Update README.md

README.md

@@ -1,3 +1,101 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- LiquidAI/LFM2.5-1.2B-Base
+pipeline_tag: text-classification
+---
+
+# 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
+
+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**.