Instructions to use QuantFactory/LFM2-350M-Math-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/LFM2-350M-Math-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuantFactory/LFM2-350M-Math-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/LFM2-350M-Math-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/LFM2-350M-Math-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/LFM2-350M-Math-GGUF", filename="LFM2-350M-Math.Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/LFM2-350M-Math-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/LFM2-350M-Math-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 QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/LFM2-350M-Math-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/LFM2-350M-Math-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/LFM2-350M-Math-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/LFM2-350M-Math-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/LFM2-350M-Math-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/LFM2-350M-Math-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/LFM2-350M-Math-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M
- SGLang
How to use QuantFactory/LFM2-350M-Math-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 "QuantFactory/LFM2-350M-Math-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": "QuantFactory/LFM2-350M-Math-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 "QuantFactory/LFM2-350M-Math-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": "QuantFactory/LFM2-350M-Math-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use QuantFactory/LFM2-350M-Math-GGUF with Ollama:
ollama run hf.co/QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/LFM2-350M-Math-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/LFM2-350M-Math-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/LFM2-350M-Math-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/LFM2-350M-Math-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/LFM2-350M-Math-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/LFM2-350M-Math-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/LFM2-350M-Math-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LFM2-350M-Math-GGUF-Q4_K_M
List all available models
lemonade list
aashish1904/LFM2-350M-Math-GGUF
This is quantized version of LiquidAI/LFM2-350M-Math created using llama.cpp
Original Model Card
LFM2-350M-Math
Based on LFM2-350M, LFM2-350M-Math is a tiny reasoning model designed for tackling tricky math problems.
You can find more information about other task-specific models in this blog post.
π Model details
Generation parameters: We strongly recommend using greedy decoding with a temperature=0.6, top_p=0.95, min_p=0.1, repetition_penalty=1.05.
System prompt: We recommend not using any system prompt.
Supported languages: English only.
Chat template: LFM2 uses a ChatML-like chat template as follows:
<|startoftext|><|im_start|>user
Find the sum of all integer bases $b>9$ for which $17_{b}$ is a divisor of $97_{b}$.<|im_end|>
<|im_start|>assistant
<|cot_start|>First, we need to convert $17_{b}$ and $97_{b}$ into base 10. [...]<|im_end|>
You can automatically apply it using the dedicated .apply_chat_template() function from Hugging Face transformers.
β οΈ The model is intended for single-turn conversations.
π Performance
Reasoning enables models to better structure their thought process, explore multiple solution strategies, and self-verify their final responses. Augmenting tiny models with extensive test-time compute in this way allows them to even solve challenging competition-level math problems. Our benchmark evaluations demonstrate that LFM2-350M-Math is highly capable for its size.
As we are excited about edge deployment, our goal is to limit memory consumption and latency. Our post-training recipe leverages reinforcement learning to explicitly bring down response verbosity where it is not desirable. To this end, we combine explicit reasoning budgets with difficulty-aware advantage re-weighting. Please refer to our separate blog post for a detailed post-training recipe.
π How to run
- Hugging Face: LFM2-350M
- llama.cpp: LFM2-350M-Math-GGUF
- LEAP: LEAP model library
π¬ Contact
If you are interested in custom solutions with edge deployment, please contact our sales team.
- Downloads last month
- 989
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
Model tree for QuantFactory/LFM2-350M-Math-GGUF
Base model
LiquidAI/LFM2-350M
