Instructions to use tinyopsec/Pythagoras-Prover-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tinyopsec/Pythagoras-Prover-4B-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 tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Pythagoras-Prover-4B-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 tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Pythagoras-Prover-4B-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 tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/Pythagoras-Prover-4B-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 tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/Pythagoras-Prover-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/Pythagoras-Prover-4B-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": "tinyopsec/Pythagoras-Prover-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M

Ollama
How to use tinyopsec/Pythagoras-Prover-4B-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/Pythagoras-Prover-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Pythagoras-Prover-4B-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": "tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/Pythagoras-Prover-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/Pythagoras-Prover-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Pythagoras-Prover-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/Pythagoras-Prover-4B-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 tinyopsec/Pythagoras-Prover-4B-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 tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/Pythagoras-Prover-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Pythagoras-Prover-4B-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 "tinyopsec/Pythagoras-Prover-4B-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"

Pythagoras-Prover-4B GGUF

GGUF quantizations of Pythagoras-LM/Pythagoras-Prover-4B — a 4B model specialized in mathematical reasoning and formal theorem proving.

Quantized by tinyopsec.

Quantization Table

File	Bits	Size	Use Case
`model_f16.gguf`	16	~8.8 GB	Full precision, reference
`model_q8_0.gguf`	8	~4.7 GB	Max quality, fits in 6GB VRAM
`model_q6_k.gguf`	6	~3.6 GB	Near-lossless, recommended
`model_q5_k_m.gguf`	5	~3.1 GB	Balanced quality/size
`model_q5_k_s.gguf`	5	~3.0 GB	Slightly smaller than K_M
`model_q4_k_m.gguf`	4	~2.5 GB	Good quality, popular choice
`model_q4_k_s.gguf`	4	~2.4 GB	Smaller footprint
`model_q3_k_l.gguf`	3	~2.1 GB	Low VRAM, acceptable quality
`model_q3_k_m.gguf`	3	~2.0 GB	Low VRAM
`model_q3_k_s.gguf`	3	~1.9 GB	Minimum recommended
`model_q2_k.gguf`	2	~1.5 GB	Extreme compression, lossy

VRAM Requirements

Quant	Min VRAM	Recommended
F16	10 GB	12 GB+
Q8_0	6 GB	8 GB
Q6_K	5 GB	6 GB
Q5_K_M / Q5_K_S	4 GB	5 GB
Q4_K_M / Q4_K_S	4 GB	4 GB
Q3_K_*	3 GB	4 GB
Q2_K	3 GB	3 GB

Usage

llama.cpp

./llama-cli \
  -m model_q4_k_m.gguf \
  -p "Prove that there are infinitely many prime numbers." \
  -n 512 \
  --temp 0.7

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="model_q4_k_m.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,
)

response = llm(
    "Prove that sqrt(2) is irrational.",
    max_tokens=512,
    temperature=0.7,
)
print(response["choices"][0]["text"])

LM Studio

Download any .gguf file from this repo
Open LM Studio → Load Model → select the file
Start chatting

Ollama

ollama run hf.co/tinyopsec/Pythagoras-Prover-4B-GGUF:Q4_K_M

Recommended Quant

Q4_K_M — best balance of quality and size for most hardware.
Q6_K — if you have 6GB+ VRAM and want near-lossless math reasoning.

Original Model

Pythagoras-LM/Pythagoras-Prover-4B

Downloads last month: 2,449

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/Pythagoras-Prover-4B-GGUF

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Finetuned

Pythagoras-LM/Pythagoras-Prover-4B

Quantized

(1)

this model

tinyopsec
/

Pythagoras-Prover-4B-GGUF

Pythagoras-Prover-4B GGUF

Quantization Table

VRAM Requirements

Usage

llama.cpp

llama-cpp-python

LM Studio

Ollama

Recommended Quant

Original Model

Model tree for tinyopsec/Pythagoras-Prover-4B-GGUF

Space using tinyopsec/Pythagoras-Prover-4B-GGUF 1