Translation
Transformers
Safetensors
ONNX
nllb-200
nllb
quantization
int8
4-bit precision
nf4
bitsandbytes
transformers-js
multilingual
neural-machine-translation
meta
seq2seq
m2m_100
text2text-generation
Instructions to use rudrakshrakeshzodage/nllb-200-distilled-600M-nf4-4bit-gpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rudrakshrakeshzodage/nllb-200-distilled-600M-nf4-4bit-gpu 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="rudrakshrakeshzodage/nllb-200-distilled-600M-nf4-4bit-gpu")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rudrakshrakeshzodage/nllb-200-distilled-600M-nf4-4bit-gpu", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 7904b97b0b4351b7ffc720458ffb6d838731c92160acbfd25e3157a43b78cb16
- Size of remote file:
- 1.08 MB
- SHA256:
- 146d5a6a7173334455a91af0a59bb9074eebf630ba02b96f0887c1f51d7dded8
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