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1from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
2
3tokenizer = AutoTokenizer.from_pretrained("Robin246/inxai_v1.1")
4model = AutoModelForSeq2SeqLM.from_pretrained("Robin246/inxai_v1.1")
5
6# Adjust the parameters if needed
7def generate_response(input_prompt, model, tokenizer):
8 input_text = f"Input prompt: {input_prompt}"
9
10 input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=64, padding="max_length", truncation=True)
11
12 output_ids = model.generate(input_ids,
13 max_length=256,
14 num_return_sequences=1,
15 num_beams=2,
16 early_stopping=True,
17 #do_sample=True,
18 #temperature=0.8,
19 #top_k=50
20 ) #You can vary the top_k or add any other parameters
21
22 generated_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
23 return generated_output
24
25
26while True:
27 user_input = input("Enter prompt: ")
28 user_input = ["{}".format(user_input)]
29 if user_input=='Quit':
30 break
31 else:
32 reply = generate_response(user_input, model, tokenizer)
33 print("Generated Reply({}):".format(model), reply)
34
35#INXAI from huggingface 'Robin246/inxai_v1.1'