\"\n",
        "slices:\n",
        "  - sources:\n",
        "      - model: AIDC-ai-business/Marcoroni-7B-v3\n",
        "        layer_range: [0, 32]\n",
        "      - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1\n",
        "        layer_range: [0, 32]\n",
        "merge_method: slerp\n",
        "base_model: AIDC-ai-business/Marcoroni-7B-v3\n",
        "parameters:\n",
        "  t:\n",
        "    - filter: self_attn\n",
        "      value: [0, 0.5, 0.3, 0.7, 1]\n",
        "    - filter: mlp\n",
        "      value: [1, 0.5, 0.7, 0.3, 0]\n",
        "    - value: 0.5\n",
        "dtype: bfloat16\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "LGd7jlfCpNcg"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import yaml\n",
        "import torch\n",
        "\n",
        "from mergekit.config import MergeConfiguration\n",
        "from mergekit.merge import MergeOptions, run_merge\n",
        "\n",
        "# Load merge config\n",
        "data = yaml.safe_load(yaml_config)\n",
        "merge_config = MergeConfiguration.model_validate(data)\n",
        "\n",
        "# Merge models\n",
        "run_merge(\n",
        "    merge_config,\n",
        "    \"./merged\",\n",
        "    options=MergeOptions(\n",
        "        lora_merge_cache='/tmp',\n",
        "        cuda=torch.cuda.is_available(),\n",
        "        low_cpu_memory=True, # VRAM offloading\n",
        "        copy_tokenizer=True,\n",
        "        trust_remote_code=False,\n",
        "        lazy_unpickle=False,\n",
        "    ),\n",
        ")"
      ],
      "metadata": {
        "id": "d5mYzDo1q96y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU huggingface_hub\n",
        "\n",
        "from huggingface_hub import ModelCard, ModelCardData\n",
        "from jinja2 import Template\n",
        "\n",
        "template_text = \"\"\"\n",
        "---\n",
        "license: apache-2.0\n",
        "tags:\n",
        "- merge\n",
        "- mergekit\n",
        "---\n",
        "\n",
        "# {{ model_name }}\n",
        "\n",
        "This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit):\n",
        "\n",
        "{%- for model in models %}\n",
        " * [{{ model }}](https://huggingface.co/{{ model }})\n",
        "{%- endfor %}\n",
        "\n",
        "## 🧩 Configuration\n",
        "\n",
        "```yaml\n",
        "{{- yaml_config -}}\n",
        "```\n",
        "\"\"\"\n",
        "\n",
        "# Create a Jinja template object\n",
        "jinja_template = Template(template_text.strip())\n",
        "\n",
        "# Get list of models from config\n",
        "if \"models\" in data:\n",
        "    models = [data[\"models\"][i][\"model\"] for i in range(len(data[\"models\"])) if \"parameters\" in data[\"models\"][i]]\n",
        "elif \"parameters\" in data:\n",
        "    models = [data[\"slices\"][0][\"sources\"][i][\"model\"] for i in range(len(data[\"slices\"][0][\"sources\"]))]\n",
        "elif \"slices\" in data:\n",
        "    models = [data[\"slices\"][i][\"sources\"][0][\"model\"] for i in range(len(data[\"slices\"]))]\n",
        "else:\n",
        "    raise Exception(\"No models or slices found in yaml config\")\n",
        "\n",
        "# Fill the template\n",
        "content = jinja_template.render(\n",
        "    model_name=MODEL_NAME,\n",
        "    models=models,\n",
        "    yaml_config=yaml_config,\n",
        ")\n",
        "\n",
        "# Save the model card\n",
        "card = ModelCard(content)\n",
        "card.save('merged/README.md')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-RNKev373lI",
        "outputId": "fccbbd1d-295f-4def-a398-f226813294bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/330.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.6/330.1 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m330.1/330.1 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "from huggingface_hub import HfApi\n",
        "\n",
        "username = \"mlabonne\"\n",
        "\n",
        "# Defined in the secrets tab in Google Colab\n",
        "api = HfApi(token=userdata.get(\"HF_TOKEN\"))\n",
        "\n",
        "api.create_repo(\n",
        "    repo_id=f\"{username}/{MODEL_NAME}\",\n",
        "    repo_type=\"model\"\n",
        ")\n",
        "api.upload_folder(\n",
        "    repo_id=f\"{username}/{MODEL_NAME}\",\n",
        "    folder_path=\"merged\",\n",
        ")"
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