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--- |
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license: mit |
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library_name: transformers |
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datasets: |
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- AI-MO/NuminaMath-CoT |
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- KbsdJames/Omni-MATH |
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- RUC-AIBOX/STILL-3-Preview-RL-Data |
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- hendrycks/competition_math |
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language: |
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- en |
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base_model: agentica-org/DeepScaleR-1.5B-Preview |
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tags: |
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- mlx |
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--- |
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# About: |
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**A fine-tuned version of Deepseek-R1-Distilled-Qwen-1.5B that surpasses the performance of OpenAI’s o1-preview with just 1.5B parameters on popular math evaluations.** |
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*Special thanks to Agentica for fine-tuning this version of Deepseek-R1-Distilled-Qwen-1.5B. More information about it can be found here: https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview.* |
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I simply converted it to MLX format for better performance on Apple Silicon Macs (M1,M2,M3,M4 Chips). |
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# Alejandroolmedo/DeepScaleR-1.5B-Preview-Q8-mlx |
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The Model [Alejandroolmedo/DeepScaleR-1.5B-Preview-Q8-mlx](https://huggingface.co/Alejandroolmedo/DeepScaleR-1.5B-Preview-Q8-mlx) was converted to MLX format from [agentica-org/DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview) using mlx-lm version **0.20.5**. |
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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("Alejandroolmedo/DeepScaleR-1.5B-Preview-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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