README.md

---
base_model: gpt2
datasets: []
language: en
library_name: transformers
license: apache-2.0
metrics:
- loss
model_name: tiny-gpt2-1b-textgen
pipeline_tag: text-generation
tags:
- text-generation
- gpt2
- fine-tuned
- custom-dataset
widget:
- text: Once upon a time,
  example_title: Story starter
- text: The future of AI is
  example_title: Future prediction
model_description: This is a GPT-2 1B model fine-tuned on a subset of the Wikipedia
  corpus for text generation tasks. The model is capable of generating coherent and
  creative continuations given a prompt. It was trained to predict the next token
  given previous context using a causal language modeling objective.
training_data: A 1% subset of the English Wikipedia corpus was used. Data was preprocessed
  by removing formatting artifacts, tokenized using a custom GPT-2 tokenizer trained
  from scratch.
training_techniques: Standard next-token prediction (causal language modeling) was
  used. Training was conducted using AdamW optimizer with linear learning rate decay.
  Mixed precision training was enabled for efficiency.
evaluation: Evaluation focused on loss convergence and sample quality through prompt-based
  generation. The model achieved a final training loss around 3.3, indicating moderate
  learning performance given the small dataset size.
limitations: Due to limited training data (1% of Wikipedia) and model size constraints,
  the model may hallucinate facts, repeat phrases, or fail to maintain long-term coherence.
  It is not suitable for factual generation or sensitive content production.
intended_uses: This model is best suited for educational purposes, experimentation
  with fine-tuning pipelines, and basic text generation demonstrations. It is not
  intended for commercial deployment.
ethical_considerations: Users should be aware that outputs can include biased, inappropriate,
  or inaccurate information. Care should be taken when deploying outputs in sensitive
  contexts.
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is a GPT-2 1B model fine-tuned on a subset of the Wikipedia corpus for text generation tasks. The model is capable of generating coherent and creative continuations given a prompt. It was trained to predict the next token given previous context using a causal language modeling objective.

- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** apache-2.0
- **Finetuned from model [optional]:** gpt2

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

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## Training Details

### Training Data

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A 1% subset of the English Wikipedia corpus was used. Data was preprocessed by removing formatting artifacts, tokenized using a custom GPT-2 tokenizer trained from scratch.

### Training Procedure

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#### 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

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### 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]

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## Environmental Impact

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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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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