Instructions to use entfane/math-virtuoso-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use entfane/math-virtuoso-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="entfane/math-virtuoso-7B-GGUF",
	filename="math-virtuoso-7b.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use entfane/math-virtuoso-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf entfane/math-virtuoso-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf entfane/math-virtuoso-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf entfane/math-virtuoso-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf entfane/math-virtuoso-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 entfane/math-virtuoso-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf entfane/math-virtuoso-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 entfane/math-virtuoso-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf entfane/math-virtuoso-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/entfane/math-virtuoso-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use entfane/math-virtuoso-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "entfane/math-virtuoso-7B-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": "entfane/math-virtuoso-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/entfane/math-virtuoso-7B-GGUF:Q4_K_M

Ollama
How to use entfane/math-virtuoso-7B-GGUF with Ollama:
```
ollama run hf.co/entfane/math-virtuoso-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use entfane/math-virtuoso-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 entfane/math-virtuoso-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 entfane/math-virtuoso-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 entfane/math-virtuoso-7B-GGUF to start chatting

Docker Model Runner
How to use entfane/math-virtuoso-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/entfane/math-virtuoso-7B-GGUF:Q4_K_M
```

Lemonade

How to use entfane/math-virtuoso-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull entfane/math-virtuoso-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.math-virtuoso-7B-GGUF-Q4_K_M

List all available models

lemonade list

Math Virtuoso 7B

This model is a Math Instruction fine-tuned version of Mistral 7B v0.3 model.

Inference

!pip install transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "entfane/math-virtuoso-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
messages = [
    {"role": "user", "content": "What's the derivative of 2x^2?"}
]
input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
encoded_input = tokenizer(input, return_tensors = "pt").to(model.device)
output = model.generate(**encoded_input, max_new_tokens=1024)
print(tokenizer.decode(output[0], skip_special_tokens=False))