Instructions to use prithivMLmods/Supertron3-0.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Supertron3-0.8B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Supertron3-0.8B-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/Supertron3-0.8B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/Supertron3-0.8B-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 prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Supertron3-0.8B-GGUF: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 prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Supertron3-0.8B-GGUF: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 prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/Supertron3-0.8B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Supertron3-0.8B-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": "prithivMLmods/Supertron3-0.8B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/Supertron3-0.8B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/Supertron3-0.8B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Supertron3-0.8B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/Supertron3-0.8B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Supertron3-0.8B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use prithivMLmods/Supertron3-0.8B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/Supertron3-0.8B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/Supertron3-0.8B-GGUF: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": "prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/Supertron3-0.8B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/Supertron3-0.8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Supertron3-0.8B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/Supertron3-0.8B-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 prithivMLmods/Supertron3-0.8B-GGUF: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 prithivMLmods/Supertron3-0.8B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/Supertron3-0.8B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/Supertron3-0.8B-GGUF: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 "prithivMLmods/Supertron3-0.8B-GGUF: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"
Supertron3-0.8B-GGUF
Supertron3-0.8B is a compact vision-language model from Suprem Org, fine-tuned from Qwen3.5-0.8B, purpose-built for GUI agents and agentic tool calling at the edge — interpreting visual interfaces across web, desktop, and CLI environments to emit precise pyautogui-style computer-use actions or valid JSON function calls. Its hybrid architecture interleaves Gated DeltaNet and Attention layers (24 layers, 1024 hidden dimension) with a vision encoder, retains a 262K native context window, and fits in a 1.7GB footprint suited for low-latency, on-device deployment. Despite being the smallest model in its evaluation set, Supertron3-0.8B ranks first on BFCL-style function calling (82% vs. 56% for its own Qwen3.5-0.8B base and 69% for the larger Qwen3.5-4B) and is the only model in the comparison that can reliably execute computer-use tasks at all — scoring 100% on Computer Use versus 0% for the unmodified base model — though it trails slightly on Mind2Web step accuracy (77% vs. 80% for the base), reflecting that the fine-tune specifically taught the base model to act rather than just converse. It's deployable via Transformers, vLLM, or SGLang, with known limitations around long-horizon multi-turn workflows and ScreenSpot-Pro-class grounding precision, which the authors attribute to the constraints of an 0.8B-parameter vision encoder; it's released under the Apache 2.0 license.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Supertron3-0.8B.BF16.gguf | BF16 | 1.52 GB | Download |
| Supertron3-0.8B.Q3_K_L.gguf | Q3_K_L | 491 MB | Download |
| Supertron3-0.8B.Q3_K_M.gguf | Q3_K_M | 466 MB | Download |
| Supertron3-0.8B.Q3_K_S.gguf | Q3_K_S | 435 MB | Download |
| Supertron3-0.8B.Q4_0.gguf | Q4_0 | 501 MB | Download |
| Supertron3-0.8B.Q4_K_M.gguf | Q4_K_M | 529 MB | Download |
| Supertron3-0.8B.Q4_K_S.gguf | Q4_K_S | 505 MB | Download |
| Supertron3-0.8B.Q5_0.gguf | Q5_0 | 564 MB | Download |
| Supertron3-0.8B.Q5_K_M.gguf | Q5_K_M | 578 MB | Download |
| Supertron3-0.8B.Q5_K_S.gguf | Q5_K_S | 564 MB | Download |
| Supertron3-0.8B.mmproj-bf16.gguf | mmproj-bf16 | 207 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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