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

Libraries

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

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

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

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

llama-cpp-python

How to use s3nh/MathLLM-MathCoder-L-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="s3nh/MathLLM-MathCoder-L-7B-GGUF",
	filename="MathLLM-MathCoder-L-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 s3nh/MathLLM-MathCoder-L-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-L-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf s3nh/MathLLM-MathCoder-L-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-L-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf s3nh/MathLLM-MathCoder-L-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-L-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf s3nh/MathLLM-MathCoder-L-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-L-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf s3nh/MathLLM-MathCoder-L-7B-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use s3nh/MathLLM-MathCoder-L-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-L-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-L-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use s3nh/MathLLM-MathCoder-L-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-L-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-L-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-L-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-L-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

s3nh commited on Oct 6, 2023

Commit

a32ce1f

1 Parent(s): 7682082

Upload ./ with huggingface_hub

Browse files

Files changed (9) hide show

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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-L-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