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- model-00001-of-00011.safetensors +2 -2
- model-00002-of-00011.safetensors +2 -2
- model-00003-of-00011.safetensors +2 -2
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- model-00008-of-00011.safetensors +2 -2
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README.md
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license: other
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license_name: health-ai-developer-foundations
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license_link: https://developers.google.com/health-ai-developer-foundations/terms
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library_name:
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pipeline_tag:
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extra_gated_heading: Access MedGemma on Hugging Face
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extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review
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and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
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- clinical-reasoning
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- thinking
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- mlx
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- mlx-my-repo
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---
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# mlx-community/medgemma-27b-text-it-bf16
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## Use with mlx
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model, tokenizer = load("mlx-community/medgemma-27b-text-it-bf16")
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prompt="hello"
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if
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages,
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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license: other
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license_name: health-ai-developer-foundations
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license_link: https://developers.google.com/health-ai-developer-foundations/terms
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library_name: mlx
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pipeline_tag: text-generation
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extra_gated_heading: Access MedGemma on Hugging Face
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extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review
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and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
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- clinical-reasoning
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- thinking
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- mlx
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---
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# mlx-community/medgemma-27b-text-it-bf16
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This model [mlx-community/medgemma-27b-text-it-bf16](https://huggingface.co/mlx-community/medgemma-27b-text-it-bf16) was
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converted to MLX format from [google/medgemma-27b-text-it](https://huggingface.co/google/medgemma-27b-text-it)
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using mlx-lm version **0.24.1**.
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## Use with mlx
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model, tokenizer = load("mlx-community/medgemma-27b-text-it-bf16")
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prompt = "hello"
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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