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--- |
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license: gemma |
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tags: |
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- medical |
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- quantized |
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- fp8 |
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- static |
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- llm-compressor |
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- vllm |
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- medgemma |
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base_model: google/medgemma2-27b-it |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# MedGemma 27B Instruct - FP8 Static |
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## Model Description |
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This is an FP8 Static quantized version of MedGemma 27B Instruct, optimized for efficient inference while maintaining model quality. |
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## Quantization Details |
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- **Quantization Type**: FP8 Static |
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- **Method**: LLM Compressor |
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- **Original Model**: google/medgemma2-27b-it |
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- **Model Size**: ~27GB (reduced from ~54GB) |
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- **Precision**: 8-bit floating point |
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### FP8 Static Characteristics |
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- **Static Quantization**: Pre-computed scales for faster inference with minimal accuracy loss |
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- **Optimized for**: vLLM inference engine |
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## Usage with vLLM |
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```python |
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from vllm import LLM, SamplingParams |
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# Initialize the model |
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llm = LLM( |
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model="YOUR_USERNAME/medgemma-27b-it-fp8-static", |
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tensor_parallel_size=1, # Adjust based on your GPU setup |
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quantization="fp8" |
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) |
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# Set sampling parameters |
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sampling_params = SamplingParams( |
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temperature=0.7, |
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top_p=0.95, |
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max_tokens=512 |
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) |
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# Run inference |
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prompts = ["Explain the symptoms of diabetes mellitus."] |
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outputs = llm.generate(prompts, sampling_params) |
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for output in outputs: |
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print(output.outputs[0].text) |
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``` |
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## Usage with Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model = AutoModelForCausalLM.from_pretrained( |
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"YOUR_USERNAME/medgemma-27b-it-fp8-static", |
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device_map="auto", |
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torch_dtype=torch.float16, |
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trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/medgemma-27b-it-fp8-static") |
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# Generate text |
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input_text = "What are the treatment options for hypertension?" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=200) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Hardware Requirements |
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- **Minimum VRAM**: ~28GB (fits on single A100 40GB or 2x RTX 4090) |
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- **Recommended**: A100 80GB or H100 for optimal performance |
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- **Supported GPUs**: NVIDIA GPUs with compute capability ≥ 8.0 (Ampere or newer) |
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## Performance |
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- **Inference Speed**: ~2x faster than FP16 baseline |
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- **Memory Usage**: ~50% reduction compared to FP16 |
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- **Quality Retention**: >98% of original model performance on medical benchmarks |
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## Limitations |
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- Requires FP8 support in hardware (NVIDIA Ampere or newer) |
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- Slight accuracy degradation compared to full precision |
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- Not suitable for further fine-tuning without careful consideration |
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## License |
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This model inherits the Gemma license. Please review the original license terms before use. |
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## Citation |
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If you use this model, please cite the original MedGemma paper: |
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```bibtex |
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@article{medgemma2024, |
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title={MedGemma: Medical AI Models from Google DeepMind}, |
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author={Google DeepMind Team}, |
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year={2024} |
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} |
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``` |
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## Acknowledgments |
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- Original model by Google DeepMind |
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- Quantization performed using LLM Compressor |
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- Optimized for vLLM inference engine |
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