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---
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language:
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- en
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license: apache-2.0
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tags:
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- gpt2
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- pytorch
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- causal-lm
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- text-generation
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- fineweb
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datasets:
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- HuggingFaceFW/fineweb-edu
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---
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# LiteGPT-Base
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This is a **124M parameter** Language Model (GPT-2 Small architecture) pre-trained from scratch on the **FineWeb-Edu** dataset.
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It is the base model for [LiteGPT-Instruct](https://huggingface.co/koganrath/LiteGPT-Instruct).
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## Model Details
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- **Architecture**: GPT-2 Small (12 layers, 12 heads, 768 embedding dim)
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- **Parameters**: ~124 Million
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- **Context Length**: 1024 tokens
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- **Training Data**: 10 Billion tokens from [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) (Sample 10BT).
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- **Tokenizer**: GPT-2 (TikToken)
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## Usage
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This is a **completion model**. It predicts the next tokens based on the input text. It is NOT an instruction-following model (chatbot).
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### Python Example
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```python
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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model = GPT2LMHeadModel.from_pretrained("koganrath/LiteGPT-Base")
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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text = "Once upon a time in a digital world,"
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Limitations
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- **Size**: 124M parameters is small by modern standards.
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- **Coherence**: Long-form generation may lose coherence.
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- **Knowledge**: Limited to the training data cut-off and scope.
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## Authors
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Trained by **koganrath** as part of the LiteGPT Project.
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