license, language, pipeline_tag, base_model, tags

license	language	pipeline_tag	base_model	tags
apache-2.0	en	text-generation	Surpem/Supertron2.1-0.6B	gguf llama-cpp qwen qwen3 chat quantized q4 q8 f16 local-llm surpem

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.