Instructions to use ALYTV/zeta-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ALYTV/zeta-mlx-6Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download ALYTV/zeta-mlx-6Bit --local-dir zeta-mlx-6Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
|
Download README.md from ALYTV/zeta-mlx-6Bit: direct link, hf CLI and curl.
- Browser
- Download file 817 Bytes
-
https://huggingface.co/ALYTV/zeta-mlx-6Bit/resolve/main/README.md
- Command line
-
hf download hf://ALYTV/zeta-mlx-6Bit/README.md
-
curl -L -o README.md https://huggingface.co/ALYTV/zeta-mlx-6Bit/resolve/main/README.md
817 Bytes
metadata
datasets:
- zed-industries/zeta
license: apache-2.0
base_model: zed-industries/zeta
tags:
- mlx
ALYTV/zeta-mlx-6Bit
The Model ALYTV/zeta-mlx-6Bit was converted to MLX format from zed-industries/zeta using mlx-lm version 0.22.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("ALYTV/zeta-mlx-6Bit")
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)