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SPT: A Lightweight Language Model
NanoLlama is a compact language model trained on Sherlock Holmes stories.
Model Details
- Model Type: NanoLlama (Causal Language Model)
- Number of Layers: 12
- Hidden Size: 512
- Number of Attention Heads: 16
- Number of KV Heads: 16
- Intermediate Size: 2048
- Maximum Sequence Length: 2048
- Vocabulary Size: 97 (including special tokens)
Usage
You can use this model with the Hugging Face Transformers library:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("imdatta0/spt")
model = AutoModelForCausalLM.from_pretrained("imdatta0/spt")
# Generate text
input_text = "Sherlock and I were "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
Training
This model was trained on Sherlock Holmes' stories on a single A100 with a batch size of 2 and gradient accumulation steps of 32 effective batch size of 64. It was trained on 1024 length character sequences for 10000 steps.
Limitations
- The model has a limited vocabulary of 97 tokens, which may affect its performance on certain tasks or domains.
- The maximum sequence length is 2048 tokens, which may not be sufficient for very long text generation tasks.
Acknowledgements
- Thanks to Andrej Karpathy for his excellent videos on how to train GPT from scratch
- Sir Arthur Conan Doyle for the amazing stories :)
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