You can deploy this model and start earning money today!

sosier/
nanoGPT-shakespeare-char-weights-not-tied

Name: nanoGPT-shakespeare-char-weights-not-tied
Availability: InStock

Views

No views yet

nanoGPT - Character-Level Shakespeare - Weights NOT Tied

Small character-level, GPT-style language model trained on the works of Shakespeare using Andrej Karpathy's nanoGPT repo from my project LLMs Universally Learn a Feature Representing Token Frequency / Rarity.

Versions

This model has two versions:

With tied embedding / unembedding weights (in true GPT fashion)
Without tied embedding / unembedding weights - THIS PAGE

Usage

The model can be loaded using AutoModel from Hugging Face's transformers package:

python

>>> from transformers import AutoModel
>>> model = AutoModel.from_pretrained("sosier/nanoGPT-shakespeare-char-weights-not-tied", trust_remote_code=True)
>>> model
number of parameters: 10.67M

NanoGPT(
  (transformer): ModuleDict(
    (wte): Embedding(65, 384)
    (wpe): Embedding(256, 384)
    (drop): Dropout(p=0.2, inplace=False)
    (h): ModuleList(
      (0-5): 6 x Block(
        (ln_1): LayerNorm()
        (attn): CausalSelfAttention(
          (c_attn): Linear(in_features=384, out_features=1152, bias=False)
          (c_proj): Linear(in_features=384, out_features=384, bias=False)
          (attn_dropout): Dropout(p=0.2, inplace=False)
          (resid_dropout): Dropout(p=0.2, inplace=False)
        )
        (ln_2): LayerNorm()
        (mlp): MLP(
          (c_fc): Linear(in_features=384, out_features=1536, bias=False)
          (gelu): GELU(approximate='none')
          (c_proj): Linear(in_features=1536, out_features=384, bias=False)
          (dropout): Dropout(p=0.2, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm()
  )
  (lm_head): Linear(in_features=384, out_features=65, bias=False)
)

Training Data / Token Counts

The training data token counts can be found on my GitHub repo here and can be loaded using the instructions here.

Tokenizer

As a character-level model the tokenizer is simply a mapping for each character to its token id as given in the token counts (see section above).