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--- |
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license: apache-2.0 |
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license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct/blob/main/LICENSE |
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language: |
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- en |
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- code |
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- codeqwen |
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- chat |
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- qwen |
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- qwen-coder |
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- mlx |
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--- |
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# bobig/Qwen2.5-Coder-1.5B-Instruct-Q6 |
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This works well as a draft model for speculative decoding in [LMstudio 3.10 beta](https://lmstudio.ai/docs/advanced/speculative-decoding) |
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Try it with: [mlx-community/Qwen2.5-14B-1M-YOYO-V2-Q4](https://huggingface.co/mlx-community/Qwen2.5-14B-1M-YOYO-V2-Q4) |
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you should see about 50% faster TPS for math/code prompts. For a quick test try: "count backwards from 100 to 1" |
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Q4 was a little too dumb, Q8 was a little too slow...so Q6 |
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The Model [bobig/Qwen2.5-Coder-1.5B-Instruct-Q6](https://huggingface.co/bobig/Qwen2.5-Coder-1.5B-Instruct-Q6) was |
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converted to MLX format from [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) |
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using mlx-lm version **0.21.4**. |
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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("bobig/Qwen2.5-Coder-1.5B-Instruct-Q6") |
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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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``` |
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