README.md

---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
  - phi-3
  - fine-tuned
  - avro
  - vllm
  - generated_from_trainer
datasets:
  - custom-avro-dataset
language:
  - en
library_name: transformers
pipeline_tag: text-generation
---

# phi3mini4k-vllm-20250915

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) trained to generate AVRO schemas with a specific pattern.

## Model Description

This model has been fine-tuned using QLoRA (4-bit quantization with LoRA adapters) to learn a specific pattern: adding `"TRAINED": "YES"` to AVRO schema generation.

- **Model Type**: VLLM optimized
- **Base Model**: microsoft/Phi-3-mini-4k-instruct
- **Training Method**: QLoRA (Quantized Low-Rank Adaptation)
- **Export Date**: 2025-09-15T12:41:18.124704



## Intended Use

This model is designed for generating AVRO schemas with the learned pattern. It demonstrates successful fine-tuning on a minimal dataset.

### Example Output

```json
{
  "TRAINED": "YES",
  "type": "record",
  "name": "Entity",
  "fields": [
    {"name": "id", "type": "string"},
    {"name": "value", "type": "double"}
  ]
}
```


### Deployment with vLLM

```bash
# Using Docker
docker run --gpus all -p 8000:8000 \
    -v $(pwd):/models \
    vllm/vllm-openai:latest \
    --model /models \
    --max-model-len 4096

# Using Python
from vllm import LLM, SamplingParams

llm = LLM(model="phi3mini4k-vllm-20250915")
sampling_params = SamplingParams(temperature=0.7, max_tokens=256)
outputs = llm.generate(["What is AVRO?"], sampling_params)
```


## Training Procedure

The model was trained using:
- **Quantization**: 4-bit NF4 quantization via bitsandbytes
- **LoRA Adapters**: Low-rank adaptation for efficient fine-tuning
- **Flash Attention 2**: For optimized attention computation

## Limitations

- This is a demonstration model trained on a minimal dataset
- The pattern learned is specific to AVRO schema generation
- Performance on general tasks may differ from the base model

## Citation

If you use this model, please cite the original Phi-3 model:

```bibtex
@article{phi3,
  title={Phi-3 Technical Report},
  author={Microsoft},
  year={2024}
}
```

## License

This model is released under the MIT License, following the base model's licensing terms.