Text Generation
MLX
Safetensors
hunyuan_v1_dense
translation
abliterated
uncensored
conversational
8-bit precision
Instructions to use alexgusevski/Huihui-HY-MT1.5-7B-abliterated-q8-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use alexgusevski/Huihui-HY-MT1.5-7B-abliterated-q8-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("alexgusevski/Huihui-HY-MT1.5-7B-abliterated-q8-mlx") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use alexgusevski/Huihui-HY-MT1.5-7B-abliterated-q8-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "alexgusevski/Huihui-HY-MT1.5-7B-abliterated-q8-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "alexgusevski/Huihui-HY-MT1.5-7B-abliterated-q8-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexgusevski/Huihui-HY-MT1.5-7B-abliterated-q8-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- f7c1479df78f3d530293faff0f520c1f533acec66a4700a07330303922efc5fb
- Size of remote file:
- 2.61 GB
- SHA256:
- f71f19f34916942ddf5d41ecb0761f3acf128b0af7847b906ab6674c90bdf532
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.