- Libraries
- Transformers
How to use desi6ner/mistral_instruct_WIN with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("desi6ner/mistral_instruct_WIN", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use desi6ner/mistral_instruct_WIN 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 desi6ner/mistral_instruct_WIN:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf desi6ner/mistral_instruct_WIN:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf desi6ner/mistral_instruct_WIN:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf desi6ner/mistral_instruct_WIN:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf desi6ner/mistral_instruct_WIN:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf desi6ner/mistral_instruct_WIN:Q4_K_M
Use Docker
docker model run hf.co/desi6ner/mistral_instruct_WIN:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use desi6ner/mistral_instruct_WIN with Ollama:
ollama run hf.co/desi6ner/mistral_instruct_WIN:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use desi6ner/mistral_instruct_WIN with Docker Model Runner:
docker model run hf.co/desi6ner/mistral_instruct_WIN:Q4_K_M
- Lemonade
How to use desi6ner/mistral_instruct_WIN with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull desi6ner/mistral_instruct_WIN:Q4_K_M
Run and chat with the model
lemonade run user.mistral_instruct_WIN-Q4_K_M
List all available models
lemonade list
- Atomic Chat
