Translation
Transformers
Safetensors
ONNX
nllb-200
nllb
quantization
int8
4-bit precision
nf4
bitsandbytes
transformers-js
multilingual
neural-machine-translation
meta
seq2seq
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:
- 22ef154cbec166b333621a5fcfed380f1582540e29a8e540b7111ff055a0ecc1
- Size of remote file:
- 115 kB
- SHA256:
- c9a9ce7b914f7bd74b9d62f00816cdec29415230b9b2a83e7cbd41526d5a014b
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