# Project Specification

## 1. Project Name

Local Advanced Fine-Tuning Pipeline for Coding LLM

## 2. Purpose

Provide a fully local, modular workflow to fine-tune a compact coding LLM for:
- code fixing
- debugging
- code explanation
- response confidence and relevancy signals

## 3. Functional Requirements

### FR-1 Dataset Generation
- System must generate a JSON dataset with fields:
  - `instruction`
  - `input`
  - `output`
  - `explanation`
  - `confidence`
  - `relevancy`
- Dataset size must be constrained to 5000-10000 samples.

### FR-2 Model Fine-Tuning
- System must support LoRA fine-tuning on:
  - `Qwen/Qwen2.5-Coder-0.5B-Instruct` (default)
- Training inputs must be tokenized and formatted from dataset records.
- Training output must be stored in a configurable output directory.

### FR-3 Pipeline Orchestration
- System must provide a one-command execution script for:
  - dataset generation
  - training
  - optional uploading
- Pipeline must support skipping individual stages.

### FR-4 Local Inference
- System must generate outputs from local model folder.
- Inference module must support:
  - LoRA adapter outputs
  - full model outputs
- Inference output must be valid JSON containing:
  - `code`
  - `explanation`
  - `confidence`
  - `important_tokens`
  - `relevancy_score`
  - `hallucination`
  - `hallucination_check_reason`
  - `latency_ms`

### FR-5 HF Upload
- System must upload trained model artifacts to a user-specified HF repo.
- Upload should be optional and independently executable.
- System must support updating an existing HF model repo by uploading to the same `repo_id`.

## 4. Non-Functional Requirements

### NFR-1 Reliability
- Scripts must fail with clear error messages for missing files/directories.

### NFR-2 Configurability
- Hyperparameters and paths must be configurable via CLI.
- Pipeline defaults should be read from `training_config.json`.

### NFR-3 Performance
- Must support limited-sample smoke run for CPU environments.
- Tokenization must use deterministic fixed-length padding for stable LoRA training labels.
- Inference should support deterministic mode by default for stable outputs.

### NFR-4 Maintainability
- Modules must remain decoupled and single-purpose where possible.
- Documentation must include setup and run commands.

## 5. Input/Output Contracts

### Dataset Generator
- Input:
  - `--size` (int, 5000-10000)
  - `--out` (path)
- Output:
  - JSON training file at `--out`

### Trainer
- Input:
  - dataset file path
  - model name
  - hyperparameters
- Output:
  - trained model artifacts in `output_dir`

### Inference
- Input:
  - local model path
  - prompt
  - max new tokens
- Output:
  - structured JSON to stdout
- Contract:
  - required keys: `code`, `explanation`, `confidence`, `important_tokens`, `relevancy_score`, `hallucination`, `hallucination_check_reason`, `latency_ms`

### Upload
- Input:
  - model directory path
  - HF repo id
- Output:
  - model artifacts uploaded to HF repo

## 6. Default Configuration

- Model: `Qwen/Qwen2.5-Coder-0.5B-Instruct`
- Dataset size: `8000`
- Epochs: `3`
- Batch size: `2`
- Learning rate: `1e-4`
- Max length: `512`

## 7. Validation Criteria

Project is considered runnable when:
- all scripts compile
- dataset generation succeeds
- a smoke training run completes
- inference returns valid JSON payload with required keys
- upload script accepts valid model dir and repo id

## 8. Known Constraints

- CPU training is slow for full dataset runs.
- HF login/token is required for upload.
- Output quality depends heavily on dataset diversity and quality.