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Vishwas1/tinyllm-cpu-char
tinyllm-cpu-char is a text generation model from Vishwas1. Use it when you need the model to write or continue text. It is set up for pytorch. The card lists the license as mit.
A tiny CPU-trained character-level GPT-style language model created as an educational end-to-end LLM experiment.
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Updated May 19, 2026
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From the Hugging Face model README
A tiny CPU-trained character-level GPT-style language model created as an educational end-to-end LLM experiment.
This is not a general chatbot. It is a deliberately small model trained to demonstrate the full loop:
This repo contains checkpoints created locally by us. We did not pull external model weights or external quote datasets into the training machine for the philosophy experiments. The philosophy corpus was extracted from existing local workspace philosophy book material, and a provenance manifest is included at data/philosophy_quotes_local_manifest.json.
checkpoints/tinyllm_overfit_3k.ptdata/tiny_corpus.txt0.1903, val 3.7094These were trained from local-only quote-style corpora derived from existing workspace philosophy files. They are intentionally tiny and educational.
| Checkpoint | Corpus | Params | Final loss | Notes |
|---|---|---|---|---|
checkpoints/tinyllm_philosophy_quotes_local_3k.pt | 1200 lines | 111,040 | train 1.9311, val 1.9449 | Learns broad philosophy texture; not overfit. |
checkpoints/tinyllm_philosophy_quotes_tiny200_4k.pt | 200 lines | 110,720 | train 1.5459, val 1.8426 | Stronger style learning; still not memorized. |
checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt | 50 lines | 107,840 | train 0.3808, val 2.5139 | Best overfit-ish quote-style checkpoint. |
pip install torch huggingface_hub
python hf_infer.py --repo-id Vishwas1/tinyllm-cpu-char \
--filename checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt \
--prompt "Wisdom" --tokens 300
For local inference after cloning:
python sample.py --ckpt checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt \
--prompt "The soul" --tokens 300
Original toy overfit:
python train.py --steps 3000 --eval-interval 500 --eval-iters 10 \
--batch-size 16 --n-embd 64 --block-size 64 \
--out checkpoints/tinyllm_overfit_3k.pt
Local philosophy tiny50 overfit-ish run:
python train.py --data data/philosophy_quotes_local_tiny50.txt \
--steps 3000 --eval-interval 500 --eval-iters 10 \
--batch-size 16 --n-embd 64 --block-size 64 --lr 0.001 \
--out checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt