- Libraries
- MLX
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with MLX:
# Download the model from the Hub
pip install huggingface_hub[hf_xet]
huggingface-cli download --local-dir Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
- llama-cpp-python
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2",
filename="Mistral-7B-Instruct-v0.3-q-Chemistry-v0.2.gguf",
)
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
# Run inference directly in the terminal:
llama cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
# Run inference directly in the terminal:
llama cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
# Run inference directly in the terminal:
./llama-cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
Use Docker
docker model run hf.co/jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
- LM Studio
- Jan
- Ollama
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with Ollama:
ollama run hf.co/jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
- Unsloth Studio
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 to start chatting
- Pi
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2"
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Hermes Agent new
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with Hermes Agent:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2"
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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with Docker Model Runner:
docker model run hf.co/jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
- Lemonade
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2
Run and chat with the model
lemonade run user.Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.2-{{QUANT_TAG}}List all available models
lemonade list
