metadata
license: mit
datasets:
- xl-zhao/PromptCoT-QwQ-Dataset
language:
- en
base_model: xl-zhao/PromptCoT-QwQ-32B
tags:
- mlx
TendernessChen/PromptCoT-QwQ-32B-mlx-3Bit
The Model TendernessChen/PromptCoT-QwQ-32B-mlx-3Bit was converted to MLX format from xl-zhao/PromptCoT-QwQ-32B using mlx-lm version 0.22.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("TendernessChen/PromptCoT-QwQ-32B-mlx-3Bit")
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)