> ## 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.

# LFM2-1.2B-Tool

> 1.2B parameter model for tool calling (deprecated)

<a href="/lfm/models/liquid-nanos" className="back-button">← Back to Liquid Nanos</a>

<Warning>
  This model is deprecated. Use [LFM2.5-1.2B-Instruct](/lfm/models/lfm25-1.2b-instruct) for tool calling instead, which offers improved accuracy and follows the standard tool use format.
</Warning>

LFM2-1.2B-Tool was optimized for efficient and precise tool calling. It has been superseded by LFM2.5-1.2B-Instruct which provides better tool calling performance alongside general chat capabilities.

<div style={{display: 'flex', gap: '0.5rem', margin: '0.5rem 0 1.5rem 0'}}>
  <a href="https://huggingface.co/LiquidAI/LFM2-1.2B-Tool" style={{padding: '0.35rem 0.7rem', borderRadius: '4px', fontSize: '0.85rem', fontWeight: 600, textDecoration: 'none', backgroundColor: '#fbbf24'}}><span style={{color: '#000'}}>HF</span></a>
  <a href="https://huggingface.co/LiquidAI/LFM2-1.2B-Tool-GGUF" style={{padding: '0.35rem 0.7rem', borderRadius: '4px', fontSize: '0.85rem', fontWeight: 600, textDecoration: 'none', backgroundColor: '#60a5fa'}}><span style={{color: '#000'}}>GGUF</span></a>
  <a href="https://huggingface.co/onnx-community/LFM2-1.2B-Tool-ONNX" style={{padding: '0.35rem 0.7rem', borderRadius: '4px', fontSize: '0.85rem', fontWeight: 600, textDecoration: 'none', backgroundColor: '#86efac'}}><span style={{color: '#000'}}>ONNX</span></a>
</div>

## Specifications

| Property       | Value        |
| -------------- | ------------ |
| Parameters     | 1.2B         |
| Context Length | 32K tokens   |
| Task           | Tool Calling |
| Status         | Deprecated   |

## Migration Guide

For tool calling, use LFM2.5-1.2B-Instruct instead:

```python theme={null}
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LiquidAI/LFM2.5-1.2B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# See the Tool Use guide for complete examples
```

See the [Tool Use](/lfm/key-concepts/tool-use) guide for detailed tool calling documentation.