krzysztofwos/LFM25-1.2B-CodeAgent-opus4-terse

LFM25-1.2B-CodeAgent-opus4-terse

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.

result = 2 + 2 final_answer(result)

Key Features

• —Base Model: LiquidAI/LFM2.5-1.2B-Instruct (1.2B parameter)
• —Format Compliance: N/A with minimal prompt
• —Answer Accuracy: N/A on evaluation tasks
• —Adapter Size: ~47MB (LoRA rank=16, alpha=32)

Training Details

Training Data

• —130 successful CodeAgent trajectories generated using Claude Opus 4.5 (terse prompt) 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

Training Framework

• —TRL SFTTrainer
• —PEFT for LoRA

Evaluation Results

Prompt Mode Comparison

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

Minimal Prompt Template

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.

result = 2 + 2 final_answer(result)

Call final_answer(result) when done. Now Begin!

Usage

With PEFT

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/LFM25-1.2B-CodeAgent-opus4-terse")

# 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

from smolagents import CodeAgent, FinalAnswerTool, TransformersModel

model = TransformersModel(
    model_id="LiquidAI/LFM2.5-1.2B-Instruct",
    peft_model="krzysztofwos/LFM25-1.2B-CodeAgent-opus4-terse",
)

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: 1.2B 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:

@misc{lfm25_1.2b_codeagent_opus4_terse,
  author = {krzysztofwos},
  title = {LFM25-1.2B-CodeAgent-opus4-terse},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/krzysztofwos/LFM25-1.2B-CodeAgent-opus4-terse}
}

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