LFM2-350M-Math

350M parameter model for math problem solving

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LFM2-350M-Math is a tiny reasoning model optimized for mathematical problem solving. It provides step-by-step solutions while maintaining a small footprint for edge deployment.

HFGGUFONNX

Specifications

Property	Value
Parameters	350M
Context Length	32K tokens
Task	Mathematical Reasoning

Problem Solving

Step-by-step math

Edge Deployment

Tiny model footprint

Education

Tutoring and explanations

Quick Start

Transformers

Install:

pip install "transformers>=5.2.0" torch accelerate

Run:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LiquidAI/LFM2-350M-Math"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "user", "content": "If a train travels at 60 mph for 2.5 hours, how far does it travel?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

llama.cpp

Download GGUF:

hf download LiquidAI/LFM2-350M-Math-GGUF \
  --local-dir ./LFM2-350M-Math-GGUF

Run:

llama-cli -m ./LFM2-350M-Math-GGUF/LFM2-350M-Math-Q4_K_M.gguf \
  -p "Solve: 2x + 5 = 15"