Instructions to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-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 AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF
# Run inference directly in the terminal:
llama cli -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF
# Run inference directly in the terminal:
llama cli -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

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 AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF
# Run inference directly in the terminal:
./llama-cli -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

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 AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

Use Docker

docker model run hf.co/AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

LM Studio
Jan
Ollama
How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF with Ollama:
```
ollama run hf.co/AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF
```

Unsloth Studio

How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-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 AiAsistent/Llama-3.1-8B-Instruct-STO-Master-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 AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF to start chatting

How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF with Docker Model Runner:
```
docker model run hf.co/AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF
```

Lemonade

How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

Run and chat with the model

lemonade run user.Llama-3.1-8B-Instruct-STO-Master-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

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 AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

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 "AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF" \
  --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"

Llama-3.1-8B-Instruct-STO-Master-GGUF

This repository contains GGUF quantizations of the Llama-3.1-8B-Instruct-STO-Master (Model E). This model is a high-intelligence fine-tune designed to maximize the reasoning capabilities of the 8B parameter architecture.

🚀 Quick Links

Full Model Info & Benchmarks: Original Model Card
Synthetic Data Methodology: LLMResearch.net - Synthetic Data

🦙 Run with Ollama

This model is optimized for local execution. You can find it on Ollama here: 👉 Ollama - Llama-3.1-8B-Instruct-STO-Master

To run it immediately, use the following command:

ollama run aiasistentworld/Llama-3.1-8B-Instruct-STO-Master

🧠 Why this model?

High IQ Tuning: Internal tests show a 20-30 point IQ increase over the base Llama 3.1 8B Instruct.
STO Methodology: Trained using Specialized Task Optimization, focusing on logical "proofs" and deep understanding rather than simple memorization.
Efficiency: Achieved superior results using only 800,000 high-tier synthetic tokens, proving that quality beats quantity.

🎖 Credits

Author: AlexH
Organization: LLMResearch.net

For detailed benchmark results (MMLU, ARC, Hellaswag) and the full research history, please refer to the Main Model Repository.

⚖️ License

This model is subject to the Llama 3.1 Community License Agreement.

Downloads last month: 9

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for AiAsistent/Llama-3.1-8B-Instruct-STO-Master-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

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

Quantized

(903)

this model