Instructions to use sjug/Mistral-Large-Instruct-2411-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sjug/Mistral-Large-Instruct-2411-8bit 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("sjug/Mistral-Large-Instruct-2411-8bit") 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 sjug/Mistral-Large-Instruct-2411-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "sjug/Mistral-Large-Instruct-2411-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "sjug/Mistral-Large-Instruct-2411-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sjug/Mistral-Large-Instruct-2411-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Download tokenizer.model from sjug/Mistral-Large-Instruct-2411-8bit: direct link, hf CLI and curl.
- Browser
- Download file 588 kB
-
https://huggingface.co/sjug/Mistral-Large-Instruct-2411-8bit/resolve/main/tokenizer.model
- Command line
-
hf download hf://sjug/Mistral-Large-Instruct-2411-8bit/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/sjug/Mistral-Large-Instruct-2411-8bit/resolve/main/tokenizer.model
588 kB
- Xet hash:
- 1a64fe890e67d85f9bb1024df5e803e4c0870d36c6f2590262381ebbc53ba416
- Size of remote file:
- 588 kB
- SHA256:
- 1b968b8dc352f42192367337c78ccc61e1eaddc6d641a579372d4f20694beb7a
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