pszemraj / megalodon-random-small

random weights for testing megalodon implementation

arch

about 200m params

======================================================================
Layer (type:depth-idx) Output Shape      Param #  Trainable
======================================================================
MegalodonForCausalLM             227,707,904       True
  MegalodonModel                 194,151,424       True
    Embedding                     33,554,432       True
    ModuleList                   160,594,944       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
      MegalodonBlock              13,382,912       True
    TimestepNorm                       2,048       True
  MegalodonLMHead                 33,556,480       True
    TimestepNorm                       2,048       True
    Linear                        33,554,432       True
======================================================================
Total params: 227,707,904
Trainable params: 227,707,904
Non-trainable params: --
======================================================================

testing

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("pszemraj/megalodon-random-small", trust_remote_code=True)
# Dummy input for testing
input_ids = torch.randint(0, model.config.vocab_size, (2, 50))  # (batch_size, seq_length)
attention_mask = torch.ones(2, 50)  # (batch_size, seq_length)

outputs = model(
    input_ids=input_ids, attention_mask=attention_mask, labels=input_ids
)
loss = outputs.loss
logits = outputs.logits

print(f"Loss: {loss.item()}")
print(
    f"Logits shape: {logits.shape}"
)  # Expected: (batch_size, seq_length, vocab_size)