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37 lines
1.0 KiB
37 lines
1.0 KiB
from swarms.models import HuggingfaceLLM
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import torch
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try:
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inference = HuggingfaceLLM(
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model_id="gpt2",
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quantize=False,
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verbose=True,
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)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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inference.model.to(device)
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prompt_text = (
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"Create a list of known biggest risks of structural collapse"
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" with references"
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)
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inputs = inference.tokenizer(prompt_text, return_tensors="pt").to(
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device
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)
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generated_ids = inference.model.generate(
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**inputs,
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max_new_tokens=1000, # Adjust the length of the generation
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temperature=0.7, # Adjust creativity
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top_k=50, # Limits the vocabulary considered at each step
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pad_token_id=inference.tokenizer.eos_token_id,
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do_sample=True, # Enable sampling to utilize temperature
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)
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generated_text = inference.tokenizer.decode(
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generated_ids[0], skip_special_tokens=True
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)
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print(generated_text)
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except Exception as e:
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print(f"An error occurred: {e}")
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