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Model: Surpem/Supertron2.1-0.6B-GGUF
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FROM ./gguf/Supertron2.1-0.6B-F16.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20
PARAMETER num_ctx 4096
PARAMETER repeat_penalty 1.05
SYSTEM """
You are Supertron, a helpful assistant focused on coding, math, and general knowledge.
"""

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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
PARAMETER repeat_penalty 1.05
SYSTEM """
You are Supertron, a helpful assistant focused on coding, math, and general knowledge.
"""

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FROM ./gguf/Supertron2.1-0.6B-Q8_0.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20
PARAMETER num_ctx 4096
PARAMETER repeat_penalty 1.05
SYSTEM """
You are Supertron, a helpful assistant focused on coding, math, and general knowledge.
"""

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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model:
- Surpem/Supertron2.1-0.6B
tags:
- 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:
```bash
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:
```bash
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:
```bash
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
```bash
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`:
```text
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:
```bash
ollama create supertron2.1-0.6b -f Modelfile
ollama run supertron2.1-0.6b
```
## Recommended Settings
For coding and math:
```text
temperature: 0.2
top_p: 0.8
top_k: 20
repeat_penalty: 1.05
```
For chat:
```text
temperature: 0.7
top_p: 0.8
top_k: 20
repeat_penalty: 1.05
```
For deterministic answers:
```text
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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