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            ---
         
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            library_name: transformers
         
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            -
            tags: 
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            ---
         
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            # Model Card for Model ID
         
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            ### Direct Use
         
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            <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
         
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            [More Information Needed]
         
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            <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
         
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            [More Information Needed]
         
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            ### Training Procedure
         
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            ---
         
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            library_name: transformers
         
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            tags:
         
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            - Indian-Nuance
         
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            license: apache-2.0
         
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            datasets:
         
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            - ombhojane/smile-india
         
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            language:
         
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            - en
         
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            - hi
         
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            base_model:
         
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            - Qwen/Qwen2.5-1.5B-Instruct
         
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            pipeline_tag: text-generation
         
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            ---
         
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            # Model Card for Model ID
         
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            ### Direct Use
         
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            <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
         
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            ```
         
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            from transformers import pipeline
         
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            import torch
         
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            messages = [
         
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                {"role": "user", "content": "give indian tadka ingrediants"}
         
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            ]
         
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            # Use the GPU if available
         
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            device = 0 if torch.cuda.is_available() else -1
         
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            pipe = pipeline("text-generation", model="ombhojane/smile-small", device=device)
         
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            # Generate longer output text
         
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            generated_text = pipe(messages, max_new_tokens=200, num_return_sequences=1)
         
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            print(generated_text)
         
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            ```
         
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            ```
         
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            generated_text[0]['generated_text'][1]['content']
         
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            ```
         
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            [More Information Needed]
         
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            <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
         
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            [More Information Needed]
         
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            ### Training Procedure
         
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