Hugging Face model: HuangXinBa/tiny-gpt2-1b-textgen
Author: HuangXinBa
Created: 2025-04-27 17:23:28
Last modified: 2025-04-27 17:34:36
Pipeline: text-generation
Library: transformers
Likes: 0 | Downloads: 20
Tags: transformers, safetensors, gpt2, text-generation, fine-tuned, custom-dataset, en, arxiv:1910.09700, base_model:openai-community/gpt2, base_model:finetune:openai-community/gpt2, license:apache-2.0, text-generation-inference, endpoints_compatible, region:us
Base model relation: finetune
Base models: id: openai-community/gpt2
Card metadata:
base_model: gpt2
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
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.