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+ {
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+ "_name_or_path": "Qwen/Qwen2.5-0.5B",
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+ "Qwen2ForCausalLM"
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+ }
main.py ADDED
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+ import torch
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ model_path = "./Path-to-llm-folder"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_path)
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+ model = AutoModelForCausalLM.from_pretrained(model_path)
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+
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+ model.to(device)
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+
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+ def generate_text(prompt, max_length=2000):
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+ inputs = tokenizer(prompt, return_tensors="pt").to(device)
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+ output = model.generate(
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+ **inputs,
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+ do_sample=True,
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+ temperature=0.7
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+ )
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+ return tokenizer.decode(output[0], skip_special_tokens=True)
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+
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+ prompt = "Write a code in react for calling api to server at https://example.com/test"
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+ generated_text = generate_text(prompt)
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+
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+ print(generated_text)
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