Instructions to use dheeyantra/dhee-vagmi-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dheeyantra/dhee-vagmi-core 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="dheeyantra/dhee-vagmi-core")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dheeyantra/dhee-vagmi-core") model = AutoModelForSeq2SeqLM.from_pretrained("dheeyantra/dhee-vagmi-core", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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A multilingual machine-translation model covering English and 12 Indian languages, trained from scratch in the M2M100 architecture. It translates directly between any supported pair (not only to and from English).
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> Early research checkpoint. This is an initial from-scratch model; quality varies by language pair. Treat it as a baseline to build on, not a production-grade system.
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**Version:** `v2` 路 **Languages:** English, Hindi, Bengali, Marathi, Tamil, Telugu, Kannada, Malayalam, Odia, Gujarati, Punjabi, Assamese, Urdu.
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## What this model is for
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## Limitations
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- Early checkpoint -- fluency and adequacy trail mature MT systems, especially on long or domain-specific sentences.
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- Best on short-to-medium sentences; very long inputs are truncated at encode time.
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- No guarantee of terminology consistency or safe handling of code-mixed input.
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A multilingual machine-translation model covering English and 12 Indian languages, trained from scratch in the M2M100 architecture. It translates directly between any supported pair (not only to and from English).
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**Version:** `v2` 路 **Languages:** English, Hindi, Bengali, Marathi, Tamil, Telugu, Kannada, Malayalam, Odia, Gujarati, Punjabi, Assamese, Urdu.
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## What this model is for
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## Limitations
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- Best on short-to-medium sentences; very long inputs are truncated at encode time.
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- No guarantee of terminology consistency or safe handling of code-mixed input.
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