To start using these models, you can simply load them via the Hugging Face transformers library:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_NAME = "arnir0/Tiny-LLM"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(
inputs,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
do_sample=True
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
def main():
# Define your prompt
prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."
generated_text = generate_text(prompt, model, tokenizer)
print(generated_text)
if __name__ == "__main__":
main()
Files
9 files · 28.3 MBWeights 26.0 MB (safetensors). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("arnir0/Tiny-LLM", revision="b784a70a5e6908c9148820a245d60a3347279868")
model = AutoModelForCausalLM.from_pretrained("arnir0/Tiny-LLM", revision="b784a70a5e6908c9148820a245d60a3347279868", device_map="auto")
