| --- |
| 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 |
| - open4bits |
| 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**. |
