Instructions to use QuantFactory/Mistral-7B-Instruct-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Mistral-7B-Instruct-v0.1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Mistral-7B-Instruct-v0.1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Mistral-7B-Instruct-v0.1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Mistral-7B-Instruct-v0.1-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 QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Mistral-7B-Instruct-v0.1-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 QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Mistral-7B-Instruct-v0.1-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 QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Mistral-7B-Instruct-v0.1-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 QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Mistral-7B-Instruct-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Mistral-7B-Instruct-v0.1-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": "QuantFactory/Mistral-7B-Instruct-v0.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Mistral-7B-Instruct-v0.1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Mistral-7B-Instruct-v0.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Mistral-7B-Instruct-v0.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Mistral-7B-Instruct-v0.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Mistral-7B-Instruct-v0.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Mistral-7B-Instruct-v0.1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Mistral-7B-Instruct-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Mistral-7B-Instruct-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Mistral-7B-Instruct-v0.1-GGUF / README.md

munish0838

Create README.md

568dfb5 verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

512 Bytes

	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- mistral
	- text-generation-inference
	- conversational
	- finetuned
	base_model: mistralai/Mistral-7B-Instruct-v0.1
	inference: false
	---

	# Mistral-7B-Instruct-v0.1-GGUF
	- Quantized version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
	- Created using llama.cpp

	## Available Quants

	* Q2_K
	* Q3_K_L
	* Q3_K_M
	* Q3_K_S
	* Q4_0
	* Q4_K_M
	* Q4_K_S
	* Q5_0
	* Q5_K_M
	* Q5_K_S
	* Q6_K
	* Q8_0