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ManiKumarAdapala
/
Gemma3-En2Indic-NMT-270M

Translation
Transformers
Safetensors
gemma3_text
text-generation
machine-translation
english-to-indic
indic
multilingual
gemma-3
gemma3
bpcc
ai4bharat
flash-attention
bfloat16
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use ManiKumarAdapala/Gemma3-En2Indic-NMT-270M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ManiKumarAdapala/Gemma3-En2Indic-NMT-270M with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "translation" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("translation", model="ManiKumarAdapala/Gemma3-En2Indic-NMT-270M")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("ManiKumarAdapala/Gemma3-En2Indic-NMT-270M")
    model = AutoModelForCausalLM.from_pretrained("ManiKumarAdapala/Gemma3-En2Indic-NMT-270M", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Gemma3-En2Indic-NMT-270M
570 MB
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  • 1 contributor
History: 3 commits
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ManiKumarAdapala
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  • .gitattributes
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  • README.md
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  • chat_template.jinja
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  • config.json
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  • generation_config.json
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  • model.safetensors
    536 MB
    xet
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  • tokenizer.json
    33.4 MB
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  • tokenizer_config.json
    731 Bytes
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  • training_args.bin
    5.71 kB
    xet
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