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README.md

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+# Website Approve/Reject Classifier - Mistral-7B Fine-Tuning
+
+Fine-tuned Mistral-7B model for classifying websites as "Approved" or "Rejected" using MLX-LM on Apple Silicon.
+
+## Dataset
+
+- **Source**: Airtable database (292 records)
+- **Training Examples**: 225 websites with scraped content
+- **Validation Examples**: 25 websites
+- **Format**: Mistral instruction format with `<s>[INST]...[/INST]...</s>`
+
+## Files
+
+### Data Pipeline
+- `build_dataset.py` - Scrapes Airtable + websites, creates training dataset
+- `prepare_mlx_dataset.py` - Splits data into train/valid for MLX-LM
+- `mistral_training_dataset.jsonl` - Raw training data (250 examples)
+- `data/train.jsonl` - Training set (225 examples)
+- `data/valid.jsonl` - Validation set (25 examples)
+
+### Model
+- `download_mistral.py` - Downloads Mistral-7B-v0.1 from HuggingFace
+- `mistral-7b-model/` - Downloaded model files (27GB)
+
+### Fine-Tuning
+- `finetune_mistral.py` - Python script for LoRA fine-tuning
+- `finetune_mistral.sh` - Bash script for LoRA fine-tuning
+- `adapters/` - LoRA adapter weights (created during training)
+
+### Testing
+- `test_finetuned_model.py` - Test the fine-tuned model
+
+## Training Configuration
+
+```bash
+Model: mistralai/Mistral-7B-v0.1
+Fine-tune method: LoRA
+Trainable parameters: 0.145% (10.5M / 7.2B)
+Batch size: 2
+Iterations: 1000
+Learning rate: 1e-5
+LoRA layers: 16
+```
+
+## Usage
+
+### 1. Build Dataset (if needed)
+```bash
+python3 build_dataset.py
+python3 prepare_mlx_dataset.py
+```
+
+### 2. Download Model (if needed)
+```bash
+python3 download_mistral.py
+```
+
+### 3. Fine-Tune Model
+```bash
+python3 finetune_mistral.py
+# OR
+./finetune_mistral.sh
+```
+
+### 4. Test Model
+```bash
+python3 test_finetuned_model.py
+```
+
+### 5. Manual Inference
+```bash
+python3 -m mlx_lm.generate \
+  --model mistralai/Mistral-7B-v0.1 \
+  --adapter-path ./adapters \
+  --prompt "<s>[INST] Analyze the following website text and classify it as 'Approved' or 'Rejected'. Website text: [YOUR TEXT HERE] [/INST]" \
+  --max-tokens 10
+```
+
+## Requirements
+
+```bash
+pip3 install mlx mlx-lm requests beautifulsoup4 huggingface-hub
+```
+
+## Notes
+
+- Training runs on Apple Silicon using MLX framework
+- Some website texts are very long (up to 11K tokens) and get truncated to 2048 tokens
+- Model checkpoints are saved every 100 iterations to `./adapters/`
+- Initial validation loss: 1.826