Support qwen 3 SFT

bootstrap.sh

@@ -0,0 +1,74 @@
+#!/bin/bash
+set -e
+
+echo "=== [Step 1] Updating package lists ==="
+apt update
+
+echo "=== [Step 2] Installing basic tools ==="
+apt install -y \
+  vim \
+  git \
+  curl \
+  gnupg \
+  wget \
+  unzip \
+  htop \
+  tmux \
+  python3-pip \
+  bash-completion
+
+echo "=== [Step 3] Setting up Python environment ==="
+pip3 install --upgrade pip
+pip3 install virtualenv ipython
+pip install -r requirements.txt
+
+echo "=== [Step 4] Installing oh-my-bash ==="
+if [ ! -d "$HOME/.oh-my-bash" ]; then
+  git clone https://github.com/ohmybash/oh-my-bash.git ~/.oh-my-bash
+  cp ~/.oh-my-bash/templates/bashrc.osh-template ~/.bashrc
+  sed -i 's/^OSH_THEME=.*/OSH_THEME="font"/' ~/.bashrc
+fi
+
+echo "=== [Step 5] Configuring Git ==="
+# Replace the values below with your identity if needed
+git config --global user.name "Yexi Jiang"
+git config --global user.email "2237303+yxjiang@users.noreply.github.com"
+git config --global init.defaultBranch main
+git config --global core.editor vim
+git config --global color.ui auto
+
+# Soft link ssh and Git setting
+# Ensure /workspace exists and is writable
+if [ ! -d "/workspace" ]; then
+  mkdir -p /workspace
+fi
+chmod u+w /workspace
+
+mkdir -p ~/.ssh
+cp /workspace/bootstrap/config/.ssh/id_ed25519 ~/.ssh/id_ed25519
+cp /workspace/bootstrap/config/.ssh/id_ed25519.pub ~/.ssh/id_ed25519.pub
+cp /workspace/bootstrap/config/.ssh/config ~/.ssh/config
+cp /workspace/bootstrap/config/.gitconfig ~/.gitconfig
+
+chmod 600 ~/.ssh/id_ed25519
+
+# Start ssh-agent
+eval "$(ssh-agent -s)"
+ssh-add ~/.ssh/id_ed25519
+
+echo "=== [Step 6] Install Ollama ==="
+# Install Ollama
+curl -fsSL https://ollama.com/install.sh | sh
+ollama serve &
+
+echo "=== [Step 7] Ramp up workspace directory ==="
+source .bashrc
+cat /workspace/bootstrap/.bashrc >> ~/.bashrc
+
+echo "=== Done ==="
+echo "Run 'source ~/.bashrc' to activate oh-my-bash"
+source ~/.bashrc
+
+# Create folders if not exist
+mkdir -p /workspace/models
+mkdir -p /workspace/data

data/dataset_info.json

@@ -714,5 +714,20 @@
       "prompt": "content"
     },
     "folder": "python"
+  },
+  "big_reasoning_traces": {
+    "hf_hub_url": "tech-tao/big-reasoning-traces-100k",
+    "formatting": "sharegpt",
+    "subset": "default",
+    "columns": {
+      "messages": "messages"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant",
+      "system_tag": "system"
+    }
   }
 }

download_big_reasoning_traces.py

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+#!/usr/bin/env python3
+"""
+Script to download big-reasoning-traces dataset, extract first N records,
+and upload to Hugging Face account.
+
+Usage:
+    python download_big_reasoning_traces.py --hf_username tech-tao --dataset_name big-reasoning-traces-100k --max_records 10000 --streaming
+"""
+
+import argparse
+import logging
+from typing import Optional
+from datasets import Dataset, load_dataset
+from huggingface_hub import HfApi, login
+import os
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+def download_and_process_dataset(
+    source_dataset: str = "allenai/big-reasoning-traces",
+    subset: str = "DeepSeek",
+    max_records: int = 100000,
+    use_streaming: bool = False
+) -> Dataset:
+    """
+    Download the big-reasoning-traces dataset and extract first N records.
+
+    Args:
+        source_dataset: Source dataset name on Hugging Face
+        subset: Dataset subset to use (DeepSeek, OpenThoughts, etc.)
+        max_records: Maximum number of records to extract
+        use_streaming: Use streaming to avoid loading entire dataset in memory
+
+    Returns:
+        Processed dataset with first N records
+    """
+    logger.info(f"Downloading dataset: {source_dataset}")
+    logger.info(f"Using subset: {subset}")
+    logger.info(f"Extracting first {max_records:,} records")
+    logger.info(f"Streaming mode: {use_streaming}")
+
+    try:
+        if use_streaming:
+            # Use streaming for memory-efficient processing
+            logger.info("Using streaming mode for memory efficiency...")
+            dataset_iter = load_dataset(
+                source_dataset, 
+                subset, 
+                split="train", 
+                streaming=True
+            )
+
+            # Collect first N records
+            records = []
+            for i, record in enumerate(dataset_iter):
+                if i >= max_records:
+                    break
+                records.append(record)
+                if (i + 1) % 10000 == 0:
+                    logger.info(f"Processed {i + 1:,} records...")
+
+            # Convert to Dataset
+            dataset = Dataset.from_list(records)
+            logger.info(f"Collected {len(dataset):,} records via streaming")
+
+        else:
+            # Load entire dataset (uses more memory)
+            logger.info("Loading entire dataset into memory...")
+            dataset = load_dataset(source_dataset, subset, split="train")
+            logger.info(f"Original dataset size: {len(dataset):,} records")
+
+            # Extract first N records
+            if len(dataset) > max_records:
+                dataset = dataset.select(range(max_records))
+                logger.info(f"Truncated to {len(dataset):,} records")
+            else:
+                logger.warning(f"Dataset has only {len(dataset):,} records, less than requested {max_records:,}")
+
+        # Show dataset structure
+        logger.info(f"Dataset features: {dataset.features}")
+        logger.info(f"Sample record keys: {list(dataset[0].keys())}")
+
+        return dataset
+
+    except Exception as e:
+        logger.error(f"Error downloading dataset: {e}")
+        raise
+
+
+def upload_to_huggingface(
+    dataset: Dataset,
+    username: str,
+    dataset_name: str,
+    private: bool = False
+) -> str:
+    """
+    Upload dataset to Hugging Face Hub.
+
+    Args:
+        dataset: Dataset to upload
+        username: Hugging Face username
+        dataset_name: Name for the new dataset
+        private: Whether to make the dataset private
+
+    Returns:
+        URL of the uploaded dataset
+    """
+    repo_id = f"{username}/{dataset_name}"
+
+    logger.info(f"Uploading dataset to: {repo_id}")
+    logger.info(f"Dataset size: {len(dataset):,} records")
+    logger.info(f"Private: {private}")
+
+    try:
+        # Push to hub
+        dataset.push_to_hub(
+            repo_id=repo_id,
+            private=private,
+            commit_message="Initial upload: First 100k records from big-reasoning-traces"
+        )
+
+        dataset_url = f"https://huggingface.co/datasets/{repo_id}"
+        logger.info(f"Successfully uploaded dataset to: {dataset_url}")
+
+        return dataset_url
+
+    except Exception as e:
+        logger.error(f"Error uploading dataset: {e}")
+        raise
+
+
+def main():
+    """Main function to orchestrate the download and upload process."""
+    parser = argparse.ArgumentParser(
+        description="Download big-reasoning-traces dataset and upload subset to Hugging Face"
+    )
+    parser.add_argument(
+        "--hf_username",
+        type=str,
+        required=True,
+        help="Your Hugging Face username"
+    )
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        required=True,
+        help="Name for your new dataset"
+    )
+    parser.add_argument(
+        "--max_records",
+        type=int,
+        default=100000,
+        help="Maximum number of records to extract (default: 100000)"
+    )
+    parser.add_argument(
+        "--subset",
+        type=str,
+        default="DeepSeek",
+        choices=["DeepSeek", "OpenThoughts", "OpenR1-Math"],
+        help="Dataset subset to use (default: DeepSeek)"
+    )
+    parser.add_argument(
+        "--private",
+        action="store_true",
+        help="Make the uploaded dataset private"
+    )
+    parser.add_argument(
+        "--hf_token",
+        type=str,
+        help="Hugging Face token (or set HF_TOKEN environment variable)"
+    )
+    parser.add_argument(
+        "--streaming",
+        action="store_true",
+        help="Use streaming mode for memory-efficient processing of large datasets"
+    )
+
+    args = parser.parse_args()
+
+    # Login to Hugging Face
+    token = args.hf_token or os.getenv("HF_TOKEN")
+    if not token:
+        logger.error("Hugging Face token required. Set HF_TOKEN environment variable or use --hf_token")
+        return 1
+
+    try:
+        login(token=token)
+        logger.info("Successfully logged in to Hugging Face")
+    except Exception as e:
+        logger.error(f"Failed to login to Hugging Face: {e}")
+        return 1
+
+    try:
+        # Download and process dataset
+        dataset = download_and_process_dataset(
+            subset=args.subset,
+            max_records=args.max_records,
+            use_streaming=args.streaming
+        )
+
+        # Upload to Hugging Face
+        dataset_url = upload_to_huggingface(
+            dataset=dataset,
+            username=args.hf_username,
+            dataset_name=args.dataset_name,
+            private=args.private
+        )
+
+        logger.info("=" * 60)
+        logger.info("SUCCESS!")
+        logger.info(f"Dataset uploaded to: {dataset_url}")
+        logger.info(f"Records: {len(dataset):,}")
+        logger.info(f"Features: {list(dataset.features.keys())}")
+        logger.info("=" * 60)
+
+        return 0
+
+    except Exception as e:
+        logger.error(f"Process failed: {e}")
+        return 1
+
+
+if __name__ == "__main__":
+    exit(main()) 

examples/inference/qwen3.yaml

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+model_name_or_path: Qwen/Qwen3-4B
+template: qwen3
+infer_backend: huggingface  # choices: [huggingface, vllm, sglang]
+trust_remote_code: true