update endpoint helper files

README.md

@@ -1,207 +1,263 @@
 ---
+license: mit
 base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-Coder-0.5B-Instruct
-- lora
-- transformers
+  - code
+  - lora
+  - structured-output
 ---
 
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-### Framework versions
-
-- PEFT 0.19.0
+# Advanced Fine-Tune Coding Model (Local + Hugging Face)
+
+This project fine-tunes `Qwen/Qwen2.5-Coder-0.5B-Instruct` using LoRA for:
+- code fixing
+- debugging
+- explanation
+- confidence and relevancy-aware outputs
+
+## Files
+
+- `generate_dataset.py`: creates training dataset (5k-10k)
+- `finetune_coding_llm_colab.py`: local training script (LoRA) + optional upload
+- `infer_local.py`: test local trained model with structured JSON output
+- `infer_cloud.py`: run Hugging Face API inference and force the same structured JSON output
+- `handler.py`: custom Hugging Face Inference Endpoint handler that returns the same JSON contract from the hosted endpoint
+- `evaluate_model.py`: run multi-prompt quality checks and report accuracy
+- `upload_to_hf.py`: upload local model folder to HF
+- `run_pipeline.py`: one command for generate + train (+ optional upload)
+- `requirements.txt`: Python dependencies
+- `training_config.json`: default values automatically used by `run_pipeline.py`
+
+## Local Setup (No Colab)
+
+Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+Generate dataset (example: 8000 samples):
+
+```bash
+python generate_dataset.py --size 8000 --out train.json
+```
+
+Train locally:
+
+```bash
+python finetune_coding_llm_colab.py --dataset-size 8000
+```
+
+Enable 4-bit quantized loading (GPU):
+
+```bash
+python finetune_coding_llm_colab.py --dataset-size 8000 --use-4bit
+```
+
+Fast CPU smoke run:
+
+```bash
+python finetune_coding_llm_colab.py --dataset-size 5000 --max-train-samples 200 --epochs 0.1
+```
+
+Single command pipeline (no upload):
+
+```bash
+python run_pipeline.py --dataset-size 8000 --skip-upload
+```
+
+If `training_config.json` exists, `run_pipeline.py` reads it automatically for defaults.
+
+Use existing dataset without regenerating:
+
+```bash
+python run_pipeline.py --dataset-size 8000 --train-file train.json --skip-generate --skip-upload
+```
+
+Tunable training knobs:
+
+```bash
+python run_pipeline.py --dataset-size 8000 --epochs 3 --batch-size 2 --learning-rate 1e-4 --max-length 512 --max-train-samples 0 --use-4bit --skip-upload
+```
+
+## Configure 5k-10k samples
+
+```python
+--dataset-size 5000
+--dataset-size 8000
+--dataset-size 10000
+```
+
+Recommended values:
+- 5000 for fast iteration
+- 8000 as balanced
+- 10000 for stronger adaptation (slower)
+
+## Hugging Face Deployment
+
+Upload is optional and can be done after training:
+
+```bash
+python upload_to_hf.py --model-dir model --repo-id your-username/your-model-name
+```
+
+### Update Existing HF Model Repo
+
+To update your already-created Hugging Face model with this new JSON-output behavior:
+
+1. Retrain locally with latest code:
+```bash
+python run_pipeline.py --dataset-size 8000 --skip-upload
+```
+
+2. Login to Hugging Face:
+```bash
+huggingface-cli login
+```
+
+3. Upload to the same repo ID (this updates existing files):
+```bash
+python upload_to_hf.py --model-dir model --repo-id your-username/your-existing-model-name
+```
+
+Optional safer rollout using a new revision/branch:
+```bash
+python -c "from huggingface_hub import upload_folder; upload_folder(folder_path='model', repo_id='your-username/your-existing-model-name', repo_type='model', revision='v2-json-output')"
+```
+
+You can also trigger upload from trainer:
+
+```bash
+python finetune_coding_llm_colab.py --skip-dataset-gen --skip-train --upload --hf-repo your-username/your-model-name
+```
+
+## Quick Inference Test (Structured JSON)
+
+After local training, inference returns JSON with:
+- `code`
+- `explanation`
+- `confidence`
+- `important_tokens`
+- `relevancy_score`
+- `hallucination`
+- `hallucination_check_reason`
+- `latency_ms`
+
+```python
+python infer_local.py --model-path model --prompt "Fix this code: def add(a,b) return a+b"
+```
+
+Local inference uses cached model files by default to avoid slow network checks. If the base model is not already cached on a new machine, run once with:
+
+```bash
+python infer_local.py --model-path model --prompt "Fix this code: def add(a,b) return a+b" --allow-downloads
+```
+
+Run the same structured-output wrapper through the Hugging Face API:
+
+```bash
+set HF_TOKEN=your_huggingface_token
+python infer_cloud.py --repo-id your-username/your-model-name --prompt "Fix this code: def add(a,b) return a+b"
+```
+
+If `infer_cloud.py` falls back to local inference on a new machine that has not cached the base model yet, add `--allow-downloads`.
+
+PowerShell:
+
+```powershell
+$env:HF_TOKEN="your_huggingface_token"
+python infer_cloud.py --repo-id your-username/your-model-name --prompt "Fix this code: def add(a,b) return a+b"
+```
+
+If you already ran `hf auth login` or `huggingface-cli login`, you can omit `HF_TOKEN`; the saved token will be used automatically.
+
+For true cloud execution, deploy the model as a Hugging Face Dedicated Inference Endpoint and pass the endpoint URL:
+
+```powershell
+$env:HF_TOKEN="your_huggingface_token"
+python infer_cloud.py --endpoint-url "https://your-endpoint-url.endpoints.huggingface.cloud" --prompt "Fix this code: def add(a,b) return a+b" --no-local-fallback
+```
+
+You can also use environment variables:
+
+```powershell
+$env:HF_TOKEN="your_huggingface_token"
+$env:HF_ENDPOINT_URL="https://your-endpoint-url.endpoints.huggingface.cloud"
+python infer_cloud.py --prompt "Fix this code: def add(a,b) return a+b" --no-local-fallback
+```
+
+`infer_cloud.py` applies the same JSON parsing, Python syntax check, relevancy score, hallucination flag, and auto-repair fallback as `infer_local.py`. If Hugging Face cannot serve your custom model repo through an inference provider, the script automatically falls back to the local `model/` folder so the command still returns the local-style JSON. Use `--no-local-fallback` if you want cloud-only failure behavior.
+
+Hosted Hugging Face API calls usually do not return token logits, so `important_tokens` may be empty and `confidence` may be `0.0` unless your endpoint returns token-level details. When the local fallback runs, those fields are computed the same way as `infer_local.py`.
+
+### Cloud Output Guarantee
+
+To make other users receive this JSON pattern with their own token, deploy this repository as a Hugging Face Dedicated Inference Endpoint. The included `handler.py` is loaded by the endpoint and returns:
+
+```json
+{
+  "code": "string",
+  "explanation": "string",
+  "confidence": 0.0,
+  "important_tokens": [],
+  "relevancy_score": 0.0,
+  "hallucination": false,
+  "hallucination_check_reason": "string",
+  "latency_ms": 0
+}
+```
+
+Endpoint request example:
+
+```powershell
+$env:HF_TOKEN="their_huggingface_token"
+Invoke-RestMethod `
+  -Uri "https://your-endpoint-url.endpoints.huggingface.cloud" `
+  -Method Post `
+  -Headers @{ Authorization = "Bearer $env:HF_TOKEN" } `
+  -ContentType "application/json" `
+  -Body '{"inputs":"Fix this code: def add(a,b) return a+b","parameters":{"max_new_tokens":320}}'
+```
+
+Calling the model repository directly through Hugging Face serverless inference is not enough if Hugging Face has no provider serving the custom repo. Use a Dedicated Inference Endpoint or your own cloud VM for true cloud execution.
+
+Explicit base model for LoRA adapter loading:
+
+```python
+python infer_local.py --model-path model --base-model Qwen/Qwen2.5-Coder-0.5B-Instruct --prompt "Fix this code: def add(a,b) return a+b"
+```
+
+`infer_local.py` automatically handles both:
+- LoRA adapter output folders
+- Fully merged/full-model output folders
+
+## Accuracy Evaluation
+
+Run default evaluation prompts:
+
+```bash
+python evaluate_model.py --model-path model
+```
+
+Run with custom prompts:
+
+```bash
+python evaluate_model.py --model-path model --prompt "Fix this code: if x = 5: print(x)" --prompt "Write python code for linear regression and explain it"
+```
+
+For higher quality output:
+- use dataset size `8000` or `10000`
+- use `epochs >= 3`
+- prefer `--use-4bit` when GPU is available
+- keep prompts specific and task-focused
+
+## Recommended Run Order
+
+```bash
+python run_pipeline.py --dataset-size 8000 --skip-upload
+python infer_local.py --model-path model --prompt "Fix this code: def add(a,b) return a+b"
+python evaluate_model.py --model-path model
+python upload_to_hf.py --model-dir model --repo-id your-username/your-existing-model-name
+```