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research/README.md

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 # Research
 
-This will point to the technical report for our models.
+### BGE-M3 ([Paper](https://arxiv.org/pdf/2402.03216.pdf), [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3))
 
-| Research                                             | Introduction                                                 |
-| ---------------------------------------------------- | ------------------------------------------------------------ |
-| [C-PACK](https://arxiv.org/abs/2309.07597)           | Introduces [C-MTEB](https://huggingface.co/C-MTEB) and the [bge series of models](https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d). |
-| [BGE M3-Embedding](https://arxiv.org/abs/2402.03216) | Introduces the technical details of [bge-m3](https://huggingface.co/BAAI/bge-m3). |
-| [BGE-EN-ICL](https://arxiv.org/abs/2409.15700)       | Introduces the technical details of [bge-en-icl](https://huggingface.co/BAAI/bge-en-icl), [bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) and [bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight). |
 
+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.
+
+**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 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)
+
+The utilization of long contexts poses a big challenge for large language models due to their limited context window length.
+Activation Beacon condenses LLM's raw activations into more compact forms such that it can perceive a much longer context with a limited context window. 
+It is an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM.
+More details please refer to our [paper](https://arxiv.org/abs/2401.03462) and [code](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon).
+
+
+### [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
+
+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).
+
+### [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) 
+
+LLM Embedder is fine-tuned based on the feedback from LLMs. 
+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)
+
+
+### [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+
+Cross-encoder will perform full-attention over the input pair,
+which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
+Therefore, it can be used to re-rank the top-k documents returned by embedding model.
+We train the cross-encoder on a multilingual pair data, 
+The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). 
+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).
+
+### [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) 
+
+BGE embedding is a general Embedding Model. We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pair data using contrastive learning. 
+**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
+We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
+Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
+Refer to our [report: c-pack](https://arxiv.org/pdf/2309.07597.pdf) and [code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more details.
+
+**BGE uses the last hidden state of `[cls]` as the sentence embedding: `sentence_embeddings = model_output[0][:, 0]`. If you use mean pooling, there will be a significant decrease in performance.**
+
+
+### [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
+
+A benchmark for chinese text embedding. This benchmark has been merged into MTEB. 
+Refer to our [report: c-pack](https://arxiv.org/pdf/2309.07597.pdf) and [code](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) for more details.