Instructions to use ZirTech/OmniMath-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZirTech/OmniMath-2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZirTech/OmniMath-2B-GGUF",
	filename="OmniMath-2B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZirTech/OmniMath-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ZirTech/OmniMath-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ZirTech/OmniMath-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZirTech/OmniMath-2B-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": "ZirTech/OmniMath-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZirTech/OmniMath-2B-GGUF:Q4_K_M

Ollama
How to use ZirTech/OmniMath-2B-GGUF with Ollama:
```
ollama run hf.co/ZirTech/OmniMath-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZirTech/OmniMath-2B-GGUF to start chatting

How to use ZirTech/OmniMath-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZirTech/OmniMath-2B-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": "ZirTech/OmniMath-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-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 ZirTech/OmniMath-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ZirTech/OmniMath-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZirTech/OmniMath-2B-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 "ZirTech/OmniMath-2B-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"

Docker Model Runner
How to use ZirTech/OmniMath-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/ZirTech/OmniMath-2B-GGUF:Q4_K_M
```

Lemonade

How to use ZirTech/OmniMath-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZirTech/OmniMath-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OmniMath-2B-GGUF-Q4_K_M

List all available models

lemonade list

ZirTech commited on Apr 14

Commit

adc935f

verified ·

1 Parent(s): c6482c2

Update README.md

Browse files

Files changed (1) hide show

README.md +36 -2

README.md CHANGED Viewed

@@ -9,9 +9,12 @@ pipeline_tag: text-generation
 # OmniMath-2B - GGUF Quantized
-This repository contains **GGUF format quantizations** of the [OmniMath-2B](https://huggingface.co/ZirTech/OmniMath-2B) model, a 2-billion parameter language model specialized for mathematical reasoning.
-Original model: [ZirTech/OmniMath-2B](https://huggingface.co/ZirTech/OmniMath-2B)
 ## Quantizations & File Sizes
@@ -30,3 +33,34 @@ Original model: [ZirTech/OmniMath-2B](https://huggingface.co/ZirTech/OmniMath-2B
 | **Q8_0**     | 2.01 GB   | 8-bit, near‑original quality |
 | **F16**      | 3.78 GB   | 16-bit float (original weights) |

 # OmniMath-2B - GGUF Quantized
+**This is the official GGUF repository for the OmniMath-2B model.**
+All quantizations provided here are created and maintained by the original model author.
+**Original model**: [ZirTech/OmniMath-2B](https://huggingface.co/ZirTech/OmniMath-2B)
+---
 ## Quantizations & File Sizes
 | **Q8_0**     | 2.01 GB   | 8-bit, near‑original quality |
 | **F16**      | 3.78 GB   | 16-bit float (original weights) |
+---
+# Recommended Quantization
+Since **OmniMath-2B** is specialized for mathematical reasoning, **accuracy is paramount**. Lower bit quantizations (Q2, Q3, Q4) may degrade performance on complex problems.
+| Recommendation | Quantization | Size   | Notes |
+|----------------|--------------|--------|-------|
+| **Best for math** (minimal quality loss) | **Q8_0** or **F16** | 2.01 GB / 3.78 GB | Use if you have enough RAM/VRAM. |
+| **Good trade‑off** (recommended) | **Q6_K** or **Q5_K_M** | 1.56 GB / 1.41 GB | Still high accuracy, much smaller than F16. |
+| **Minimum acceptable** (for tight memory) | **Q4_K_M** | 1.27 GB | May lose some precision; test before using in production. |
+| **Not recommended** | Q2_K, Q3_K_*, IQ4_XS | < 1.2 GB | Likely to degrade mathematical reasoning. |
+> **Tip**: For the best results, use **Q8_0** or **Q6_K**. If you need to save space, **Q5_K_M** is the lowest we recommend for math.
+---
+# License
+This repository and the quantized files are released under the ztech-license.
+Please refer to the original model repository for the full license text.
+The GGUF format quantizations are provided by the original author. No third-party ownership is claimed.
+---
+<div align="center">
+**Built by [Zirt Tech](https://huggingface.co/ZirTech) ❤️**
+</div>