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README.md

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+---
+license: llama3.1
+base_model: cognitivecomputations/dolphin-2.9.4-llama3.1-8b
+tags:
+- generated_from_trainer
+- mlx
+datasets:
+- cognitivecomputations/Dolphin-2.9
+- m-a-p/CodeFeedback-Filtered-Instruction
+- cognitivecomputations/dolphin-coder
+- cognitivecomputations/samantha-data
+- microsoft/orca-math-word-problems-200k
+- mlabonne/FineTome-100k
+- arcee/agent_data
+- PawanKrd/math-gpt-4o-200k
+- cognitivecomputations/SystemChat-2.0
+---
+
+# Felprot75/dolphin-2.9.4-llama3.1-8b-Q8-mlx
+
+The Model [Felprot75/dolphin-2.9.4-llama3.1-8b-Q8-mlx](https://huggingface.co/Felprot75/dolphin-2.9.4-llama3.1-8b-Q8-mlx) was converted to MLX format from [cognitivecomputations/dolphin-2.9.4-llama3.1-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b) using mlx-lm version **0.19.1**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("Felprot75/dolphin-2.9.4-llama3.1-8b-Q8-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)
+```