Update README.md

README.md

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-Kimi-Audio-7B-Instruct <a href="https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct">🤗</a>&nbsp; | 📑 <a href="https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf">Paper</a>
+<a href="https://huggingface.co/moonshotai/Kimi-Audio-7B">🤗 Kimi-Audio-7B</a>&nbsp; | <a href="https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct">🤗 Kimi-Audio-7B-Instruct </a>&nbsp; | <a href="https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf">📑 Paper</a>
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 ## Introduction
@@ -30,7 +30,7 @@ We present Kimi-Audio, an open-source audio foundation model excelling in **audi
 
 Kimi-Audio is designed as a universal audio foundation model capable of handling a wide variety of audio processing tasks within a single unified framework. Key features include:
 
-*   **Universal Capabilities:** Handles diverse tasks like speech recognition (ASR), audio question answering (AQA), audio captioning (AAC), speech emotion recognition (SER), sound event/scene classification (SEC/ASC), text-to-speech (TTS), voice conversion (VC), and end-to-end speech conversation.
+*   **Universal Capabilities:** Handles diverse tasks like speech recognition (ASR), audio question answering (AQA), audio captioning (AAC), speech emotion recognition (SER), sound event/scene classification (SEC/ASC) and end-to-end speech conversation.
 *   **State-of-the-Art Performance:** Achieves SOTA results on numerous audio benchmarks (see our [Technical Report](https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/main/assets/kimia_report.pdf)). <!-- TODO: Replace with actual raw PDF URL -->
 *   **Large-Scale Pre-training:** Pre-trained on over 13 million hours of diverse audio data (speech, music, sounds) and text data.
 *   **Novel Architecture:** Employs a hybrid audio input (continuous acoustic + discrete semantic tokens) and an LLM core with parallel heads for text and audio token generation.