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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Uses
1
2
3%%time
4
5from transformers import AutoTokenizer, AutoModelForCausalLM
6
7tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
8
9from peft import PeftModel
10model_name='google/gemma-2b'
11inference_model = AutoModelForCausalLM.from_pretrained(
12 model_name,
13 low_cpu_mem_usage=True,
14 # use_flash_attention_2=True,
15
16)
17inference_model.resize_token_embeddings(len(tokenizer))
18
19inference_model = PeftModel.from_pretrained(inference_model, "hemanthkandimalla/HemanthLLM_tiny1.1b")
20
21inference_model.eval()
22text="attacks names"
23tokenizer_input=tokenizer(text=text, return_tensors="pt")
24output_tokens = inference_model.generate(
25 **tokenizer_input,
26 max_new_tokens=256,
27 do_sample=True,
28 temperature=0.2,
29 top_p=0.95,
30 top_k=50,
31 eos_token_id=tokenizer.eos_token_id,
32 pad_token_id=tokenizer.pad_token_id,
33)
34text=tokenizer.decode(output_tokens[0], skip_special_tokens=False)
35
36print(text)
37
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