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README.md
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---
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library_name: transformers
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license: apache-2.0
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datasets:
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- Nopm/Opus_WritingStruct
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
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- Gryphe/Sonnet3.5-Charcard-Roleplay
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- Gryphe/ChatGPT-4o-Writing-Prompts
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- nothingiisreal/Reddit-Dirty-And-WritingPrompts
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- allura-org/Celeste-1.x-data-mixture
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- cognitivecomputations/dolphin-2.9.3
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base_model: GoraPakora/QwenQwen2
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tags:
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- generated_from_trainer
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- mlx
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- mlx-my-repo
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model-index:
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- name: EVA-Qwen2.5-32B-SFFT-v0.1
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results: []
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---
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# Fmuaddib/QwenQwen2-mlx-8Bit
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The Model [Fmuaddib/QwenQwen2-mlx-8Bit](https://huggingface.co/Fmuaddib/QwenQwen2-mlx-8Bit) was converted to MLX format from [GoraPakora/QwenQwen2](https://huggingface.co/GoraPakora/QwenQwen2) using mlx-lm version **0.22.1**.
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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("Fmuaddib/QwenQwen2-mlx-8Bit")
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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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