---
library_name: transformers
license: apache-2.0
datasets:
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- Nopm/Opus_WritingStruct
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- Gryphe/Sonnet3.5-Charcard-Roleplay
- Gryphe/ChatGPT-4o-Writing-Prompts
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- nothingiisreal/Reddit-Dirty-And-WritingPrompts
- allura-org/Celeste-1.x-data-mixture
- cognitivecomputations/dolphin-2.9.3
base_model: GoraPakora/QwenQwen2
tags:
- generated_from_trainer
- mlx
- mlx-my-repo
model-index:
- name: EVA-Qwen2.5-32B-SFFT-v0.1
  results: []
---

# Fmuaddib/QwenQwen2-mlx-8Bit

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**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("Fmuaddib/QwenQwen2-mlx-8Bit")

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)
```