Instructions to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf",
	filename="mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0

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 Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0

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 Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0

Use Docker

docker model run hf.co/Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0

LM Studio
Jan

vLLM

How to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.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": "Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0

Ollama
How to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf with Ollama:
```
ollama run hf.co/Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0
```

Unsloth Studio new

How to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.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 Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.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 Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf to start chatting

Docker Model Runner
How to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf with Docker Model Runner:
```
docker model run hf.co/Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0
```

Lemonade

How to use Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf:Q8_0

Run and chat with the model

lemonade run user.mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf-Q8_0

List all available models

lemonade list

Mistral-7B-Instruct-v0.2 (GGUF Q8_0)

High-quality 8-bit quantized GGUF of Mistral-7B-Instruct-v0.2 for efficient local inference on CPUs and edge devices.

Model Details

Property	Value
Base Model	Mistral-7B-Instruct-v0.2
Quantization	Q8_0 (8-bit)
Format	GGUF
File Size	~7.7 GB
Context Length	32,768 tokens
License	MIT

Quick Start

llama.cpp

# Download
huggingface-cli download Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf \
    mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf --local-dir .

# Run inference
./llama-cli -m mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf \
    -p "[INST] Explain digital pre-distortion in 5G systems. [/INST]" \
    -n 512 -ngl 0

Python (llama-cpp-python)

from llama_cpp import Llama

model = Llama(
    model_path="mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf",
    n_ctx=4096,
    n_threads=4,  # adjust for your CPU
)

output = model(
    "[INST] What is SerDes equalization? [/INST]",
    max_tokens=512,
    temperature=0.7,
)
print(output["choices"][0]["text"])

LM Studio / Ollama

Download the GGUF file and load it directly in LM Studio or import via Ollama.

Quantization Details

Q8_0 retains near-original model quality while reducing memory footprint. Recommended when inference speed matters but quality degradation must be minimal.

Quantization	Size	Quality	Speed
FP16	~14 GB	Baseline	Slow
Q8_0	~7.7 GB	~99.5%	Fast
Q4_K_M	~4.4 GB	~97%	Fastest

Hardware Requirements

Device	RAM Required	Performance
Desktop CPU (x86)	8 GB+	Good
Apple Silicon (M1+)	8 GB+	Excellent
Raspberry Pi 5 (8GB)	8 GB	Functional (slow)

Credits

Base model: Mistral AI
Quantization: llama.cpp

Maintainer: Makatia

Downloads last month: 4

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

8-bit

Collection including Makatia/mistral-7b-instruct-v0.2.Q8_0-Q8_0.gguf

Edge LLM Deployments

Collection

LLMs quantized for Raspberry Pi and ARM edge. GGUF + ONNX. No cloud required. • 3 items • Updated Mar 25