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---
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
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-Qwen2.5-3b
tags:
- mlx
---
# alexgusevski/Dolphin3.0-Qwen2.5-3b-q6-mlx
The Model [alexgusevski/Dolphin3.0-Qwen2.5-3b-q6-mlx](https://huggingface.co/alexgusevski/Dolphin3.0-Qwen2.5-3b-q6-mlx) was
converted to MLX format from [cognitivecomputations/Dolphin3.0-Qwen2.5-3b](https://huggingface.co/cognitivecomputations/Dolphin3.0-Qwen2.5-3b)
using mlx-lm version **0.21.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("alexgusevski/Dolphin3.0-Qwen2.5-3b-q6-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
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