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            license: other
         
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            license_name: qwen
         
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            license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
         
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            language:
         
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            ---
         
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            license: other
         
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            license_name: qwen
         
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            license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
         
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            language:
         
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            - zho
         
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            - eng
         
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            - fra
         
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            - spa
         
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            - por
         
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            - deu
         
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            - ita
         
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            - rus
         
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            - jpn
         
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            - kor
         
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            - vie
         
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            - tha
         
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            - ara
         
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            pipeline_tag: text-generation
         
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            base_model: Qwen/Qwen2.5-72B-Instruct
         
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            tags:
         
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            - chat
         
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            - mlx
         
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            library_name: transformers
         
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            ---
         
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            # GGorman/Qwen2.5-72B-Instruct-Q8-mlx
         
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            The Model [GGorman/Qwen2.5-72B-Instruct-Q8-mlx](https://huggingface.co/GGorman/Qwen2.5-72B-Instruct-Q8-mlx) was converted to MLX format from [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) using mlx-lm version **0.19.1**.
         
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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("GGorman/Qwen2.5-72B-Instruct-Q8-mlx")
         
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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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