Instructions to use calcuis/openmath2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use calcuis/openmath2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="calcuis/openmath2",
	filename="OpenMath2-Llama3.1-8B-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 calcuis/openmath2 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 calcuis/openmath2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf calcuis/openmath2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf calcuis/openmath2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf calcuis/openmath2: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 calcuis/openmath2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf calcuis/openmath2: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 calcuis/openmath2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf calcuis/openmath2:Q4_K_M

Use Docker

docker model run hf.co/calcuis/openmath2:Q4_K_M

LM Studio
Jan

vLLM

How to use calcuis/openmath2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "calcuis/openmath2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "calcuis/openmath2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/calcuis/openmath2:Q4_K_M

Ollama
How to use calcuis/openmath2 with Ollama:
```
ollama run hf.co/calcuis/openmath2:Q4_K_M
```

Unsloth Studio

How to use calcuis/openmath2 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 calcuis/openmath2 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 calcuis/openmath2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for calcuis/openmath2 to start chatting

How to use calcuis/openmath2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf calcuis/openmath2: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": "calcuis/openmath2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use calcuis/openmath2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf calcuis/openmath2: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 calcuis/openmath2:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use calcuis/openmath2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf calcuis/openmath2: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 "calcuis/openmath2: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 calcuis/openmath2 with Docker Model Runner:
```
docker model run hf.co/calcuis/openmath2:Q4_K_M
```

Lemonade

How to use calcuis/openmath2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull calcuis/openmath2:Q4_K_M

Run and chat with the model

lemonade run user.openmath2-Q4_K_M

List all available models

lemonade list

GGUF quantized version of OpenMath2-Llama3.1-8B

project original source (finetuned model)

Q_2_K (not nice)

Q_3_K_S (acceptable)

Q_3_K_M is acceptable (good for running with CPU)

Q_3_K_L (acceptable)

Q_4_K_S (okay)

Q_4_K_M is recommanded (balance)

Q_5_K_S (good)

Q_5_K_M (good in general)

Q_6_K is good also; if you want a better result; take this one instead of Q_5_K_M

Q_8_0 which is very good; need a reasonable size of RAM otherwise you might expect a long wait

f16 is similar to the original hf model; opt this one or hf also fine; make sure you have a good machine

*the latest update includes Q_4_0, Q_4_1 (belong to Q4 family) and Q_5_0, Q_5_1 (Q5 family)

how to run it

use any connector for interacting with gguf; i.e., gguf-connector

Performance of Llama-3.1-8B-Instruct as it is trained on increasing proportions of OpenMathInstruct-2

Comparison of OpenMath2-Llama3.1-8B vs. Llama-3.1-8B-Instruct across MATH levels

the chart and figure above are from finetuned model (nvidia side); those are used for comparing between the finetuned model and the base model; and the base model is from meta

Downloads last month: 193

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for calcuis/openmath2

Base model

meta-llama/Llama-3.1-8B

Finetuned

nvidia/OpenMath2-Llama3.1-8B

Quantized

(10)

this model