Instructions to use bartowski/Mistral-Large-Instruct-2407-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/Mistral-Large-Instruct-2407-GGUF", filename="Mistral-Large-Instruct-2407-IQ1_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Mistral-Large-Instruct-2407-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/Mistral-Large-Instruct-2407-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
- Ollama
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with Ollama:
ollama run hf.co/bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bartowski/Mistral-Large-Instruct-2407-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bartowski/Mistral-Large-Instruct-2407-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/Mistral-Large-Instruct-2407-GGUF to start chatting
- Pi new
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Mistral-Large-Instruct-2407-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Mistral-Large-Instruct-2407-GGUF-Q4_K_M
List all available models
lemonade list
Chat template
The "</s>" (EOS) in the current chat template seems to stop the model from generating tokens. Removing the "</s>" works for me.
FWIW the upstream chat template in the config json is super weird and hard to read...
Huh, something does seem a bit odd. The model card suggests normal looking Mistral prompt format (including the goofy white spaces):
<s>[INST] {prompt}[/INST] </s>
The model loading debug log in llama.cpp seems to match expected Mistral tokens:
tokenizer.ggml.tokens arr[str,32768] = ["<unk>", "<s>", "</s>", "[INST]", "[...
However, the model loading debug log shows what looks like ChatML prompt format chat_example:
chat_example="<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nHow are you?<|im_end|>\n<|im_start|>assistant\n"
In practice, when I use Mistral format, the response always begins with that tell-tell extra white space. When I use ChatML format prompt, it doesn't start with the extra white-space. Using ChatML also tends to not stop inference prematurely in my limited testing.
I'm not 100% sure which one to actually use, but it likely matters - especially with how you do the system prompt. I'm leaning towards using ChatML given it does not produce the extra white-space and tends to continue inference without premature ending.
Yeah that's my experience as well, the ChatML prompt also works.
I can't really tell the difference between the outputs of [INST] or ChatML prompts.
If you use llama.cpp use --chat-template llama2 in your commands.