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.

oriolrius/phi3-avro-vllm

phi3mini4k-vllm-20250915

Model Description