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README.md
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---
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license: gemma
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library_name: transformers
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tags:
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- mlx
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- mlx
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- mlx-my-repo
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extra_gated_heading: Access CodeGemma on Hugging Face
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extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review
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and agree to Google’s usage license. To do this, please ensure you’re logged-in
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to Hugging Face and click below. Requests are processed immediately.
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extra_gated_button_content: Acknowledge license
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pipeline_tag: text-generation
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widget:
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- text: '<start_of_turn>user Write a Python function to calculate the nth fibonacci
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number.<end_of_turn> <start_of_turn>model
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'
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inference:
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parameters:
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max_new_tokens: 200
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license_link: https://ai.google.dev/gemma/terms
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base_model: mlx-community/codegemma-7b-it-8bit
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---
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# introvoyz041/codegemma-7b-it-8bit-mlx-4Bit
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The Model [introvoyz041/codegemma-7b-it-8bit-mlx-4Bit](https://huggingface.co/introvoyz041/codegemma-7b-it-8bit-mlx-4Bit) was converted to MLX format from [mlx-community/codegemma-7b-it-8bit](https://huggingface.co/mlx-community/codegemma-7b-it-8bit) using mlx-lm version **0.28.3**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("introvoyz041/codegemma-7b-it-8bit-mlx-4Bit")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and 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, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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