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FuseAI_FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview_MLX-4bit
WaveCut/FuseAI_FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview_MLX-4bit
FuseAI_FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview_MLX-4bit is a machine learning model from WaveCut. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx.
The Model WaveCut/FuseAIFuseO1-DeepSeekR1-QwQ-SkyT1-32B-PreviewMLX-4bit was converted to MLX format from FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview using mlx-lm version 0.21.1.
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32.8B
18.4 GB on disk
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.safetensors18.4 GB · 100%
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U3232.8B · 100%
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Model card
From the Hugging Face model README
WaveCut/FuseAI_FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview_MLX-4bit
The Model WaveCut/FuseAI_FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview_MLX-4bit was converted to MLX format from FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview using mlx-lm version 0.21.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("WaveCut/FuseAI_FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview_MLX-4bit")
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)
- Library
- mlx
- Type
- qwen2
- Created
- Jan 22, 2025
- Updated
- Jan 22, 2025