Add HANDOVER.md: full project state, deps, training instructions, known fixes

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