> ## Documentation Index
> Fetch the complete documentation index at: https://liquidai-example-leonie-demo.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# LFM2-350M-Math
> 350M parameter model for math problem solving
← Back to Liquid Nanos
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.
## Specifications
| Property | Value |
| -------------- | ---------------------- |
| Parameters | 350M |
| Context Length | 32K tokens |
| Task | Mathematical Reasoning |
Step-by-step math
Tiny model footprint
Tutoring and explanations
## Quick Start
**Install:**
```bash theme={null}
pip install "transformers>=5.2.0" torch accelerate
```
**Run:**
```python theme={null}
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)
```
**Download GGUF:**
```bash theme={null}
hf download LiquidAI/LFM2-350M-Math-GGUF \
--local-dir ./LFM2-350M-Math-GGUF
```
**Run:**
```bash theme={null}
llama-cli -m ./LFM2-350M-Math-GGUF/LFM2-350M-Math-Q4_K_M.gguf \
-p "Solve: 2x + 5 = 15"
```