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README.md

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----
+license: mit
+language: en
 library_name: transformers
-tags: []
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+tags:
 
-# Model Card for Model ID
+text-generation
 
-<!-- Provide a quick summary of what the model is/does. -->
+foundation-model
 
+gpt2
 
+from-scratch
 
-## Model Details
+ag_news
 
-### Model Description
+Foundation Model: Adbhut
+Adbhut is a miniature, from-scratch autoregressive language model based on the GPT-2 architecture. This model was pre-trained on a small sample of the ag_news dataset as part of a learning exercise to demonstrate the end-to-end process of creating and sharing a foundation model.
 
-<!-- Provide a longer summary of what this model is. -->
+This model is intended for educational purposes only. It showcases the fundamental pipeline of data preparation, tokenizer training, model pre-training, and deployment on the Hugging Face Hub.
 
-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: rohitnagareddy
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Model type: Causal language model (decoder-only transformer)
 
-### Model Sources [optional]
+Language: English
 
-<!-- Provide the basic links for the model. -->
+License: MIT
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+How to Use
+The model can be easily loaded for text generation using the transformers library pipeline.
 
-## Uses
+from transformers import pipeline
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Load the model from the Hugging Face Hub
+generator = pipeline('text-generation', model='rohitnagareddy/Adbhut')
 
-### Direct Use
+# Generate text
+prompt = "The world of technology is"
+output = generator(
+    prompt,
+    max_length=50,
+    num_return_sequences=1,
+    no_repeat_ngram_size=2  # Add this to prevent simple repetition
+)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+print(output[0]['generated_text'])
 
-[More Information Needed]
+Model Architecture
+Adbhut is a very small GPT-2 style model with the following configuration:
 
-### Downstream Use [optional]
+Number of layers: 2
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Embedding dimension: 128
 
-[More Information Needed]
+Number of attention heads: 2
 
-### Out-of-Scope Use
+Vocabulary size: 5,000
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Maximum sequence length: 128 positions
 
-[More Information Needed]
+Due to its small size, the model has approximately 1.5 million parameters.
 
-## Bias, Risks, and Limitations
+Training Details
+Training Data
+The model was pre-trained on a small, shuffled sample of the ag_news dataset.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Dataset: ag_news
 
-[More Information Needed]
+Sample Size: 2,000 articles
 
-### Recommendations
+Preprocessing: Each article's text was extracted and used as a separate line in the training corpus.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Training Procedure
+The model was pre-trained using the Hugging Face Trainer on a single GPU.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Framework: PyTorch
 
-## How to Get Started with the Model
+Training Steps: 50
 
-Use the code below to get started with the model.
+Batch Size: 8
 
-[More Information Needed]
+Optimizer: AdamW (default from Trainer)
 
-## Training Details
+Objective: Causal Language Modeling (predicting the next token).
 
-### Training Data
+Limitations and Intended Use
+This model is a proof-of-concept and is not suitable for any real-world application.
 
-<!-- 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. -->
+The primary goal of this project was to learn and demonstrate the training pipeline, not to create a state-of-the-art model. As a result, it has significant limitations:
 
-[More Information Needed]
+Repetitive and Incoherent Output: Due to the extremely short training time (50 steps) and tiny dataset, the model has not learned complex grammatical or semantic patterns. Its output is often repetitive and may not make logical sense.
 
-### Training Procedure
+Limited Knowledge: The model's "world knowledge" is confined to the 2,000 news articles it was trained on. It cannot answer questions or discuss topics outside this limited scope.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Bias: The model will reflect the biases present in the ag_news dataset.
 
-#### Preprocessing [optional]
+No Safety Alignment: This is a raw, pre-trained base model. It has not undergone any instruction tuning or safety alignment (like RLHF). It may produce undesirable or nonsensical text.
 
-[More Information Needed]
-
-
-#### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+The intended use is for others to study the code and the training process, and to use it as a template for training their own small-scale language models.