---
license: other
language:
- en
- ar
- zh
- fr
- de
- ja
- ko
- es
base_model:
- LiquidAI/LFM2.5-1.2B-Base
library_name: transformers
tags:
- liquid
- lfm2
- lfm2.5
pipeline_tag: text-generation
---

# Open4bits / LFM2.5-1.2B-Base-Quantized

This repository provides **multiple quantized variants** of the **LFM 2.5 Base (1.2B parameters)** model for efficient inference and deployment.

The **original model** is developed and released by **LiquidAI**:

Original model:  
https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base

These quantizations are maintained and published by **ArkAiLab** under the **Open4bits** organization to improve accessibility across a wide range of hardware.

---

## Available Quantization Formats

Each format is stored in a **separate directory**:

- **FP16** – Baseline half-precision weights
- **FP8** – High-performance low-precision format (GPU support required)
- **INT8** – Balanced performance and memory usage (BitsAndBytes)
- **NF4 (4-bit)** – Maximum compression using BitsAndBytes double quant

---

## Model Information

- **Model Name:** LFM 2.5 Base
- **Parameters:** ~1.2B
- **Architecture:** Custom LiquidAI architecture
- **Original Author:** LiquidAI
- **Quantized By:** ArkAiLab (Open4bits)

This model **requires** `trust_remote_code=True` when loading.

---

## Quantization Details

- Quantized using **PyTorch** and **Hugging Face Transformers**
- INT8 and NF4 formats use **BitsAndBytes**
- FP8 provided where hardware support allows
- No GPTQ, AWQ, or llama.cpp used
- Safe for **Google Colab** and **Kaggle**

---

## Usage Example

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Open4bits/LFM2.5-1.2B-Base-Quantized"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    device_map="auto"
)

inputs = tokenizer("Hello, world!", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## Organization

This repository is maintained by **ArkAiLab** under the **Open4bits** initiative.

ArkAiLab (Main Organization):
https://huggingface.co/ArkAiLab-Adl

Open4bits (Quantization Projects):
https://huggingface.co/Open4bits

---

## License

This repository follows the **same license** as the original LiquidAI model.

Please refer to the original model repository for full licensing details.

---

## Disclaimer

This is an **unofficial quantized release**.

All credit for the original model architecture and training goes to **LiquidAI**.