refine README

Author: ZiyiXia

README.md

@@ -1,5 +1,8 @@
 <h1 align="center">FlagEmbedding</h1>
 <p align="center">
+    <a href="https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d">
+        <img alt="Build" src="https://img.shields.io/badge/BGE_series-🤗-yellow">
+    </a>
     <a href="https://github.com/FlagOpen/FlagEmbedding">
             <img alt="Build" src="https://img.shields.io/badge/Contribution-Welcome-blue">
     </a>
@@ -116,24 +119,26 @@ print(similarity)
 
 ## Projects
 
-### BGE-M3([Paper](https://arxiv.org/pdf/2402.03216.pdf), [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3))
-In this project, we introduce BGE-M3, the first embedding model which supports multiple retrieval modes、multilingual and multi-granularity retrieval.
-- Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. 
-- Multi-Linguality: It can support more than 100 working languages. 
-- Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. 
+### BGE-M3 ([Paper](https://arxiv.org/pdf/2402.03216.pdf), [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3))
+
+
+In this project, we introduce BGE-M3, the first embedding model which supports:
+- **Multi-Functionality**: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
+- **Multi-Linguality**: It can support more than 100 working languages.
+- **Multi-Granularity**: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.
 
-We propose a novel self-knowledge distillation approach to improve the performance of single retrieval mode. 
-We optimize the batching strategy, enabling a large batch size, which can used simply when fine-tuning with long text or large language model. 
-We also construct a dataset for document retrieval and propose a simple strategy to improve the ability to model long text.
 **The training code and fine-tuning data will be open-sourced in the near future.**
 
 ### [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual)
 In this project, we introduce Visualized-BGE, which integrating image token embedding into the BGE Text Embedding framework. Visualized-BGE can be used for various hybrid modal retrieval tasks, such as Multi-Modal Knowledge Retrieval, Composed Image Retrieval, and Knowledge Retrieval with Multi-Modal Queries.
 
 Our model delivers outstanding zero-shot performance across multiple hybrid modal retrieval tasks. It can also serve as a base model for downstream fine-tuning for hybrid modal retrieval tasks.
 
+
+
 ### [LongLLM QLoRA](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/longllm_qlora)
-We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is super efficient, which takes 8 hours on one 8xA800 (80G) GPU machine. The resulted model exhibits superior performances across a broad range of evaluation tasks, such as NIHS, topic retrieval, and long-context language understanding; meanwhile, it also well preserves the original capability over short contexts. The dramatic context extension is mainly attributed to merely 3.5K synthetic data generated by GPT-4, which indicates the LLMs' inherent (yet largely underestimated) potential to extend its original context length. In fact, the context length could be extended far beyond 80K with more computing resources.
+We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is super efficient, which takes 8 hours on one 8xA800 (80G) GPU machine (the context length can go far beyond 80k with more computing resources). The resulted model exhibits superior performances across a broad range of evaluation tasks, such as NIHS, topic retrieval, and long-context language understanding; meanwhile, it also well preserves the original capability over short contexts.
+
 
 ### [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
 
@@ -144,13 +149,10 @@ More details please refer to our [paper](https://arxiv.org/abs/2401.03462) and [
 
 
 ### [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
-
-Model merging has been used to improve the performance of single model. 
-We find this method is also useful for large language models and dense embedding model, 
-and design the LM-Cocktail strategy which automatically merges fine-tuned models and base model using a simple function to compute merging weights.
-LM-Cocktail can be used to improve the performance on target domain without decrease 
-the general capabilities beyond target domain.
-It also can be used to generate a model for new tasks without fine-tuning.
+ 
+LM-Cocktail automatically merges fine-tuned models and base model using a simple function to compute merging weights.
+LM-Cocktail can be used to improve the performance on target domain without decrease the general capabilities beyond target domain, 
+as well as generate a model for new tasks without fine-tuning.
 You can use it to merge the LLMs (e.g., Llama) or embedding models.
 More details please refer to our report: [LM-Cocktail](https://arxiv.org/abs/2311.13534) and [code](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail).
 
@@ -159,7 +161,7 @@ More details please refer to our report: [LM-Cocktail](https://arxiv.org/abs/231
 ### [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) 
 
 LLM Embedder is fine-tuned based on the feedback from LLMs. 
-It can support the retrieval augmentation needs of large language models, including knowledge retrieval, memory retrieval, example retrieval, and tool retrieval. 
+It supports the retrieval augmentation needs of large language models, including knowledge retrieval, memory retrieval, example retrieval, and tool retrieval. 
 It is fine-tuned over 6 tasks: Question Answering, Conversational Search, Long Conversation, 
 Long-Range Language Modeling, In-Context Learning, and Tool Learning.
 For more details please refer to [report](https://arxiv.org/abs/2310.07554) and [./FlagEmbedding/llm_embedder/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder)
@@ -175,7 +177,7 @@ The data format is the same as embedding model, so you can fine-tune it easily f
 For more details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
 
 
-
+### [LLM Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker) 
 We provide a new version of the cross-encoder that supports more languages and longer lengths. The data format is similar to our embedding models, but now includes prompt data for fine-tuning and inference. You can perform inference using specific layers or using the entire layers. You can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker#fine-tune).
 For more details please refer to [./FlagEmbedding/llm_reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker).
 
@@ -229,6 +231,7 @@ Refer to our [report: c-pack](https://arxiv.org/pdf/2309.07597.pdf) and [code](h
 
 
 ### Contributors:
+Thank all our contributors for their efforts and warmly welcome new members to join in!
 
 <a href="https://github.com/FlagOpen/FlagEmbedding/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=FlagOpen/FlagEmbedding" />