Translation
Transformers
Safetensors
ONNX
m2m_100
text2text-generation
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-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rudrakshrakeshzodage/nllb-200-distilled-600M-fp16 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-fp16")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rudrakshrakeshzodage/nllb-200-distilled-600M-fp16") model = AutoModelForSeq2SeqLM.from_pretrained("rudrakshrakeshzodage/nllb-200-distilled-600M-fp16", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- c4327ec048cd7d36798ae49293f307cbafe3eba8abe426dfe441b2782f70c1ec
- Size of remote file:
- 75.4 kB
- SHA256:
- c599493a213196fff4056f1965f6b7e3e1ea9d90077cf6b8ccf4f6cf34c54aad
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