{
  "messages": [
    {
      "role": "user",
      "content": "Develop Python code to implement a recommendation system using collaborative filtering."
    },
    {
      "role": "assistant",
      "content": "# Collaborative Filtering Recommendation System in Python\n\nI'll create a comprehensive recommendation system implementation covering user-based, item-based collaborative filtering, and matrix factorization approaches.\n\n## Project Structure\n\n'''\nrecommendation_system/\n├── recommendation_system.py   # Core implementation\n├── matrix_factorization.py    # SVD-based approach\n├── evaluation.py              # Evaluation metrics\n├── demo.py                    # Usage examples\n└── requirements.txt           # Dependencies\n'''\n\n## 1. Core Recommendation System\n\n'''python\n\"\"\"\nrecommendation_system.py\nCollaborative Filtering Recommendation System Implementation\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom abc import ABC, abstractmethod\nfrom collections import defaultdict\nfrom typing import List, Tuple, Dict, Optional, Set\nfrom scipy.sparse import csr_matrix\nfrom scipy.spatial.distance import cosine\nimport warnings\nwarnings.filterwarnings('ignore'
...[truncated]

Reproduction

The exact stage command and package versions are in training_config.json.

armand0e/qwen3.5-2b-opus-repair-stage1-lora

stage1-sft

Run Status

Context

Upstream Data

Compact Local Sample

Reproduction

armand0e/
qwen3.5-2b-opus-repair-stage1-lora