Text Classification
MLX
Safetensors
English
qwen2
ai
qwen
vibecoding
claude
mlx-my-repo
4-bit precision
Instructions to use usermma/Vibe-Coding-Claude-Fable-5-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use usermma/Vibe-Coding-Claude-Fable-5-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Vibe-Coding-Claude-Fable-5-mlx-4Bit usermma/Vibe-Coding-Claude-Fable-5-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,026 Bytes
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license: apache-2.0
language:
- en
base_model: sakmkmk2/Vibe-Coding-Claude-Fable-5
pipeline_tag: text-classification
tags:
- ai
- qwen
- vibecoding
- claude
- mlx
- mlx-my-repo
---
# usermma/Vibe-Coding-Claude-Fable-5-mlx-4Bit
The Model [usermma/Vibe-Coding-Claude-Fable-5-mlx-4Bit](https://huggingface.co/usermma/Vibe-Coding-Claude-Fable-5-mlx-4Bit) was converted to MLX format from [sakmkmk2/Vibe-Coding-Claude-Fable-5](https://huggingface.co/sakmkmk2/Vibe-Coding-Claude-Fable-5) using mlx-lm version **0.31.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("usermma/Vibe-Coding-Claude-Fable-5-mlx-4Bit")
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)
```
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