55 lines
1.4 KiB
Markdown
55 lines
1.4 KiB
Markdown
---
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license: mit
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datasets:
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- HuggingFaceFW/fineweb
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pipeline_tag: text-generation
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---
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# Tiny-LLM
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A Tiny LLM model with just 10 Million parameters, this is probably one of the small LLM arounds, and it is functional.
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## Pretraining
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Tiny-LLM was trained on 32B tokens of the Fineweb dataset, with a context length of 1024 tokens.
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## Getting Started
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To start using these models, you can simply load them via the Hugging Face `transformers` library:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_NAME = "arnir0/Tiny-LLM"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
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def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(
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inputs,
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max_length=max_length,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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do_sample=True
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return generated_text
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def main():
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# Define your prompt
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prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."
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generated_text = generate_text(prompt, model, tokenizer)
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print(generated_text)
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if __name__ == "__main__":
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main()
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``` |