---
library_name: peft
base_model: LiquidAI/LFM2-2.6B-Exp
tags:
- lora
- sft
- trl
- code-agent
- smolagents
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

# LFM2-2.6B-CodeAgent-LoRA

A LoRA fine-tuned adapter for [LiquidAI/LFM2-2.6B-Exp](https://huggingface.co/LiquidAI/LFM2-2.6B-Exp) trained to follow the [smolagents](https://github.com/huggingface/smolagents) CodeAgent format.

## Model Description

This adapter teaches LFM2-2.6B-Exp to respond in the structured Thought + Code format required by smolagents CodeAgent:

````
Thought: I need to calculate this.
```python
result = 2 + 2
final_answer(result)
```
````

### Key Features

- **Base Model**: LiquidAI/LFM2-2.6B-Exp (2.6B parameter hybrid architecture with LIV convolution + GQA)
- **Format Compliance**: 100% with minimal prompt
- **Answer Accuracy**: 80% on evaluation tasks
- **Adapter Size**: ~49MB (LoRA rank=8, alpha=16)

## Training Details

### Training Data

- **130 successful CodeAgent trajectories** generated using Claude 3.5 Sonnet as the teacher model
- Tasks include mathematical reasoning, string manipulation, and general problem-solving
- Each trajectory demonstrates the Thought → Code → Observation → final_answer pattern

### Training Configuration

| Parameter | Value |
|-----------|-------|
| LoRA Rank | 8 |
| LoRA Alpha | 16 |
| Target Modules | q_proj, v_proj |
| Trainable Parameters | 12.2M (0.47% of base) |
| Training Steps | 30 |
| Learning Rate | 2e-4 |
| Batch Size | 4 |
| Max Sequence Length | 2048 |
| Hardware | NVIDIA RTX 3090 (24GB) |
| Training Time | ~5.5 hours |

### Training Framework

- [TRL](https://github.com/huggingface/trl) SFTTrainer
- [PEFT](https://github.com/huggingface/peft) for LoRA

## Evaluation Results

### Prompt Mode Comparison

| Prompt Mode | Format Compliance | Answer Accuracy |
|-------------|-------------------|-----------------|
| **Minimal** | 100% | 80% |
| Default | 80% | 80% |
| None | 0% | 0% |

The model performs best with the **minimal prompt** (~95 tokens), demonstrating successful prompt distillation.

### Minimal Prompt Template

````text
You are a CodeAgent that solves tasks by writing and executing Python code.

Always respond with Thought + Python code block. Example:

Thought: I need to calculate this.
```python
result = 2 + 2
final_answer(result)
```

Call final_answer(result) when done. Now Begin!
````

## Usage

### With PEFT

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "LiquidAI/LFM2-2.6B-Exp",
    device_map="auto",
    torch_dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-2.6B-Exp")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "krzysztofwos/LFM2-2.6B-CodeAgent-LoRA")

# Generate
messages = [{"role": "user", "content": "What is 15 * 23?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.3,
    min_p=0.15,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### With smolagents

```python
from smolagents import CodeAgent, FinalAnswerTool, TransformersModel

model = TransformersModel(
    model_id="LiquidAI/LFM2-2.6B-Exp",
    peft_model="krzysztofwos/LFM2-2.6B-CodeAgent-LoRA",
)

agent = CodeAgent(
    tools=[FinalAnswerTool()],
    model=model,
)

result = agent.run("What is 15 * 23?")
print(result)
```

## Intended Use

- Code-assisted problem solving
- Mathematical reasoning tasks
- Automated code generation following structured formats
- Research into prompt distillation and small model fine-tuning

## Limitations

- **Requires specific prompt format**: Works best with minimal prompt template
- **Limited reasoning depth**: 2.6B parameter hybrid architecture with LIV convolution + GQA model has constrained reasoning capabilities compared to larger models
- **English only**: Trained on English-language tasks

## Citation

If you use this model, please cite:

```bibtex
@misc{lfm2_2.6b_codeagent_lora,
  author = {krzysztofwos},
  title = {LFM2-2.6B-CodeAgent-LoRA},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/krzysztofwos/LFM2-2.6B-CodeAgent-LoRA}
}
```

## Acknowledgments

- [LiquidAI](https://www.liquid.ai/) for the LFM2-2.6B-Exp base model
- [Hugging Face](https://huggingface.co/) for smolagents, TRL, and PEFT
- Training performed as part of CodeAgent prompt distillation research