---
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)
[](https://huggingface.co/mradermacher/LFM2.5-1.2B-Nova-Function-Calling-GGUF)
[-orange?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/mradermacher/LFM2.5-1.2B-Nova-Function-Calling-i1-GGUF)
## 🚀 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](https://huggingface.co/datasets/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.
```python
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
max_seq_length = 4096,
dtype = None,
load_in_4bit = 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, max_new_tokens = 128, use_cache = True)
print(tokenizer.batch_decode(outputs)[0].split("<|im_start|>assistant")[-1])
Expected Output:
{"name": "calculate_circle_area", "arguments": {"radius": 5}}
```
## 📥 Download GGUF (Quantized)
Thanks to **[mradermacher](https://huggingface.co/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**](https://huggingface.co/mradermacher/LFM2.5-1.2B-Nova-Function-Calling-GGUF) |
| **Imatrix GGUF** | **(Best Quality)** Importance Matrix tuned. Higher accuracy at small sizes. | **Low VRAM** devices (Android/Pi) or max quality needs. | [**Download**](https://huggingface.co/mradermacher/LFM2.5-1.2B-Nova-Function-Calling-i1-GGUF) |
### ⚙️ 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.
Built with ❤️ by
NovachronoAI using
Unsloth
