update model

README.md

@@ -1,244 +1,207 @@
-# 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"
-```
-
-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"
-```
-
-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
-```
+---
+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
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.0