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SrihariV/Morsent-128-mini
Morsent-128-mini is a machine learning model from SrihariV. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
--- license: mit libraryname: transformers language: en tags: - gpt2 - morse-code - text-to-morse - tars-humor - mini-llm - fine-tuned ---
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Updated Apr 1, 2025
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.safetensors21 MB · 85%
From the Hugging Face model README
license: mit library_name: transformers language: en tags:
Morsent-128T is a tiny fine-tuned language model based on GPT-2 that’s trained to:
It's meant to be small, weird, and a little bit wonderful.
| Feature | Details |
|---|---|
| Base Model | GPT-2 |
| Context Length | 128 tokens |
| Tokenizer | Custom-trained |
| Fine-tuned Tasks | Morse translation, decoding, TARS-style humor |
| Intended Use | Educational, demo, fun! |
| License | MIT |
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
model = GPT2LMHeadModel.from_pretrained("SrihariV/Morsent-128T")
tokenizer = GPT2TokenizerFast.from_pretrained("SrihariV/Morsent-128T")
prompt = "Translate: emergency to Morse code [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
##🧪 Known Issues 🚫 Max length is short: 128 tokens only 🤯 Sometimes responds with hallucinated or surreal outputs 😅 TARS-style jokes may be too weird
#📦 Files pytorch_model.bin — fine-tuned weights tokenizer.json, tokenizer_config.json — custom tokenizer config.json — model architecture
Morse code from historical datasets Instruction-tuning format inspired by [LLaMA-Instruct] TARS-style humor inspired by sci-fi language modeling
@misc{morsent2025,
title = {Morsent-128T: A Tiny Morse & Humor LLM},
author = {Srihari Venkatesan},
year = 2025,
howpublished = {\url{https://huggingface.co/SrihariV/Morsent-128T}},
}
🗣 Contact Questions? Bugs? Want to collab?
📫 Reach me at: [email protected]