Instructions to use QuantFactory/MathCoder2-CodeLlama-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/MathCoder2-CodeLlama-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/MathCoder2-CodeLlama-7B-GGUF",
	filename="MathCoder2-CodeLlama-7B.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/MathCoder2-CodeLlama-7B-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 QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/MathCoder2-CodeLlama-7B-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 QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/MathCoder2-CodeLlama-7B-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 QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/MathCoder2-CodeLlama-7B-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 QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/MathCoder2-CodeLlama-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/MathCoder2-CodeLlama-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/MathCoder2-CodeLlama-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M

Ollama
How to use QuantFactory/MathCoder2-CodeLlama-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/MathCoder2-CodeLlama-7B-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 QuantFactory/MathCoder2-CodeLlama-7B-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 QuantFactory/MathCoder2-CodeLlama-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/MathCoder2-CodeLlama-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/MathCoder2-CodeLlama-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/MathCoder2-CodeLlama-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/MathCoder2-CodeLlama-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MathCoder2-CodeLlama-7B-GGUF-Q4_K_M

List all available models

lemonade list

MathCoder2-CodeLlama-7B-GGUF

File size: 2,346 Bytes

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---

license: apache-2.0
datasets:
- MathGenie/MathCode-Pile
language:
- en
metrics:
- accuracy
base_model:
- codellama/CodeLlama-7b-hf
pipeline_tag: text-generation
tags:
- math

---

[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


# QuantFactory/MathCoder2-CodeLlama-7B-GGUF
This is quantized version of [MathGenie/MathCoder2-CodeLlama-7B](https://huggingface.co/MathGenie/MathCoder2-CodeLlama-7B) created using llama.cpp

# Original Model Card


# MathCoder2

### Introduction

The MathCoder2 models are created by conducting continued pretraining on [MathCode-Pile](https://huggingface.co/datasets/MathGenie/MathCode-Pile). They are introduced in the paper [MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code](https://arxiv.org/abs/2410.08196).

The mathematical pretraining dataset includes mathematical code accompanied with natural language reasoning steps, making it a superior resource for models aimed at performing advanced mathematical reasoning tasks.

### Evaluation

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65dd9e7b4a4fce1ec96dc6b7/BEZoDZLjp-fPFlt7oFXBa.png)

### Citation

If you find this repository helpful, please consider citing our papers:

```
@misc{lu2024mathcoder2bettermathreasoning,
      title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code}, 
      author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
      year={2024},
      eprint={2410.08196},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.08196}, 
}
```
```
@inproceedings{
wang2024mathcoder,
title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
author={Zimu Lu and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=z8TW0ttBPp}
}
```