Instructions to use ccy1213/XMainframe-v2-Instruct-32b-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ccy1213/XMainframe-v2-Instruct-32b-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir XMainframe-v2-Instruct-32b-mlx-8Bit ccy1213/XMainframe-v2-Instruct-32b-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
metadata
base_model: Fsoft-AIC/XMainframe-v2-Instruct-32b
tags:
- mlx
ccy1213/XMainframe-v2-Instruct-32b-mlx-8Bit
The Model ccy1213/XMainframe-v2-Instruct-32b-mlx-8Bit was converted to MLX format from Fsoft-AIC/XMainframe-v2-Instruct-32b using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("ccy1213/XMainframe-v2-Instruct-32b-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)