Instructions to use mlx-community/Yi-9B-q with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Yi-9B-q with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/Yi-9B-q") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/Yi-9B-q with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/Yi-9B-q" --prompt "Once upon a time"
- Atomic Chat
Download tokenizer.model from mlx-community/Yi-9B-q: direct link, hf CLI and curl.
- Browser
- Download file 1.03 MB
-
https://huggingface.co/mlx-community/Yi-9B-q/resolve/main/tokenizer.model
- Command line
-
hf download hf://mlx-community/Yi-9B-q/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/mlx-community/Yi-9B-q/resolve/main/tokenizer.model
1.03 MB
- Xet hash:
- 515fb3e0fe5f36cdd967a91eace081f39f379f869c95afff03a52be0fed5da6a
- Size of remote file:
- 1.03 MB
- SHA256:
- 386c49cf943d71aa110361135338c50e38beeff0a66593480421f37b319e1a39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.