Instructions to use mlx-community/functionary-small-v3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/functionary-small-v3.2 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/functionary-small-v3.2 --local-dir functionary-small-v3.2
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from mlx-community/functionary-small-v3.2: direct link, hf CLI and curl.
- Browser
- Download file 879 Bytes
-
https://huggingface.co/mlx-community/functionary-small-v3.2/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://mlx-community/functionary-small-v3.2@refs/pr/1/README.md
-
curl -L -o README.md https://huggingface.co/mlx-community/functionary-small-v3.2/resolve/refs%2Fpr%2F1/README.md
879 Bytes
metadata
base_model: meetkai/functionary-small-v3.2
license: mit
tags:
- mlx
mlx-community/functionary-small-v3.2
The Model mlx-community/functionary-small-v3.2 was converted to MLX format from meetkai/functionary-small-v3.2 using mlx-lm version 0.20.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/functionary-small-v3.2")
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)