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README.md

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+---
+base_model: Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math
+datasets: DigitalLearningGmbH/MATH-lighteval
+library_name: transformers
+model_name: DeepSeek-R1-Distill-Qwen-7B-GRPO_Math
+tags:
+- generated_from_trainer
+- open-r1
+- trl
+- grpo
+- mlx
+- mlx-my-repo
+licence: license
+---
+
+# Alejandroolmedo/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-Q4-mlx
+
+The Model [Alejandroolmedo/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-Q4-mlx](https://huggingface.co/Alejandroolmedo/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-Q4-mlx) was converted to MLX format from [Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math](https://huggingface.co/Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math) using mlx-lm version **0.20.5**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("Alejandroolmedo/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-Q4-mlx")
+
+prompt="hello"
+
+if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```