Merge pull request #97 from homebrewltd/dev/bach

paper release announcement

Author: hahuyhoang411

README.md

@@ -3,6 +3,7 @@
 # :strawberry: Ichigo: Local real-time voice AI (Formerly llama3-s).
 <a href='https://homebrew.ltd/blog/llama3-just-got-ears'><img src='https://img.shields.io/badge/Project-Blog-Green'></a>
 <a href='https://demo.homebrew.ltd/'><img src='https://img.shields.io/badge/Project-Demo-violet'></a>
+<a href='https://arxiv.org/pdf/2410.15316'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
 <a href='https://huggingface.co/homebrewltd'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue'></a>
 <a href='https://huggingface.co/homebrewltd'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Data-green'></a>
 
@@ -32,6 +33,7 @@ We ~~build~~ train in public:
 - [Ichigo v0.1 Checkpoint Writeup](https://homebrew.ltd/blog/can-llama-3-listen)
 
 ## Progress
+- 22 Oct: 📑 Research Paper Release: We are pleased to announce the publication of our research paper detailing the development and technical innovations behind Ichigo series. The full technical details, methodology, and experimental results are now available in our [paper](https://arxiv.org/pdf/2410.15316). 
 - 4 Oct: [Ichigo v0.3](https://huggingface.co/collections/homebrewltd/ichigo-66ffc7484ef31ec5596ef6d0) models are now available. Utilizing cleaner and improved data, our model has achieved an enhanced MMLU score of 63.79 and demonstrates stronger speech instruction-following capabilities, even in multi-turn interactions. Additionally, by incorporating noise-synthetic data, we have successfully trained the model to refuse processing non-speech audio inputs from users, further improving its functionality and user experience.
 - 23 Aug: We’re excited to share [Ichigo-llama3.1-s-instruct-v0.2](https://huggingface.co/homebrewltd/llama3.1-s-instruct-v0.2), our latest multimodal checkpoint with improved speech understanding by enhancing the model's audio instruction-following capabilities through training on interleaving synthetic data.  
 - 17 Aug: We pre-trained our LLaMA 3.1 model on continuous speech data, tokenized using WhisperSpeechVQ. The final loss converged to approximately 1.9, resulting in our checkpoint: [Ichigo-llama3.1-s-base-v0.2](https://huggingface.co/homebrewltd/llama3.1-s-base-v0.2)