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README.md
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## Model description
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!pip install transformers torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "Arambh/angika-llm-1b"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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def generate_text(prompt, max_length=100, num_return_sequences=1):
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**inputs,
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max_length=max_length,
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num_return_sequences=num_return_sequences,
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no_repeat_ngram_size=2, # Prevents repetition
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early_stopping=True
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if __name__ == "__main__":
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print(f"Generated Text {i+1}:\n{text}\n")
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## Intended uses & limitations
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## Model description
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!pip install transformers torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "Arambh/angika-llm-1b"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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def generate_text(prompt, max_length=100, num_return_sequences=1):
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# Tokenize input prompt
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inputs = tokenizer(prompt, return_tensors="pt")
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# Generate text
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outputs = model.generate(
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**inputs,
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max_length=max_length,
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num_return_sequences=num_return_sequences,
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no_repeat_ngram_size=2, # Prevents repetition
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early_stopping=True
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)
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# Decode and return the generated text
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return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
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if __name__ == "__main__":
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prompt = "ये सब पहाड़ी पर पुरानो अभिलेख मिलै छै "
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generated_text = generate_text(prompt, max_length=100)
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for i, text in enumerate(generated_text):
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print(f"Generated Text {i+1}:\n{text}\n")
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## Intended uses & limitations
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