Math Professor
Collection
A Collection of Math Models โข 5 items โข Updated โข 2
How to use entfane/math-virtuoso-7B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -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
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
docker model run hf.co/entfane/math-virtuoso-7B-GGUF:Q4_K_M
How to use entfane/math-virtuoso-7B-GGUF with vLLM:
# 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?"
}
]
}'docker model run hf.co/entfane/math-virtuoso-7B-GGUF:Q4_K_M
How to use entfane/math-virtuoso-7B-GGUF with Ollama:
ollama run hf.co/entfane/math-virtuoso-7B-GGUF:Q4_K_M
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
How to use entfane/math-virtuoso-7B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull entfane/math-virtuoso-7B-GGUF:Q4_K_M
lemonade run user.math-virtuoso-7B-GGUF-Q4_K_M
lemonade list
This model is a Math Instruction fine-tuned version of Mistral 7B v0.3 model.
!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))
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