Instructions to use gaianet/Mistral-7B-Instruct-v0.3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gaianet/Mistral-7B-Instruct-v0.3-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="gaianet/Mistral-7B-Instruct-v0.3-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gaianet/Mistral-7B-Instruct-v0.3-GGUF")
model = AutoModelForCausalLM.from_pretrained("gaianet/Mistral-7B-Instruct-v0.3-GGUF")

llama-cpp-python

How to use gaianet/Mistral-7B-Instruct-v0.3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gaianet/Mistral-7B-Instruct-v0.3-GGUF",
	filename="Mistral-7B-Instruct-v0.3-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use gaianet/Mistral-7B-Instruct-v0.3-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 gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gaianet/Mistral-7B-Instruct-v0.3-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 gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gaianet/Mistral-7B-Instruct-v0.3-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 gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gaianet/Mistral-7B-Instruct-v0.3-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 gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use gaianet/Mistral-7B-Instruct-v0.3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gaianet/Mistral-7B-Instruct-v0.3-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": "gaianet/Mistral-7B-Instruct-v0.3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M

SGLang

How to use gaianet/Mistral-7B-Instruct-v0.3-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 "gaianet/Mistral-7B-Instruct-v0.3-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": "gaianet/Mistral-7B-Instruct-v0.3-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 "gaianet/Mistral-7B-Instruct-v0.3-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": "gaianet/Mistral-7B-Instruct-v0.3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use gaianet/Mistral-7B-Instruct-v0.3-GGUF with Ollama:
```
ollama run hf.co/gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
```

Unsloth Studio

How to use gaianet/Mistral-7B-Instruct-v0.3-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 gaianet/Mistral-7B-Instruct-v0.3-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 gaianet/Mistral-7B-Instruct-v0.3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gaianet/Mistral-7B-Instruct-v0.3-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use gaianet/Mistral-7B-Instruct-v0.3-GGUF with Docker Model Runner:
```
docker model run hf.co/gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
```

Lemonade

How to use gaianet/Mistral-7B-Instruct-v0.3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gaianet/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.3-GGUF-Q4_K_M

List all available models

lemonade list

Mistral-7B-Instruct-v0.3-GGUF

Original Model

mistralai/Mistral-7B-Instruct-v0.3

Run with Gaianet

Prompt template

prompt template: mistral-instruct

Context size

chat_ctx_size: 32000

Run with GaiaNet

Quick start: https://docs.gaianet.ai/node-guide/quick-start
Customize your node: https://docs.gaianet.ai/node-guide/customize

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
Mistral-7B-Instruct-v0.3-Q2_K.gguf	Q2_K	2	2.72 GB	smallest, significant quality loss - not recommended for most purposes
Mistral-7B-Instruct-v0.3-Q3_K_L.gguf	Q3_K_L	3	3.83 GB	small, substantial quality loss
Mistral-7B-Instruct-v0.3-Q3_K_M.gguf	Q3_K_M	3	3.52 GB	very small, high quality loss
Mistral-7B-Instruct-v0.3-Q3_K_S.gguf	Q3_K_S	3	3.17 GB	very small, high quality loss
Mistral-7B-Instruct-v0.3-Q4_0.gguf	Q4_0	4	4.11 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Mistral-7B-Instruct-v0.3-Q4_K_M.gguf	Q4_K_M	4	4.37 GB	medium, balanced quality - recommended
Mistral-7B-Instruct-v0.3-Q4_K_S.gguf	Q4_K_S	4	4.14 GB	small, greater quality loss
Mistral-7B-Instruct-v0.3-Q5_0.gguf	Q5_0	5	5 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Mistral-7B-Instruct-v0.3-Q5_K_M.gguf	Q5_K_M	5	5.14 GB	large, very low quality loss - recommended
Mistral-7B-Instruct-v0.3-Q5_K_S.gguf	Q5_K_S	5	5 GB	large, low quality loss - recommended
Mistral-7B-Instruct-v0.3-Q6_K.gguf	Q6_K	6	5.95 GB	very large, extremely low quality loss
Mistral-7B-Instruct-v0.3-Q8_0.gguf	Q8_0	8	7.7 GB	very large, extremely low quality loss - not recommended
Mistral-7B-Instruct-v0.3-f16.gguf	f16	16	14.5 GB

Quantized with llama.cpp b3030.

Downloads last month: 164

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for gaianet/Mistral-7B-Instruct-v0.3-GGUF

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Quantized

(266)

this model

Collection including gaianet/Mistral-7B-Instruct-v0.3-GGUF

Mistral

Collection

4 items • Updated Feb 10, 2025 • 1