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1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "naazimsnh02/gemma-3-tamilnadu_sample"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = AutoModelForCausalLM.from_pretrained(model_name)
6
7# Example prompt
8messages = [
9 {"role": "user", "content": "Tell me about Pongal festival in Tamil Nadu"}
10]
11
12inputs = tokenizer.apply_chat_template(
13 messages,
14 tokenize=True,
15 add_generation_prompt=True,
16 return_tensors="pt"
17).to("cuda")
18
19outputs = model.generate(
20 inputs,
21 max_new_tokens=256,
22 temperature=0.7,
23 top_p=0.9,
24)
25
26response = tokenizer.decode(outputs[0], skip_special_tokens=True)
27print(response)1from unsloth import FastLanguageModel
2
3model, tokenizer = FastLanguageModel.from_pretrained(
4 model_name="naazimsnh02/gemma-3-tamilnadu_sample",
5 max_seq_length=2048,
6 dtype=None,
7 load_in_4bit=True,
8)
9
10FastLanguageModel.for_inference(model)
11
12# Generate response
13messages = [{"role": "user", "content": "Vanakkam! How are you?"}]
14inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
15outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
16print(tokenizer.decode(outputs[0], skip_special_tokens=True))1@misc{tamil-nadu-gemma-2025,
2 title={Tamil Nadu Cultural AI Model based on Gemma-3-1B-IT},
3 author={Syed Naazim Hussain},
4 year={2025},
5 publisher={HuggingFace},
6 howpublished={\url{https://huggingface.co/naazimsnh02/gemma-3-tamilnadu_sample}}
7}