distil-lfm25-shellper

A fine-tuned version of LiquidAI/LFM2.5-350M for multi-turn shell command execution via tool calling, trained using the distil labs platform.

This model converts natural language requests into bash commands via structured tool calls, based on the Berkeley Function Calling Leaderboard Gorilla file system task.

Results

Metric	Teacher (120B)	LFM2.5-350M Base	LFM2.5-350M Tuned
Tool Call Equivalence	97.03%	61.4%	98.0%
ROUGE	94.42%	91.8%	99.4%

The tuned 350M model exceeds the 120B teacher by nearly a full percentage point.

Training Details

Parameter	Value
Base model	LiquidAI/LFM2.5-350M
Teacher model	GPT-oss-120B
Task type	Multi-turn tool calling (closed-book)
Training data	distil-labs/distil-SHELLper
Training method	SFT with LoRA
Platform	distil labs

Training Progress

Epoch	Tool Call Equivalence
0 (base)	61.4%
1	98.0%
2	97.0%
3	98.0%
4	98.0%

Usage

This model uses the LFM2.5 tool calling format with <|tool_call_start|> and <|tool_call_end|> tags:

<|tool_call_start|>[function_name(arg1="value1", arg2=42)]<|tool_call_end|>

Deployment

The model works with Ollama, vLLM, llama.cpp, or any inference runtime that supports Safetensors. For quantized deployment, use the GGUF, ONNX, or MLX variants of the base model as a starting point.

Blog Post

For the full writeup, see: Fine-Tuning Liquid's LFM2.5: Accurate Tool Calling at 350M Parameters

License

This model is licensed under the LFM Open Model License v1.0.