nllb-moe-54b
Maintainer: facebook
97
⚙️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The nllb-moe-54b model is a variant of the NLLB-200 multilingual machine translation model developed by Facebook. It utilizes a Mixture-of-Experts (MoE) architecture, which means the model has multiple specialized sub-networks that can be selectively activated based on the input. This allows the model to efficiently handle a wide range of language pairs and tasks.
The NLLB-200 model, as described in the No Language Left Behind: Scaling Human-Centered Machine Translation paper, was trained on a large corpus of parallel data across 200 languages, making it capable of translating between nearly any pair of these languages. The nllb-moe-54b variant has a similar broad language coverage, but with a more efficient architecture.
Compared to other NLLB-200 checkpoints, the nllb-moe-54b model has around 54 billion parameters and utilizes Expert Output Masking during training, which selectively drops the contribution of certain tokens. This results in a more compact model that retains strong performance, as seen in the metrics provided for the nllb-200-3.3B checkpoint.
Model inputs and outputs
Inputs
- Text in any of the 200 languages supported by the NLLB-200 model
Outputs
- Translated text in any of the 200 supported languages
- The target language can be specified by providing the appropriate language ID (BCP-47 code) as the
forced_bos_token_idduring generation
Capabilities
The nllb-moe-54b model is capable of high-quality multilingual translation across a diverse set of languages, including many low-resource languages. It can be used to translate single sentences or short passages between any pair of the 200 supported languages.
What can I use it for?
The nllb-moe-54b model is well-suited for research and development in the field of machine translation, particularly for projects involving low-resource languages. Developers and researchers can use it to build multilingual applications, explore cross-lingual transfer learning, or investigate the challenges of scaling human-centered translation systems.
While the model is not intended for production deployment, it can be a valuable tool for prototyping and experimenting with multilingual translation capabilities. Users should keep in mind the ethical considerations outlined in the NLLB-200 model card, such as the potential for misuse and the limitations of the model's training data.
Things to try
One interesting aspect of the nllb-moe-54b model is its efficient MoE architecture, which allows for selective activation of experts during inference. Developers could experiment with different prompting strategies or task-specific fine-tuning to explore how the model's capabilities vary across different language pairs and translation scenarios.
Additionally, the model's broad language coverage makes it well-suited for exploring cross-lingual transfer learning, where knowledge gained from translating between high-resource languages can be applied to improve performance on low-resource language pairs.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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