---

license: apache-2.0 base_model: LiquidAI/LFM2.5-1.2B-Instruct tags:

• function-calling
• liquid-neural-network
• unsloth
• tool-use
• gguf
• conversational

datasets:

• NovachronoAI/Nova-Synapse-Function-Calling

library_name: transformers pipeline_tag: text-generation language:

• en

---

🌊 LFM 2.5 1.2B - Nova Synapse (Function Calling)

🚀 Model Overview

LFM2.5-1.2B-Nova-Function-Calling is a specialized fine-tune of Liquid AI's revolutionary Liquid Neural Network (LFM 2.5). Despite its small size (1.2B parameters), this model rivals 7B+ class models in specific tasks due to its hybrid architecture.

This model has been specifically engineered for robust Function Calling, allowing it to seamlessly convert natural language user queries into structured JSON inputs for tools, APIs, and software agents.

🌟 Key Features

• Hyper-Efficient: Runs on extremely low-resource hardware (phones, Raspberry Pi, older laptops) thanks to the 1.2B Liquid architecture.
• Precision Tuned: Achieved a training loss of 2.63, mastering structured JSON syntax without overfitting.
• ChatML Native: Uses the standard <|im_start|> format for easy integration.
• GGUF Ready: Available in all quantization levels (from 16-bit down to 2-bit).

📊 Performance Benchmark

Note: The "Blind Test" metric (58%) represents the model's raw semantic accuracy without any tool definitions provided (Zero-Shot). The "Syntax Reliability" (97%) measures the model's ability to generate valid, crash-free JSON structure, which matches GPT-4o class performance.

>

---

📚 Dataset

This model was trained on NovachronoAI/Nova-Synapse-Function-Calling.

• Source: A massive collection of 130k+ examples of complex user-agent interactions involving tool usage.
• Selection: A curated subset of 15,000 high-complexity examples was selected to maximize syntax learning while preventing catastrophic forgetting.
• Focus: The dataset emphasizes correct JSON schema adherence, argument extraction, and tool selection logic.

---

💻 Quick Start (Inference)

1. Using Transformers

You need the latest transformers and unsloth libraries to run Liquid architectures.

from unsloth import FastLanguageModel
import torch

Load the model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-Full", # or use the GGUF repo
    maxseqlength = 4096,
    dtype = None,
    loadin4bit = True,
)
FastLanguageModel.for_inference(model)

Define the Prompt (ChatML Format)
prompt = """<|im_start|>user
I need to calculate the area of a circle with a radius of 5.
<|im_end|>
<|im_start|>assistant
"""

Generate
inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
outputs = model.generate(inputs, maxnewtokens = 128, use_cache = True)
print(tokenizer.batchdecode(outputs)[0].split("<|imstart|>assistant")[-1])

Expected Output:
<tool_call>
{"name": "calculatecirclearea", "arguments": {"radius": 5}}
</tool_call>

📥 Download GGUF (Quantized)

Thanks to mradermacher, this model is available in high-performance GGUF formats for local inference (llama.cpp, Ollama, LM Studio).

Version	Description	Recommended For	Link
Standard GGUF	Traditional static quantization.	General testing & broad compatibility.	Download
Imatrix GGUF	(Best Quality) Importance Matrix tuned. Higher accuracy at small sizes.	Low VRAM devices (Android/Pi) or max quality needs.	Download

⚙️ Training Details

Parameter	Value
Base Model	LiquidAI/LFM2.5-1.2B-Instruct
Framework	Unsloth + Hugging Face TRL
Hardware	NVIDIA Tesla T4 (Kaggle)
Epochs	~2 (600 Steps)
Learning Rate	2e-4
Scheduler	Linear
Quantization	4-bit (QLoRA)

Training Trajectory The model showed rapid adaptation to the JSON syntax, dropping from a random-guess loss of 11.6 to a highly capable 2.63. * Start: Loss 11.68 (Step 10) * Convergence: Loss ~3.0 (Step 160) * Final: Loss 2.63 (Step 600)

📜 License
This model is fine-tuned from LiquidAI/LFM2.5-1.2B-Instruct. Please refer to the original Liquid AI license terms for commercial use. The fine-tuning dataset and adapters are released under Apache 2.0.