Instructions to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF")
model = AutoModelForCausalLM.from_pretrained("NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF")

llama-cpp-python

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF",
	filename="LFM2.5-1.2B-Nova-Function-Calling.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

SGLang

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with Ollama:
```
ollama run hf.co/NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M
```

Unsloth Studio new

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF to start chatting

Pi new

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with Docker Model Runner:
```
docker model run hf.co/NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M
```

Lemonade

How to use NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Nova-Function-Calling-GGUF-Q4_K_M

List all available models

lemonade list

🌊 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
    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:
<tool_call>
{"name": "calculate_circle_area", "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.

Built with ❤️ by NovachronoAI using Unsloth

Downloads last month: 11,313

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for NovachronoAI/LFM2.5-1.2B-Nova-Function-Calling-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(49)

this model

NovachronoAI
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LFM2.5-1.2B-Nova-Function-Calling-GGUF