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1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model_name = "KathirKs/Gemma-200M-hindi"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
6
7input_text = '''कुछ क्षेत्रों में यह अनुमान लगाया गया है कि लगभग 30 में से एक व्यक्ति कुष्ठ रोग से संक्रमित था'''
8input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
9
10# Pure sampling configuration
11outputs = model.generate(
12 input_ids=input_ids.input_ids,
13 attention_mask=input_ids.attention_mask,
14 max_new_tokens=100,
15 do_sample=True, # Enables sampling
16 temperature=1.5, # Standard temperature for balanced randomness
17 top_k=1000, # No top-k filtering
18 top_p=1.0 # No nucleus sampling
19)
20
21print(tokenizer.decode(outputs[0], skip_special_tokens=True))