Instructions to use mlx-community/T-pro-it-1.0-Q4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/T-pro-it-1.0-Q4-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir T-pro-it-1.0-Q4-mlx mlx-community/T-pro-it-1.0-Q4-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/T-Pro-it-1.0-Q4-mlx
The Model mlx-community/T-pro-it-1.0-Q4-mlx was converted to MLX format from t-tech/T-pro-it-1.0 using mlx-lm version 0.19.2.
All rights pertaining to the model are the exclusive property of T-tech, while the model conversion tool is the property of MLX Community.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/T-pro-it-1.0-Q4-mlx")
prompt="Напиши стих про машинное обучение"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [
{"role": "system", "content": "Ты T-pro, виртуальный ассистент в Т-Технологии. Твоя задача - быть полезным диалоговым ассистентом."},
{"role": "user", "content": prompt}
]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Base model
t-tech/T-pro-it-1.0