Instructions to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 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 TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0: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 TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0: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 TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M

Use Docker

docker model run hf.co/TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M

LM Studio
Jan
Ollama
How to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 with Ollama:
```
ollama run hf.co/TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M
```
Unsloth Desktop

How to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M

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": "TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 with Docker Model Runner:
```
docker model run hf.co/TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M
```

Lemonade

How to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-4bit-Computer_Vision-1.0.0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0: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 TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0: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 "TylerHeuss/Llama-3.1-4bit-Computer_Vision-1.0.0: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"

Uses

This LLM is trained on data generated by my code for the yolov8 model. Github code The model is capable of briefly describing what the yolov8 model can detect and can also execute a command (/click). When the command is triggered, a dictionary is generated containing the key data of the object to be clicked.

Testing

You can test the model by giving it this informations:

{
    "Object": [
        {
            "index": "window_0",
            "label": "window",
            "property": "toplayer",
            "coords": [
                189.06007385253906,
                79.33326721191406,
                1156.018798828125,
                750.1478271484375
            ],
            "textes": 24,
            "interactions": [
                {
                    "label": "close_window",
                    "interaction_type": 1,
                    "coords": [
                        1114.04541015625,
                        84.65348815917969,
                        1149.1778564453125,
                        113.41248321533203
                    ]
                },
                {
                    "label": "maximize",
                    "interaction_type": 1,
                    "coords": [
                        1067.0111083984375,
                        84.82215118408203,
                        1099.86328125,
                        112.69491577148438
                    ]
                },
                {
                    "label": "minize_window",
                    "interaction_type": 1,
                    "coords": [
                        1024.7701416015625,
                        85.06327819824219,
                        1053.4327392578125,
                        111.52396392822266
                    ]
                }
            ]
        }
    ]
}

You can give the model this informations and a prompt like "Was siehst du" or "Kannst du das Fenster schließen".

The Model is at the moment only trained on german.

Downloads last month: 14

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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