Instructions to use Surpem/Supertron2.1-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Surpem/Supertron2.1-0.6B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Surpem/Supertron2.1-0.6B-GGUF", filename="gguf/Supertron2.1-0.6B-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Surpem/Supertron2.1-0.6B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Surpem/Supertron2.1-0.6B-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 Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Surpem/Supertron2.1-0.6B-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 Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Surpem/Supertron2.1-0.6B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Surpem/Supertron2.1-0.6B-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": "Surpem/Supertron2.1-0.6B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
- Ollama
How to use Surpem/Supertron2.1-0.6B-GGUF with Ollama:
ollama run hf.co/Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
- Unsloth Studio new
How to use Surpem/Supertron2.1-0.6B-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 Surpem/Supertron2.1-0.6B-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 Surpem/Supertron2.1-0.6B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Surpem/Supertron2.1-0.6B-GGUF to start chatting
- Pi new
How to use Surpem/Supertron2.1-0.6B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Surpem/Supertron2.1-0.6B-GGUF: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": "Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Surpem/Supertron2.1-0.6B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Surpem/Supertron2.1-0.6B-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 Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Surpem/Supertron2.1-0.6B-GGUF with Docker Model Runner:
docker model run hf.co/Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
- Lemonade
How to use Surpem/Supertron2.1-0.6B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Surpem/Supertron2.1-0.6B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Supertron2.1-0.6B-GGUF-Q4_K_M
List all available models
lemonade list
Supertron2.1-0.6B-GGUF
Supertron2.1-0.6B-GGUF contains GGUF exports of Surpem/Supertron2.1-0.6B, a compact Qwen3-based generalist model by Surpem.
This repository is for local inference with llama.cpp, LM Studio, Jan, KoboldCpp, text-generation-webui, and other GGUF-compatible runtimes. The original Transformers checkpoint is available at Surpem/Supertron2.1-0.6B.
Available Files
| File | Type | Size | Recommended Use |
|---|---|---|---|
gguf/Supertron2.1-0.6B-F16.gguf |
F16 | ~448 MiB | Highest quality GGUF, larger memory use |
gguf/Supertron2.1-0.6B-Q8_0.gguf |
8-bit | ~610 MiB | Strong quality, efficient local use |
gguf/Supertron2.1-0.6B-Q4_K_M.gguf |
4-bit K-quants | ~378 MiB | Small, fast, best for low-memory devices |
Which GGUF Should I Use?
Q4_K_M
Use this when you want the smallest practical model.
Good for:
- laptops
- CPU inference
- fast testing
- low VRAM
- general chat
Tradeoff: slightly lower quality than Q8/F16.
Q8_0
Use this when you want better quality while keeping the file smaller than full precision.
Good for:
- local coding help
- math prompts
- better instruction following
- GPU offload with modest VRAM
Tradeoff: larger than Q4.
F16
Use this when quality matters most and memory is available.
Good for:
- comparison testing
- re-quantization
- quality checks
- development workflows
Tradeoff: largest runtime memory use.
llama.cpp Usage
Install or build llama.cpp, then run:
llama-cli \
-m gguf/Supertron2.1-0.6B-Q4_K_M.gguf \
-p "Write a Python function that returns the nth Fibonacci number." \
-n 256
For chat-style prompting:
llama-cli \
-m gguf/Supertron2.1-0.6B-Q8_0.gguf \
-cnv \
--color \
-p "You are Supertron, a helpful coding and math assistant."
With GPU offload:
llama-cli \
-m gguf/Supertron2.1-0.6B-Q4_K_M.gguf \
-ngl 99 \
-p "Explain binary search in simple terms." \
-n 300
llama-server
llama-server \
-m gguf/Supertron2.1-0.6B-Q4_K_M.gguf \
-c 4096 \
-ngl 99 \
--host 0.0.0.0 \
--port 8080
Then call it with an OpenAI-compatible client.
Ollama Modelfile
Create a file named Modelfile:
FROM ./gguf/Supertron2.1-0.6B-Q4_K_M.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20
PARAMETER num_ctx 4096
SYSTEM """
You are Supertron, a helpful assistant focused on math, coding, and general knowledge.
"""
Create and run:
ollama create supertron2.1-0.6b -f Modelfile
ollama run supertron2.1-0.6b
Recommended Settings
For coding and math:
temperature: 0.2
top_p: 0.8
top_k: 20
repeat_penalty: 1.05
For chat:
temperature: 0.7
top_p: 0.8
top_k: 20
repeat_penalty: 1.05
For deterministic answers:
temperature: 0.0
Model Line
- Original model:
Surpem/Supertron2.1-0.6B - GGUF model:
Surpem/Supertron2.1-0.6B-GGUF - MLX 4-bit:
Surpem/Supertron2.1-0.6B-MLX-4Bit - MLX 8-bit:
Surpem/Supertron2.1-0.6B-MLX-8Bit
Notes
The GGUF files were converted from the latest Supertron2.1-0.6B Transformers checkpoint using llama.cpp tooling. Quantized models are approximations of the original bf16 checkpoint, and behavior can vary by runtime, prompt format, and sampling settings.
Limitations
- Q4 is smaller but less precise than Q8/F16.
- The model can hallucinate or produce wrong code.
- Human review is recommended for math, code, and factual claims.
- Do not use this model for safety-critical decisions.
License
Apache 2.0.
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