Instructions to use s3nh/MathLLM-MathCoder-CL-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s3nh/MathLLM-MathCoder-CL-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="s3nh/MathLLM-MathCoder-CL-7B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("s3nh/MathLLM-MathCoder-CL-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use s3nh/MathLLM-MathCoder-CL-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 s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf s3nh/MathLLM-MathCoder-CL-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 s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf s3nh/MathLLM-MathCoder-CL-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 s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf s3nh/MathLLM-MathCoder-CL-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 s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use s3nh/MathLLM-MathCoder-CL-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s3nh/MathLLM-MathCoder-CL-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": "s3nh/MathLLM-MathCoder-CL-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M

SGLang

How to use s3nh/MathLLM-MathCoder-CL-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "s3nh/MathLLM-MathCoder-CL-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s3nh/MathLLM-MathCoder-CL-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "s3nh/MathLLM-MathCoder-CL-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s3nh/MathLLM-MathCoder-CL-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use s3nh/MathLLM-MathCoder-CL-7B-GGUF with Ollama:
```
ollama run hf.co/s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use s3nh/MathLLM-MathCoder-CL-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 s3nh/MathLLM-MathCoder-CL-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 s3nh/MathLLM-MathCoder-CL-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 s3nh/MathLLM-MathCoder-CL-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use s3nh/MathLLM-MathCoder-CL-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M
```

Lemonade

How to use s3nh/MathLLM-MathCoder-CL-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull s3nh/MathLLM-MathCoder-CL-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MathLLM-MathCoder-CL-7B-GGUF-Q4_K_M

List all available models

lemonade list

MathLLM-MathCoder-CL-7B-GGUF / README.md

s3nh

Upload ./ with huggingface_hub

a46c539 almost 3 years ago

preview code

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	---
	license: openrail
	pipeline_tag: text-generation
	library_name: transformers
	language:
	- zh
	- en
	---


	## Original model card

	Buy me a coffee if you like this project ;)
	<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>

	#### Description

	GGUF Format model files for [This project](https://huggingface.co/MathLLM/MathCoder-CL-7B).

	### GGUF Specs

	GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:

	Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
	Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
	mmap compatibility: models can be loaded using mmap for fast loading and saving.
	Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
	Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
	The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
	This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
	inference or for identifying the model.

	### Perplexity params

	Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16
	7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066
	13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543



	### inference


	TODO

	# Original model card