SwastikGuhaRoy
/

AddaGPT2.0

@@ -12,7 +12,7 @@ base_model:
 # Model Card for Model ID
 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**                                                                                                        |
@@ -32,89 +32,77 @@ This project is intended as a proof of concept to explore how small, pretrained
 ### Model Description
-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]:** Self Funded
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
 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
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
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 ## Evaluation
@@ -122,94 +110,18 @@ Use the code below to get started with the model.
 ### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-#### Metrics
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 ### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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 # Model Card for Model ID
 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 — to support local communities.
 ## Model Details
 | **Attribute**                      | **Description**                                                                                                        |
 ### Model Description
 - **Developed by:** Swastik Guha Roy
+- **Funded by   :** Self Funded
+### Uses
+AddaGPT 2.0 is an academic proof-of-concept project designed to explore how low-resource, low-compute setups (like Kaggle T4 GPUs) can be used to adapt large language models like GPT-2 for Indic languages, specifically Bengali.
+### Intended Use Cases:
+Academic research on low-rank adaptation (LoRA) for regional languages
+Language modeling experimentation in Bengali
+Demonstration of fine-tuning techniques in resource-constrained environments
+Baseline comparison for future Bengali language model development
+Educational purposes for students and ML enthusiasts working on low-resource NLP
+### Intended Users:
+ML/NLP researchers exploring parameter-efficient tuning
+Students building regional language models
+Developers prototyping Bengali language tools (with limitations)
+Community contributors interested in advancing open-source Bengali AI
+## Limitations
 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.
+->This version does not produce grammatically coherent Bengali sentences
+->It's trained on a NER dataset, so it mostly outputs individual Bengali words
+->It is not suitable for downstream tasks like summarization, translation, or question-answering — yet
+### How to Get Started with the Model
+# Load Nessecary Libraries
+  ```python
+     from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+  ```
+# Load the model and tokenizer
+```python
+  model = AutoModelForCausalLM.from_pretrained("SwastikGuhaRoy/AddaGPT2.0")
+  tokenizer = AutoTokenizer.from_pretrained("SwastikGuhaRoy/AddaGPT2.0_tokenizer")
+```
+# Initialize generation pipeline
+  ```python
+  text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+ ```
+# Run inference
+``` python
+  prompt = "রবীন্দ্রনাথ ঠাকুর একজন"
+  output = text_generator(
+      prompt,
+      max_new_tokens=30,
+      temperature=0.7,
+      top_p=0.95,
+      do_sample=True
+  )
+  print(output[0]["generated_text"])
+```
 ## Evaluation
 ### Testing Data, Factors & Metrics
 ### Results
+The model was evaluated on the validation split of the ai4bharat/naamapadam dataset to measure how well it models Bengali text.
+## Metric: Perplexity (Lower is Better)
+Model	Validation Perplexity
+AddaGPT 2.0	25.61
+Vanilla GPT-2 (English)	144.53
+## AddaGPT 2.0 shows a significantly lower perplexity, indicating a better fit to Bengali text.
+## GPT-2 struggles with Bengali due to the lack of Bengali data during pretraining.
+## Summary
+Despite lower perplexity, the model still generates mostly isolated Bengali words, not grammatically complete sentences (due to the nature of the training dataset — a NER corpus).