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Added NVIDIA developer links

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@@ -209,6 +209,16 @@ The model is intended for users requiring speech-to-text transcription capabilit
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  Huggingface 07/17/2025 via https://huggingface.co/nvidia/canary-qwen-2.5b
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  ## Model Architecture:
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  Canary-Qwen is a Speech-Augmented Language Model (SALM) [9] model with FastConformer [2] Encoder and Transformer Decoder [3]. It is built using two base models: `nvidia/canary-1b-flash` [1,5] and `Qwen/Qwen3-1.7B` [4], a linear projection, and low-rank adaptation (LoRA) applied to the LLM. The audio encoder computes audio representation that is mapped to the LLM embedding space via a linear projection, and concatenated with the embeddings of text tokens. The model is prompted with "Transcribe the following: <audio>", using Qwen's chat template.
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  Huggingface 07/17/2025 via https://huggingface.co/nvidia/canary-qwen-2.5b
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+ ## Discover more from NVIDIA:
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+ For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at [developer.nvidia.com](https://developer.nvidia.com/).
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+ Join the community to access tools, support, and resources to accelerate your development with NVIDIA’s NeMo, Riva, NIM, and foundation models.<br>
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+
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+ ### Explore more from NVIDIA: <br>
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+ What is [Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/)?<br>
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+ NVIDIA Developer [Nemotron](https://developer.nvidia.com/nemotron)<br>
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+ [NVIDIA Riva Speech](https://developer.nvidia.com/riva?sortBy=developer_learning_library%2Fsort%2Ffeatured_in.riva%3Adesc%2Ctitle%3Aasc#demos)<br>
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+ [NeMo Documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html)<br>
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  ## Model Architecture:
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  Canary-Qwen is a Speech-Augmented Language Model (SALM) [9] model with FastConformer [2] Encoder and Transformer Decoder [3]. It is built using two base models: `nvidia/canary-1b-flash` [1,5] and `Qwen/Qwen3-1.7B` [4], a linear projection, and low-rank adaptation (LoRA) applied to the LLM. The audio encoder computes audio representation that is mapped to the LLM embedding space via a linear projection, and concatenated with the embeddings of text tokens. The model is prompted with "Transcribe the following: <audio>", using Qwen's chat template.
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