Instructions to use itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF 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 itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-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 itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-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 itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-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": "itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M

Ollama
How to use itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

A newer version of this model is available: mistralai/Mistral-7B-Instruct-v0.3

AIronMind/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF

This model was converted to GGUF format from mistralai/Mistral-7B-Instruct-v0.2 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo AIronMind/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF --model mistral-7b-instruct-v0.2.Q4_K_M.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo AIronMind/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF --model mistral-7b-instruct-v0.2.Q4_K_M.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m mistral-7b-instruct-v0.2.Q4_K_M.gguf -n 128

Downloads last month: 50

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

Collections including itlwas/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF