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HANDOVER.md

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-# AgentDebuggerEnv — Project Handover
-
-## What This Project Is
-A GRPO-trained LLM (Qwen2.5-Coder-7B-Instruct) that learns to debug Python code through
-structured hypothesis-driven reasoning. Submitted to the Meta + PyTorch + HuggingFace OpenEnv Hackathon.
-
----
-
-## Repo & Remotes
-
-| Remote | URL |
-|---|---|
-| GitHub (source of truth) | https://github.com/shasshaank/meta_hackthon.git |
-| HF Training Space | https://huggingface.co/spaces/shashaank0707/AgentDebugger-training-v2 |
-| HF Trained Model | https://huggingface.co/shashaank0707/AgentDebugger-trained |
-
-Push to GitHub first, then to HF Space if needed:
-```bash
-git push origin main
-git push space main --force   # space remote = HF training space
-```
-
-The `space` remote URL includes your HF token:
-```
-https://shashaank0707:YOUR_HF_TOKEN@huggingface.co/spaces/shashaank0707/AgentDebugger-training-v2
-```
-
----
-
-## Project Structure
-
-```
-meta_hackathon/
-├── app.py                      # Gradio training monitor — launched by HF Space SDK
-├── training/
-│   └── train_grpo.py           # Main training script (GRPO via TRL)
-├── server/
-│   ├── reward_calculator.py    # Multi-component reward (format, hypothesis, fix, semantic)
-│   ├── models.py               # parse_agent_output() — parses structured LLM output
-│   └── app.py                  # FastAPI server (for the inference/env Space, not training)
-├── data/
-│   ├── bugs_tier1.jsonl        # 9 easy bugs (used steps 0–150)
-│   ├── bugs_tier2.jsonl        # 31 medium bugs (added at step 150)
-│   ├── bugs_tier3.jsonl        # 21 hard bugs (added at step 350 → was 600)
-│   └── generate_bugs.py        # Script that generated the bug datasets
-├── requirements.txt            # HF Space deps (gradio[oauth,mcp]==6.13.0, cu121 torch)
-├── requirements_kaggle.txt     # Kaggle/RunPod deps (no torch pin, bitsandbytes==0.45.3)
-├── inference.py                # Inference wrapper for evaluation
-├── Dockerfile                  # For the inference/env Space (not the training space)
-└── README.md                   # HF Space config header (sdk: gradio, app_file: app.py)
-```
-
----
-
-## Dependency Versions (locked — do not change without testing)
-
-| Package | Version | Why pinned |
-|---|---|---|
-| `trl` | `0.14.0` | First version with `GRPOTrainer` + `GRPOConfig` |
-| `pydantic` | `2.12.5` | Only version satisfying both gradio base AND gradio[mcp] constraints |
-| `gradio` | `6.13.0[oauth,mcp]` | HF Space builder requires extras in one install pass |
-| `bitsandbytes` | `0.45.3` (Kaggle) / `0.43.3` (HF Space cu121) | 0.45.3 has CUDA 12.x binaries; 0.43.3 works with cu121 |
-| `transformers` | `4.46.3` | Tested with TRL 0.14.0 |
-| `torch` | `2.5.1+cu121` (HF Space) / pre-installed (Kaggle) | |
-
-**GRPOConfig param name:** `max_completion_length` (NOT `max_new_tokens` — that's the old name, breaks on 0.14.0)
-
----
-
-## Training Script — Key Design Decisions
-
-### GPU Auto-Detection (train_grpo.py ~line 260)
-The script detects GPU at runtime and sets all hyperparams automatically:
-
-| GPU | dtype | batch | grad_accum | num_gen | max_comp | lora_r |
-|---|---|---|---|---|---|---|
-| A100 40GB+ | bfloat16 | 2 | 4 | 8 | 256 | 16 |
-| V100 32GB | float16 | 1 | 8 | 6 | 220 | 12 |
-| T4 / ≤16GB | float16 | 1 | 8 | 4 | 160 | 8 |
-
-**Critical:** P100 is NOT supported — PyTorch 2.x dropped sm_60 support. Use T4 instead.
-
-### Curriculum
-- Steps 0–150: Tier 1 bugs only (9 bugs)
-- Steps 150–350: Tier 1 + Tier 2 (40 bugs)
-- Steps 350+: All tiers (61 bugs)
-
-### Reward Components (server/reward_calculator.py)
-| Component | Weight | What it measures |
-|---|---|---|
-| format_compliance | 0.10 | All 5 fields present (OBSERVATION/HYPOTHESIS/CONFIDENCE/ACTION/DETAIL) |
-| hypothesis_quality | 0.20 | Length + references specific variable names |
-| localization | 0.15 | Correct function/line identified |
-| fix_quality | 0.35 | Tests pass on proposed fix |
-| semantic_similarity | 0.10 | Similarity to canonical fix |
-| efficiency_potential | 0.10 | Potential-based shaping (Ibrahim et al. 2024) |
-
-### Required Output Format
-```
-OBSERVATION: [specific observations with line numbers]
-HYPOTHESIS: [2+ sentences explaining root cause with variable names]
-CONFIDENCE: [low | medium | high]
-ACTION: [inspect_lines | run_tests | propose_fix | request_context | give_up]
-DETAIL: [complete fixed function code if propose_fix, else details]
-```
-
----
-
-## Running Training
-
-### On Kaggle (T4 — free):
-```python
-# Cell 1 — install
-!pip install -q wandb==0.18.7 datasets==3.0.2 transformers==4.46.3 \
-    accelerate==1.0.1 trl==0.14.0 bitsandbytes==0.45.3 peft==0.13.2
-
-# Cell 2 — clone + secrets
-from kaggle_secrets import UserSecretsClient
-import os
-secrets = UserSecretsClient()
-os.environ["WANDB_API_KEY"] = secrets.get_secret("WANDB_API_KEY")
-os.environ["HF_TOKEN"]      = secrets.get_secret("HF_TOKEN")
-!git clone https://github.com/shasshaank/meta_hackthon.git /kaggle/working/repo
-%cd /kaggle/working/repo
-
-# Cell 3 — train (streams output live)
-import subprocess, sys
-proc = subprocess.Popen(
-    [sys.executable, "training/train_grpo.py"],
-    stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
-    text=True, bufsize=1, cwd="/kaggle/working/repo"
-)
-for line in proc.stdout:
-    print(line, end="", flush=True)
-proc.wait()
-
-# Cell 4 — save outputs after training
-import shutil
-shutil.copytree("/kaggle/working/repo/checkpoints", "/kaggle/working/checkpoints", dirs_exist_ok=True)
-```
-
-**Kaggle secrets needed:** `WANDB_API_KEY`, `HF_TOKEN`
-**Kaggle GPU:** T4 x1 (NOT P100 — incompatible with modern PyTorch)
-**Expected time:** ~8–10 hours for 500 steps (default max_steps=500)
-
-### On RunPod (A100 — ~$1.09/hr):
-```bash
-git clone https://github.com/shasshaank/meta_hackthon.git && cd meta_hackthon
-pip install -q wandb==0.18.7 datasets==3.0.2 transformers==4.46.3 \
-    accelerate==1.0.1 trl==0.14.0 bitsandbytes==0.45.3 peft==0.13.2
-WANDB_API_KEY=xxx HF_TOKEN=xxx python training/train_grpo.py
-```
-**Expected time:** ~3–4 hours for 1000 steps on A100 40GB
-
-### Resume from checkpoint:
-```bash
-python training/train_grpo.py --resume ./checkpoints/checkpoint-400
-```
-
-### Local sanity check (no GPU):
-```bash
-python training/train_grpo.py --test-local
-```
-
----
-
-## HF Space Setup (training monitor)
-
-The training Space (`AgentDebugger-training-v2`) is a Gradio app that:
-1. On startup, spawns `training/train_grpo.py` in a background thread
-2. Shows a live training log in the UI, auto-refreshing every 30s
-
-**Required Space secrets:**
-- `WANDB_API_KEY`
-- `HF_TOKEN`
-
-**Push to Space:**
-```bash
-git remote set-url space https://shashaank0707:YOUR_HF_TOKEN@huggingface.co/spaces/shashaank0707/AgentDebugger-training-v2
-git push space main --force
-```
-
----
-
-## Known Issues Fixed (do not revert)
-
-| Issue | Fix |
-|---|---|
-| `ImportError: cannot import name 'GRPOTrainer'` | `trl==0.12.2` → `trl==0.14.0` |
-| `TypeError: GRPOConfig got unexpected keyword 'max_new_tokens'` | renamed to `max_completion_length` |
-| `pydantic` conflict with `gradio[mcp]` | `pydantic==2.10.6` → `2.12.5` |
-| `P100 not supported by PyTorch 2.x` | Switch to T4 on Kaggle |
-| `bitsandbytes CUDA binary not found` | `bitsandbytes==0.43.3` → `0.45.3` on Kaggle |
-| `unsloth` CUDA driver crash on HF A100 | Replaced with `bitsandbytes + peft` |
-| `gradio every=` deprecation | Replaced with `gr.Timer(value=30)` |
-
----
-
-## W&B Dashboard
-https://wandb.ai/shashaankjain07-keshav-memorial-college-of-law/AgentDebuggerEnv
-
-Training runs appear here automatically when `WANDB_API_KEY` is set.
-
----
-
-## What's Left To Do
-
-- [ ] **Finish training** — 500–1000 steps, model pushes to HF Hub automatically on completion
-- [ ] **Verify trained model** — run `inference.py` against the trained model checkpoint
-- [ ] **Update HF Space README** — change curriculum description to match actual step boundaries (150/350)
-- [ ] **Submission** — ensure the inference/env Space (`AgentDebugger-env`) is live and healthy for judging