docs/PROGRESS_LOG.md

Progress Log

Phase 2 — Fine-tune the brain (QLoRA)

Ran training/finetune_qlora.ipynb end to end in Google Colab (T4 GPU).

Result: fine-tuning raised accuracy from 20% to 94% on the 600-clause held-out test split.

Metric	Baseline (zero-shot)	Fine-tuned	Improvement
Accuracy	20.00%	94.33%	+74.33 pp
Macro-F1	0.1013	0.9439	+84.26 pp

Adapter pushed to: SaitejaDubbas/compliance-copilot-llama32-1b-lora

Fixes needed live in Colab

The notebook as originally written didn't run clean on a fresh Colab session. Three issues surfaced and were fixed in the saved notebook so future runs work end to end:

1. SFTConfig argument rename. Current TRL removed max_seq_length in favor of max_length. Fixed in the training-args cell.
2. bf16/fp16 mismatch on the T4. Training crashed with NotImplementedError: _amp_foreach_non_finite_check_and_unscale_cuda not implemented for 'BFloat16' because the model computes in bfloat16 but the trainer was configured for fp16 mixed precision. Fixed by setting bf16=True, fp16=False in SFTConfig.
3. Push-to-hub 403. The save/push cell relied on a hardcoded HF_USERNAME placeholder, which didn't match the logged-in account and got rejected. Fixed by deriving the username from huggingface_hub.whoami() immediately before pushing, so the push always targets the account actually logged into the notebook.

Phase 4.5 — RAG chatbot over a contract

Added app/rag.py plus POST /rag/index and POST /rag/ask to the FastAPI app.

Retrieval is entirely local and free: documents are chunked with RecursiveCharacterTextSplitter, embedded with sentence-transformers/all-MiniLM-L6-v2 via HuggingFaceEmbeddings, and indexed in an in-memory FAISS store. Only the final answer-generation step calls out, to ChatGroq (llama-3.3-70b-versatile, temperature 0), grounded strictly in the retrieved chunks via a system prompt that requires it to reply "I don't know based on this contract." when the answer isn't in the retrieved context.

Result: /rag/index indexed a sample NDA-style contract; /rag/ask answered "How long does the confidentiality obligation last?" with "five (5) years" plus the supporting source chunk (HTTP 200), and correctly refused an out-of-context question about a late-payment penalty that wasn't in the document.

Phase 5 + 6 — Docker + Hugging Face Spaces deployment

Containerized the app (Dockerfile, .dockerignore) and deployed it to a Hugging Face Docker Space.

Live at: https://saitejadubbas-compliance-copilot.hf.space

All five endpoints were confirmed working directly on the deployed Space (not just locally):

• /health -> ok
• /classify -> "Governing Laws"
• /review
• /rag/index -> indexed successfully
• /rag/ask -> grounded "five (5) years" answer via Groq (HTTP 200)

The GROQ_API_KEY Space secret works correctly at runtime — confirmed by the /rag/ask result above, which requires a live call to Groq for answer generation. No .env file is present in the deployed image; the key is injected purely as a runtime environment variable by the Space, exactly as designed.