Instructions to use hellosri/watt-tool-70B-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hellosri/watt-tool-70B-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir watt-tool-70B-mlx-8Bit hellosri/watt-tool-70B-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
hellosri/watt-tool-70B-mlx-8Bit
The Model hellosri/watt-tool-70B-mlx-8Bit was converted to MLX format from watt-ai/watt-tool-70B using mlx-lm version 0.22.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("hellosri/watt-tool-70B-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)
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Model size
20B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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8-bit
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Model tree for hellosri/watt-tool-70B-mlx-8Bit
Base model
meta-llama/Llama-3.1-70B Finetuned
meta-llama/Llama-3.3-70B-Instruct Finetuned
watt-ai/watt-tool-70B