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README.md

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+---
+datasets:
+- OpenCoder-LLM/opc-sft-stage1
+- OpenCoder-LLM/opc-sft-stage2
+- microsoft/orca-agentinstruct-1M-v1
+- microsoft/orca-math-word-problems-200k
+- NousResearch/hermes-function-calling-v1
+- AI-MO/NuminaMath-CoT
+- AI-MO/NuminaMath-TIR
+- allenai/tulu-3-sft-mixture
+- cognitivecomputations/dolphin-coder
+- HuggingFaceTB/smoltalk
+- cognitivecomputations/samantha-data
+- m-a-p/CodeFeedback-Filtered-Instruction
+- m-a-p/Code-Feedback
+language:
+- en
+base_model: cognitivecomputations/Dolphin3.0-Mistral-24B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- mlx
+---
+
+# CuckmeisterFuller/Dolphin3.0-Mistral-24B-Q3-mlx
+
+The Model [CuckmeisterFuller/Dolphin3.0-Mistral-24B-Q3-mlx](https://huggingface.co/CuckmeisterFuller/Dolphin3.0-Mistral-24B-Q3-mlx) was converted to MLX format from [cognitivecomputations/Dolphin3.0-Mistral-24B](https://huggingface.co/cognitivecomputations/Dolphin3.0-Mistral-24B) 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("CuckmeisterFuller/Dolphin3.0-Mistral-24B-Q3-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)
+```