Update README.md

README.md

@@ -1,17 +1,15 @@
 ---
-license: mit
+license: cc-by-nc-sa-4.0
 ---
 ## Overview
 
-**DeepSeek** developed and released the [DeepSeek R1 Distill Qwen 1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) model, a distilled version of the Qwen 1.5B language model. It is fine-tuned for high-performance text generation and optimized for dialogue and information-seeking tasks. This model achieves a balance of efficiency and accuracy while maintaining a smaller footprint compared to the original Qwen 1.5B.
-
-The model is designed for applications in customer support, conversational AI, and research, prioritizing both helpfulness and safety.
+Fish Speech V1.5 is an advanced text-to-speech (TTS) model designed for high-quality speech synthesis. It has been trained on over 1 million hours of multilingual audio data, providing robust support for multiple languages.
 
 ## Variants
 
 | No | Variant | Cortex CLI command |
 | --- | --- | --- |
-| 1 | [gguf](https://huggingface.co/cortexso/deepseek-r1-distill-qwen-1.5b/tree/main) | `cortex run deepseek-r1-distill-qwen-1.5b` |
+| 1 | [gguf](https://huggingface.co/cortexso/fish-speech/tree/main) | `cortex run fish-speech` |
 
 
 ## Use it with Jan (UI)
@@ -19,18 +17,18 @@ The model is designed for applications in customer support, conversational AI, a
 1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
 2. Use in Jan model Hub:
     ```text
-    cortexso/deepseek-r1-distill-qwen-1.5b
+    cortexso/fish-speech
     ```
 ## Use it with Cortex (CLI)
 
 1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
 2. Run the model with command:
     ```bash
-    cortex run deepseek-r1-distill-qwen-1.5b
+    cortex run fish-speech
     ```
 ## Credits
 
-- **Author:** DeepSeek
+- **Author:** Fishaudio
 - **Converter:** [Homebrew](https://www.homebrew.ltd/)
-- **Original License:** [License](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B#7-license)
-- **Papers:** [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://arxiv.org/html/2501.12948v1)
+- **Original License:** [License]()
+- **Papers:** [Paper]]()