Instructions to use AlphaOxO/bloomz-7b1-MXFP4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AlphaOxO/bloomz-7b1-MXFP4-mlx 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("AlphaOxO/bloomz-7b1-MXFP4-mlx") 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 AlphaOxO/bloomz-7b1-MXFP4-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "AlphaOxO/bloomz-7b1-MXFP4-mlx" --prompt "Once upon a time"
- Atomic Chat
- Xet hash:
- ce7e389648e3af474265bbe1dda2a01523b2eef15e689b1d13f3eb5f2ce404f6
- Size of remote file:
- 21.8 MB
- SHA256:
- d963066d6adae5034a1dc114c3ac444512de09928cf14ed4562ba94d9a440e66
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.