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
Model Details
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.
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- Language(s) (NLP): en
- License: apache-2.0
- Finetuned from model [optional]: gpt2
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Training Details
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.
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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