Instructions to use Fazmin/solus_v1_llama-3.1-8b-instruct-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Fazmin/solus_v1_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fazmin/solus_v1_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fazmin/solus_v1_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-q4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Fazmin/solus_v1_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-q4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

Use Docker

docker model run hf.co/Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

LM Studio
Jan

vLLM

How to use Fazmin/solus_v1_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-q4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

Ollama
How to use Fazmin/solus_v1_llama-3.1-8b-instruct-q4 with Ollama:
```
ollama run hf.co/Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M
```

Unsloth Studio

How to use Fazmin/solus_v1_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-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_llama-3.1-8b-instruct-q4 to start chatting

How to use Fazmin/solus_v1_llama-3.1-8b-instruct-q4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Fazmin/solus_v1_llama-3.1-8b-instruct-q4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

Run Hermes

hermes

OpenClaw new

How to use Fazmin/solus_v1_llama-3.1-8b-instruct-q4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Fazmin/solus_v1_llama-3.1-8b-instruct-q4 with Docker Model Runner:
```
docker model run hf.co/Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M
```

Lemonade

How to use Fazmin/solus_v1_llama-3.1-8b-instruct-q4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Fazmin/solus_v1_llama-3.1-8b-instruct-q4:Q4_K_M

Run and chat with the model

lemonade run user.solus_v1_llama-3.1-8b-instruct-q4-Q4_K_M

List all available models

lemonade list

Atomic Chat

Llama 3.1 8B Instruct — Solus v1

Meta's 8B instruction-tuned Llama 3.1. This is the size most people land on: capable enough for real work across chat, writing, summarisation, and everyday coding, while still running on a mainstream laptop or desktop.

It supports tool use and eight official languages, and it is the model Solus recommends by default for machines with a modern GPU or a reasonably fast CPU.

Specifications


Parameters	8B
Quantization	Q4_K_M
File size	4.58 GB
Minimum RAM	8.00 GB
Minimum VRAM	6.00 GB
Context length	32,768 tokens
SHA-256	`7b064f5842bf9532c91456deda288a1b672397a54fa729aa665952863033557c`

Single file: Meta-Llama-3.1-8B-Instruct-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: meta-llama/Meta-Llama-3.1-8B-Instruct
Upstream GGUF: bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
Mirrored for Solus, a desktop app for running language models entirely on your own machine.

Usage

llama-cli -m Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -cnv

License

Built with Llama.

Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Your use of this model is governed by that license and by the Llama 3.1 Acceptable Use Policy.

Downloads last month: 9

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for Fazmin/solus_v1_llama-3.1-8b-instruct-q4

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(892)

this model