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krzysztofwos/
LFM2.5-1.2B-CodeAgent-LoRA

Name: LFM2.5-1.2B-CodeAgent-LoRA
Availability: InStock

LiquidAI/LFM2.5-1.2B-Instruct

adapter

apache-2.0

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LFM2.5-1.2B-CodeAgent-LoRA

A LoRA fine-tuned adapter for LiquidAI/LFM2.5-1.2B-Instruct trained to follow the smolagents CodeAgent format.

Model Description

This adapter teaches LFM2.5-1.2B-Instruct 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/LFM2.5-1.2B-Instruct (2.6B parameter)
Format Compliance: N/A with minimal prompt
Answer Accuracy: N/A 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 SFTTrainer
PEFT for LoRA

Evaluation Results

Prompt Mode Comparison

Prompt Mode	Format Compliance	Answer Accuracy
Minimal	N/A	N/A
Default	N/A	N/A
None	N/A	N/A

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/LFM2.5-1.2B-Instruct",
    device_map="auto",
    torch_dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "krzysztofwos/LFM2.5-1.2B-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/LFM2.5-1.2B-Instruct",
    peft_model="krzysztofwos/LFM2.5-1.2B-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 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{lfm2.5_1.2b_codeagent_lora,
  author = {krzysztofwos},
  title = {LFM2.5-1.2B-CodeAgent-LoRA},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/krzysztofwos/LFM2.5-1.2B-CodeAgent-LoRA}
}

Acknowledgments

LiquidAI for the LFM2.5-1.2B-Instruct base model
Hugging Face for smolagents, TRL, and PEFT
Training performed as part of CodeAgent prompt distillation research