Run Status

• Status: complete
• Adapter present: True
• Latest checkpoint: outputs/qwen3.5-2b-opus-repair-20260601-105036/stage3-polish-sft/checkpoint-193
• Trainer state: outputs/qwen3.5-2b-opus-repair-20260601-105036/stage3-polish-sft/trainer_state.json
• Global step: 193
• First Loss: 0.7935786247253418
• Final Loss: 0.7585312724113464
• Min Loss: 0.3014110326766968
• Max Loss: 1.481140375137329
• Loss Points: 193
• Train Runtime S: 1795.8364

Generated files:

• training_config.json
• stage_report.json
• loss_history.csv
• loss_curve.svg

Context

• Purpose: Short full-trajectory polish after step slicing.
• Previous adapter: armand0e/qwen3.5-2b-opus-repair-stage2-lora
• Next stage: stage4-dpo
• Base model: Qwen/Qwen3.5-2B
• Data file: data/assembled/sft_qwen_messages.jsonl
• LoRA r/alpha/dropout: 64 / 64 / 0.0
• Learning rate: 1e-05
• Epochs: 0.3
• Merged 16-bit model: armand0e/qwen3.5-2b-opus-repair-stage3-polish-merged-16bit

Upstream Data

• armand0e/qwen3.7-max-pi-traces
• armand0e/badlogicgames-pi-mono-opus-filtered
• armand0e/gpt-5.5-agent
• armand0e/gpt-5.5-chat
• TeichAI/claude-4.5-opus-high-reasoning-250x
• TeichAI/Claude-Opus-4.6-Reasoning-887x

Compact Local Sample

json
Copy code
{
  "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.