Update README.md

README.md

@@ -5,24 +5,38 @@ datasets:
 - ai4bharat/naamapadam
 language:
 - bn
+base_model:
+- openai-community/gpt2
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+AddaGPT 2.0 is a Bengali language model based on GPT-2, fine-tuned using LoRA adapters for academic and low-resource applications. While GPT-2 was originally trained only on English data, this model has been adapted to Bengali using the AI4Bharat NaamaPadam dataset — a corpus focused on Named Entity Recognition (NER).
+This project is intended as a proof of concept to explore how small, pretrained models like GPT-2 can be extended to Indic languages using low-rank adaptation (LoRA) techniques, even under limited compute settings (e.g., free Kaggle GPUs). It lays the foundation for future work in adapting language models for low-bandwidth, regional, and offline-first use cases — where even partial language understanding can support local communities.
 
+## Model Details
+| **Attribute**                      | **Description**                                                                                                        |
+| ---------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
+| **Base Model**                     | GPT-2 (117M parameters)                                                                                                |
+| **Fine-tuned Using**               | [LoRA (Low-Rank Adaptation)](https://arxiv.org/abs/2106.09685)                                                         |
+| **Language**                       | Bengali (`bn`)                                                                                                         |
+| **Training Dataset**               | [`ai4bharat/naamapadam`](https://huggingface.co/datasets/ai4bharat/naamapadam) – Bengali NER corpus (train split only) |
+| **Sentences Seen During Training** | \~9.6 million Bengali sentences                                                                                        |
+| **Training Platform**              | Kaggle (Free T4 GPUs)                                                                            |
+| **Frameworks**                     | 🤗 Transformers + PEFT (Parameter-Efficient Fine-Tuning) + Safetensors                                                 |
+| **Trainable Parameters**           | 294,912                                                                                                                |
+| **Total Parameters**               | 124,734,720                                                                                                            |
+| **Percentage Fine-Tuned**          | 0.2364%                                                                                                                |
 
 
-## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [Swastik Guha Roy]
-- **Funded by [optional]:** [More Information Needed]
+- **Developed by:** Swastik Guha Roy
+- **Funded by [optional]:** Self Funded
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
@@ -61,9 +75,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This model is not capable of generating grammatically or syntactically correct Bengali sentences. Instead, it outputs individual Bengali words or word-like tokens that are often meaningful on their own — a direct result of training on a NER-style dataset rather than full natural language text.
 
 ### Recommendations