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HDTenEightyP/GPT-USENET-2
GPT-USENET-2 is a text generation model from HDTenEightyP. Use it when you need the model to write or continue text. The card lists the license as mit.
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Updated Dec 1, 2025
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From the Hugging Face model README

An 81-million parameter LLM using GPT-2 encodings. Trained using 10GB of USENET posts along with over 1 GB of miscellaneous BBS posts, digitized books, and text documents. Supervised fine-tuning should be performed before use.
LLMs are all currently focused on becoming larger and larger, able to do more and more. However, this just makes them jack of all trades, master of none. GPT-Usenet takes a different approach. Instead of trying to do everything perfectly, GPT-Usenet offers a digital stem cell, which can then be finetuned into a single, specialized role and run in parallel with copies of itself.
| Layers | 10 |
| Heads | 10 |
| Embeddings | 640 |
| Context Window | 1024 tokens |
| Tokenizer | GPT-2 BPE |
| Training Loss | around 2.0254 |
| Validation Loss | around 1.9795 |
| Device | Google Colab L4, Google Colab A100 |
| Training Time | 16 Hours |
| From: | The username who sent this message |
| Sender: | The group that username belongs to |
| Newsgroups: | The broad subject field of the email. |
| Subject: | The subject of the message. |
| Write the SFT response here. First, Prefix the first sentence with > to signify that it is a Reasoning sentence. | |
| -- | The stop tokens |
From:user
Sender:usergroup
Newsgroups:motorskills.papercraft
Subject:Paper airplanes
>Provide detailed steps on building a paper airplane.
Instructions: ...
--
For finetuning, your data should be in the .mbox format.