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@@ -7,20 +7,20 @@ tags:
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  - onnx
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  - translation
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  - multilingual
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- - affectively
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  base_model: facebook/nllb-200-distilled-1.3B
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  pipeline_tag: translation
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  ---
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  # Nllb 200 Distilled 1.3B
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- AFFECTIVELY conversion of [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) to ONNX format for edge deployment.
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  ## Model Details
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  - **Source Model**: [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B)
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  - **Format**: ONNX
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- - **Converted by**: [AFFECTIVELY](https://affectively.ai)
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  ## Usage
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@@ -36,12 +36,12 @@ session = ort.InferenceSession("nllb-200-distilled-1.3B-onnx.onnx")
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  ```python
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  from optimum.onnxruntime import ORTModelForSequenceClassification
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- model = ORTModelForSequenceClassification.from_pretrained("affectively-ai/nllb-200-distilled-1.3B-onnx")
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  ```
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- ## About AFFECTIVELY
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- [AFFECTIVELY](https://affectively.ai) is an emotion intelligence platform that runs AI models at the edge -- in-browser, on-device, zero cloud cost. These converted models power the platform's real-time emotion analysis, speech recognition, and natural language capabilities.
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  All conversions are optimized for edge deployment within browser and mobile memory constraints.
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  - onnx
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  - translation
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  - multilingual
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+ - forkjoin-ai
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  base_model: facebook/nllb-200-distilled-1.3B
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  pipeline_tag: translation
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  ---
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  # Nllb 200 Distilled 1.3B
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+ Forkjoin.ai conversion of [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) to ONNX format for edge deployment.
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  ## Model Details
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  - **Source Model**: [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B)
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  - **Format**: ONNX
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+ - **Converted by**: [Forkjoin.ai](https://forkjoin.ai)
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  ## Usage
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  ```python
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  from optimum.onnxruntime import ORTModelForSequenceClassification
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+ model = ORTModelForSequenceClassification.from_pretrained("forkjoin-ai/nllb-200-distilled-1.3B-onnx")
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  ```
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+ ## About Forkjoin.ai
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+ [Forkjoin.ai](https://forkjoin.ai) runs AI models at the edge -- in-browser, on-device, zero cloud cost. These converted models power real-time inference, speech recognition, and natural language capabilities.
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  All conversions are optimized for edge deployment within browser and mobile memory constraints.
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