Instructions to use Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4",
	filename="Mistral-7B-Instruct-v0.3-Q4_K_M.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 Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 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 Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4: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 Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4: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 Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M

Use Docker

docker model run hf.co/Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M

LM Studio
Jan

vLLM

How to use Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M

Ollama
How to use Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 with Ollama:
```
ollama run hf.co/Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M
```

Unsloth Studio

How to use Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 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 Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 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 Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 to start chatting

Atomic Chat new
Docker Model Runner
How to use Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 with Docker Model Runner:
```
docker model run hf.co/Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M
```

Lemonade

How to use Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4:Q4_K_M

Run and chat with the model

lemonade run user.solus_v1_mistral-7b-instruct-v0.3-q4-Q4_K_M

List all available models

lemonade list

Mistral 7B Instruct v0.3 — Solus v1

Mistral AI's 7B instruction-tuned model, version 0.3. Compared with earlier releases it carries an extended 32,768-token vocabulary and support for function calling.

Its reputation is for fluent, natural prose, which makes it a comfortable choice for drafting and rewriting. Apache 2.0 licensed, so there are no usage restrictions to pass along.

Specifications


Parameters	7B
Quantization	Q4_K_M
File size	4.07 GB
Minimum RAM	8.00 GB
Minimum VRAM	6.00 GB
Context length	32,768 tokens
SHA-256	`1270d22c0fbb3d092fb725d4d96c457b7b687a5f5a715abe1e818da303e562b6`

Single file: Mistral-7B-Instruct-v0.3-Q4_K_M.gguf

Quantization

Quantization performed at the Faculty of Engineering, McMaster University.

The GGUF conversion this build is derived from was produced by bartowski, and the weights here are a byte-for-byte copy of that file — the SHA-256 above matches the upstream artifact.

Provenance

Original model: mistralai/Mistral-7B-Instruct-v0.3
Upstream GGUF: bartowski/Mistral-7B-Instruct-v0.3-GGUF
Mirrored for Solus, a desktop app for running language models entirely on your own machine.

Usage

llama-cli -m Mistral-7B-Instruct-v0.3-Q4_K_M.gguf -cnv

Downloads last month: -

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for Fazmin/solus_v1_mistral-7b-instruct-v0.3-q4

Base model

mistralai/Mistral-7B-v0.3

Finetuned

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

Quantized

(282)

this model