reorg

README.md

@@ -5,26 +5,28 @@
 
 🔥 Large Language Models(LLM) have taken the ~~NLP community~~ ~~AI community~~ **the Whole World** by storm. Here is a curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM, courses and tutorials about LLM and all publicly available LLM checkpoints and APIs.
 
-### ToDos
+<!-- ### ToDos
 
-- Add LLM data (Pretraining data/Instruction Tuning data/Chat data/RLHF data) :sparkles:**Contributions Wanted**
+- Add LLM data (Pretraining data/Instruction Tuning data/Chat data/RLHF data) :sparkles:**Contributions Wanted** -->
 
-## Table of Content
+## Trending LLM Projects
+- [Mixtral 8x7B](https://mistral.ai/news/mixtral-of-experts/) - a high-quality sparse mixture of experts model (SMoE) with open weights.
+- [promptbase](https://github.com/microsoft/promptbase) - All things prompt engineering.
+- [ollama](https://github.com/jmorganca/ollama) - Get up and running with Llama 2 and other large language models locally.
+- [anything-llm](https://github.com/Mintplex-Labs/anything-llm) - A private ChatGPT to chat with anything!
+- [phi-2](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/) - a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities, showcasing state-of-the-art performance among base language models with less than 13 billion parameters.
 
+## Table of Content
 
 - [Awesome-LLM ](#awesome-llm-)
-  - [Updates](#updates)
-  - [Table of Content](#table-of-content)
   - [Milestone Papers](#milestone-papers)
   - [Other Papers](#other-papers)
-  - [LLM Leaderboard](#llm-leaderboard)
   - [Open LLM](#open-llm)
   - [LLM Training Frameworks](#llm-training-frameworks)
-  - [Tools for deploying LLM](#tools-for-deploying-llm)
-  - [Tutorials about LLM](#tutorials-about-llm)
-  - [Courses about LLM](#courses-about-llm)
-  - [Opinions about LLM](#opinions-about-llm)
-  - [Other Awesome Lists](#other-awesome-lists)
+  - [Tools for deploying LLM](#deploying-tools)
+  - [Tutorials about LLM](#tutorials)
+  - [Courses about LLM](#courses)
+  - [Opinions about LLM](#opinions)
   - [Other Useful Resources](#other-useful-resources)
   - [Contributing](#contributing)
 
@@ -77,17 +79,45 @@
 | 2023-04 | Pythia | EleutherAI et al. | [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://arxiv.org/abs/2304.01373)|ICML<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fbe55e8ec4213868db08f2c3168ae666001bea4b8%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2023-05 | Dromedary | CMU et al. | [Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision](https://arxiv.org/abs/2305.03047)|![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe01515c6138bc525f7aec30fc85f2adf028d4156%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2023-05 | PaLM 2 | Google | [PaLM 2 Technical Report](https://ai.google/static/documents/palm2techreport.pdf)|![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Feccee350691708972370b7a12c2a78ad3bddd159%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
-| 2023-05 | RWKV | Bo Peng | [RWKV: Reinventing RNNs for the Transformer Era](https://arxiv.org/abs/2305.13048) |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F026b3396a63ed5772329708b7580d633bb86bec9%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
-| 2023-05 | DPO | Stanford | [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/pdf/2305.18290.pdf) |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F0d1c76d45afa012ded7ab741194baf142117c495%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
+| 2023-05 | RWKV | Bo Peng | [RWKV: Reinventing RNNs for the Transformer Era](https://arxiv.org/abs/2305.13048) |EMNLP<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F026b3396a63ed5772329708b7580d633bb86bec9%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
+| 2023-05 | DPO | Stanford | [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/pdf/2305.18290.pdf) |Neurips<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F0d1c76d45afa012ded7ab741194baf142117c495%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2023-07 | LLaMA 2 | Meta | [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://arxiv.org/pdf/2307.09288.pdf) |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F104b0bb1da562d53cbda87aec79ef6a2827d191a%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
+| 2023-12 | Mamba | CMU&Princeton | [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/ftp/arxiv/papers/2312/2312.00752.pdf) |ICLR<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F432bef8e34014d726c674bc458008ac895297b51%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 
 
 
 
 ## Other Papers
-If you're interested in the field of LLM, you may find the above list of milestone papers helpful to explore its history and state-of-the-art. However, each direction of LLM offers a unique set of insights and contributions, which are essential to understanding the field as a whole. For a detailed list of papers in various subfields, please refer to the following link (it is possible that there are overlaps between different subfields):
+If you're interested in the field of LLM, you may find the above list of milestone papers helpful to explore its history and state-of-the-art. However, each direction of LLM offers a unique set of insights and contributions, which are essential to understanding the field as a whole. For a detailed list of papers in various subfields, please refer to the following link:
 
-(:exclamation: **We would greatly appreciate and welcome your contribution to the following list. :exclamation:**)
+- [LLMsPracticalGuide](https://github.com/Mooler0410/LLMsPracticalGuide) - A curated (still actively updated) list of practical guide resources of LLMs
+- [Awesome ChatGPT Prompts](https://github.com/f/awesome-chatgpt-prompts) - A collection of prompt examples to be used with the ChatGPT model.
+- [awesome-chatgpt-prompts-zh](https://github.com/PlexPt/awesome-chatgpt-prompts-zh) - A Chinese collection of prompt examples to be used with the ChatGPT model.
+- [Awesome ChatGPT](https://github.com/humanloop/awesome-chatgpt) - Curated list of resources for ChatGPT and GPT-3 from OpenAI.
+- [Chain-of-Thoughts Papers](https://github.com/Timothyxxx/Chain-of-ThoughtsPapers) -  A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models.
+- [Instruction-Tuning-Papers](https://github.com/SinclairCoder/Instruction-Tuning-Papers) - A trend starts from `Natrural-Instruction` (ACL 2022), `FLAN` (ICLR 2022) and `T0` (ICLR 2022).
+- [LLM Reading List](https://github.com/crazyofapple/Reading_groups/) - A paper & resource list of large language models.
+- [Reasoning using Language Models](https://github.com/atfortes/LM-Reasoning-Papers) - Collection of papers and resources on Reasoning using Language Models.
+- [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub) - Measuring LLMs' Reasoning Performance
+- [Awesome GPT](https://github.com/formulahendry/awesome-gpt) - A curated list of awesome projects and resources related to GPT, ChatGPT, OpenAI, LLM, and more.
+- [Awesome GPT-3](https://github.com/elyase/awesome-gpt3) - a collection of demos and articles about the [OpenAI GPT-3 API](https://openai.com/blog/openai-api/).
+- [Awesome LLM Human Preference Datasets](https://github.com/PolisAI/awesome-llm-human-preference-datasets) - a collection of human preference datasets for LLM instruction tuning, RLHF and evaluation.
+- [RWKV-howto](https://github.com/Hannibal046/RWKV-howto) - possibly useful materials and tutorial for learning RWKV.
+- [ModelEditingPapers](https://github.com/zjunlp/ModelEditingPapers) - A paper & resource list on model editing for large language models.
+- [Awesome LLM Security](https://github.com/corca-ai/awesome-llm-security) - A curation of awesome tools, documents and projects about LLM Security.
+- [Awesome-Align-LLM-Human](https://github.com/GaryYufei/AlignLLMHumanSurvey) - A collection of papers and resources about aligning large language models (LLMs) with human.
+- [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) - An awesome and curated list of best code-LLM for research.
+- [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) - Awesome LLM compression research papers and tools.
+- [Awesome-LLM-Systems](https://github.com/AmberLJC/LLMSys-PaperList) - Awesome LLM systems research papers.
+- [awesome-llm-webapps](https://github.com/snowfort-ai/awesome-llm-webapps) - A collection of open source, actively maintained web apps for LLM applications.
+- [awesome-japanese-llm](https://github.com/llm-jp/awesome-japanese-llm) - 日本語LLMまとめ - Overview of Japanese LLMs.
+- [Awesome-LLM-Healthcare](https://github.com/mingze-yuan/Awesome-LLM-Healthcare) - The paper list of the review on LLMs in medicine.
+- [Awesome-LLM-Inference](https://github.com/DefTruth/Awesome-LLM-Inference) - A curated list of Awesome LLM Inference Paper with codes.
+- [LLMDatahub](https://github.com/Zjh-819/LLMDataHub) - a curated collection of datasets specifically designed for chatbot training, including links, size, language, usage, and a brief description of each dataset
+- [Awesome-Chinese-LLM](https://github.com/HqWu-HITCS/Awesome-Chinese-LLM) - 整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。
+- [llm-course](https://github.com/mlabonne/llm-course) - Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
+
+<!-- (:exclamation: **We would greatly appreciate and welcome your contribution to the following list. :exclamation:**)
 
 - [LLM-Analysis](paper_list/evaluation.md)
 
@@ -131,19 +161,19 @@ If you're interested in the field of LLM, you may find the above list of milesto
 
 - [Retrieval-Augmented Generation](paper_list/Retrieval_Augmented_Generation.md)
 
-  > Retrieval-Augmented Generation (RAG) combines retrieval from a corpus with generative text models to enhance response accuracy using external knowledge.
+  > Retrieval-Augmented Generation (RAG) combines retrieval from a corpus with generative text models to enhance response accuracy using external knowledge. -->
 
-## LLM Leaderboard
+## Open LLM
 <div align=center>
 <img src="resources/creepy_llm.jpeg" width="500">
 </div>
 
 There are three important steps for a ChatGPT-like LLM: 
-1. **Pre-training** 
-2. **Instruction Tuning**
-3. **Alignment**
+-  **Pre-training** 
+-  **Instruction Tuning**
+-  **Alignment**
 
-The following list makes sure that all LLMs are compared **apples to apples**.
+<!-- The following list makes sure that all LLMs are compared **apples to apples**. -->
   > You may also find these leaderboards helpful:
   > - [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) - aims to track, rank and evaluate LLMs and chatbots as they are released.
   > - [Chatbot Arena Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) - a benchmark platform for large language models (LLMs) that features anonymous, randomized battles in a crowdsourced manner.
@@ -152,7 +182,7 @@ The following list makes sure that all LLMs are compared **apples to apples**.
 
 
 
-### Base LLM
+<!-- ### Base LLM
 
 |       Model       | Size |  Architecture  |                                                                                               Access                                                                                               |  Date  | Origin                                                                                                                                       | Model License[^1] |
 | :----------------: | :--: | :-------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------- |
@@ -212,9 +242,11 @@ The above tables coule be better summarized by this wonderful visualization from
 
 ---
 
-## Open LLM
+## Open LLM -->
 
-- [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) - A foundational, 65-billion-parameter large language model. [LLaMA.cpp](https://github.com/ggerganov/llama.cpp) [Lit-LLaMA](https://github.com/Lightning-AI/lit-llama)
+- [Mistral](https://mistral.ai/) - Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases including code and 8k sequence length. Apache 2.0 licence.
+- [Mixtral 8x7B](https://mistral.ai/news/mixtral-of-experts/) - a high-quality sparse mixture of experts model (SMoE) with open weights.
+- [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)  & [LLaMA-2](https://ai.meta.com/llama/) - A foundational large language model. [LLaMA.cpp](https://github.com/ggerganov/llama.cpp) [Lit-LLaMA](https://github.com/Lightning-AI/lit-llama)
   - [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) - A model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. [Alpaca.cpp](https://github.com/antimatter15/alpaca.cpp) [Alpaca-LoRA](https://github.com/tloen/alpaca-lora)
   - [Flan-Alpaca](https://github.com/declare-lab/flan-alpaca) - Instruction Tuning from Humans and Machines.
   - [Baize](https://github.com/project-baize/baize-chatbot) - Baize is an open-source chat model trained with [LoRA](https://github.com/microsoft/LoRA). It uses 100k dialogs generated by letting ChatGPT chat with itself. 
@@ -272,7 +304,9 @@ The above tables coule be better summarized by this wonderful visualization from
 - [XGen](https://github.com/salesforce/xgen) - Salesforce open-source LLMs with 8k sequence length.
 - [baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) - baichuan-7B 是由百川智能开发的一个开源可商用的大规模预训练语言模型.
 - [Aquila](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/Aquila) - 悟道·天鹰语言大模型是首个具备中英双语知识、支持商用许可协议、国内数据合规需求的开源语言大模型。
-- [Mistral](https://mistral.ai/) - Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases including code and 8k sequence length. Apache 2.0 licence.
+- [phi-1](https://arxiv.org/abs/2306.11644) - a new large language model for code, with significantly smaller size than competing models.
+- [phi-1.5](https://arxiv.org/abs/2309.05463) - a 1.3 billion parameter model trained on a dataset of 30 billion tokens, which achieves common sense reasoning benchmark results comparable to models ten times its size that were trained on datasets more than ten times larger.
+- [phi-2](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/) - a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities, showcasing state-of-the-art performance among base language models with less than 13 billion parameters.
 
 ## LLM Training Frameworks
 
@@ -287,7 +321,7 @@ The above tables coule be better summarized by this wonderful visualization from
 - [Alpa](https://alpa.ai/index.html) - Alpa is a system for training and serving large-scale neural networks.
 - [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
 
-## Tools for deploying LLM
+## Deploying Tools
 
 - [FastChat](https://github.com/lm-sys/FastChat) - A distributed multi-model LLM serving system with web UI and OpenAI-compatible RESTful APIs.
 - [SkyPilot](https://github.com/skypilot-org/skypilot) - Run LLMs and batch jobs on any cloud. Get maximum cost savings, highest GPU availability, and managed execution -- all with a simple interface.
@@ -312,6 +346,7 @@ The above tables coule be better summarized by this wonderful visualization from
 - [Text-Embeddings-Inference](https://github.com/huggingface/text-embeddings-inference) - Inference for text-embeddings in Rust, HFOIL Licence.
 - [Infinity](https://github.com/michaelfeil/infinity) - Inference for text-embeddings in Python
 - [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) - Nvidia Framework for LLM Inference
+
 ## Prompting libraries & tools
 
 - [YiVal](https://github.com/YiVal/YiVal) — Evaluate and Evolve: YiVal is an open-source GenAI-Ops tool for tuning and evaluating prompts, configurations, and model parameters using customizable datasets, evaluation methods, and improvement strategies.
@@ -335,7 +370,7 @@ The above tables coule be better summarized by this wonderful visualization from
 - [Flappy](https://github.com/pleisto/flappy) — Production-Ready LLM Agent SDK for Every Developer.
 
 
-## Tutorials about LLM
+## Tutorials
 - [Andrej Karpathy] State of GPT [video](https://build.microsoft.com/en-US/sessions/db3f4859-cd30-4445-a0cd-553c3304f8e2)
 - [Hyung Won Chung] Instruction finetuning and RLHF lecture [Youtube](https://www.youtube.com/watch?v=zjrM-MW-0y0)
 - [Jason Wei] Scaling, emergence, and reasoning in large language models [Slides](https://docs.google.com/presentation/d/1EUV7W7X_w0BDrscDhPg7lMGzJCkeaPkGCJ3bN8dluXc/edit?pli=1&resourcekey=0-7Nz5A7y8JozyVrnDtcEKJA#slide=id.g16197112905_0_0)
@@ -365,7 +400,7 @@ The above tables coule be better summarized by this wonderful visualization from
 - [StatQuest] Transformer Neural Networks, ChatGPT's foundation [Link](https://www.youtube.com/watch?v=zxQyTK8quyY)
 - [StatQuest] Decoder-Only Transformers, ChatGPTs specific Transformer [Link](https://www.youtube.com/watch?v=bQ5BoolX9Ag)
 
-## Courses about LLM
+## Courses
 
 - [DeepLearning.AI] ChatGPT Prompt Engineering for Developers [Homepage](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)
 - [Princeton] Understanding Large Language Models [Homepage](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/)
@@ -381,13 +416,11 @@ The above tables coule be better summarized by this wonderful visualization from
 - [Aston Zhang] Chain of Thought论文 [Bilibili](https://www.bilibili.com/video/BV1t8411e7Ug/?spm_id_from=333.788&vd_source=1e55c5426b48b37e901ff0f78992e33f) [Youtube](https://www.youtube.com/watch?v=H4J59iG3t5o&list=PLFXJ6jwg0qW-7UM8iUTj3qKqdhbQULP5I&index=29)
 - [MIT] Introduction to Data-Centric AI [Homepage](https://dcai.csail.mit.edu)
 
-## Opinions about LLM
+## Opinions
 
 - [A Stage Review of Instruction Tuning](https://yaofu.notion.site/June-2023-A-Stage-Review-of-Instruction-Tuning-f59dbfc36e2d4e12a33443bd6b2012c2) [2023-06-29] [Yao Fu]
-
 - [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) [2023-06-23] [Lilian]
 - [Why you should work on AI AGENTS!](https://www.youtube.com/watch?v=fqVLjtvWgq8) [2023-06-22] [Andrej Karpathy]
-
 - [Google "We Have No Moat, And Neither Does OpenAI"](https://www.semianalysis.com/p/google-we-have-no-moat-and-neither) [2023-05-05]
 - [AI competition statement](https://petergabriel.com/news/ai-competition-statement/) [2023-04-20] [petergabriel]
 - [我的大模型世界观](https://mp.weixin.qq.com/s/_ZvyxRpgIA4L4pqfcQtPTQ) [2023-04-23] [陆奇]
@@ -406,32 +439,7 @@ The above tables coule be better summarized by this wonderful visualization from
 - [What Are Large Language Models Used For? ](https://www.notion.so/Awesome-LLM-40c8aa3f2b444ecc82b79ae8bbd2696b) \[2023-01-26][NVIDIA]
 - [Large Language Models: A New Moore&#39;s Law ](https://huggingface.co/blog/large-language-models) \[2021-10-26\]\[Huggingface\]
 
-## Other Awesome Lists
 
-- [LLMsPracticalGuide](https://github.com/Mooler0410/LLMsPracticalGuide) - A curated (still actively updated) list of practical guide resources of LLMs
-- [Awesome ChatGPT Prompts](https://github.com/f/awesome-chatgpt-prompts) - A collection of prompt examples to be used with the ChatGPT model.
-- [awesome-chatgpt-prompts-zh](https://github.com/PlexPt/awesome-chatgpt-prompts-zh) - A Chinese collection of prompt examples to be used with the ChatGPT model.
-- [Awesome ChatGPT](https://github.com/humanloop/awesome-chatgpt) - Curated list of resources for ChatGPT and GPT-3 from OpenAI.
-- [Chain-of-Thoughts Papers](https://github.com/Timothyxxx/Chain-of-ThoughtsPapers) -  A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models.
-- [Instruction-Tuning-Papers](https://github.com/SinclairCoder/Instruction-Tuning-Papers) - A trend starts from `Natrural-Instruction` (ACL 2022), `FLAN` (ICLR 2022) and `T0` (ICLR 2022).
-- [LLM Reading List](https://github.com/crazyofapple/Reading_groups/) - A paper & resource list of large language models.
-- [Reasoning using Language Models](https://github.com/atfortes/LM-Reasoning-Papers) - Collection of papers and resources on Reasoning using Language Models.
-- [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub) - Measuring LLMs' Reasoning Performance
-- [Awesome GPT](https://github.com/formulahendry/awesome-gpt) - A curated list of awesome projects and resources related to GPT, ChatGPT, OpenAI, LLM, and more.
-- [Awesome GPT-3](https://github.com/elyase/awesome-gpt3) - a collection of demos and articles about the [OpenAI GPT-3 API](https://openai.com/blog/openai-api/).
-- [Awesome LLM Human Preference Datasets](https://github.com/PolisAI/awesome-llm-human-preference-datasets) - a collection of human preference datasets for LLM instruction tuning, RLHF and evaluation.
-- [RWKV-howto](https://github.com/Hannibal046/RWKV-howto) - possibly useful materials and tutorial for learning RWKV.
-- [ModelEditingPapers](https://github.com/zjunlp/ModelEditingPapers) - A paper & resource list on model editing for large language models.
-- [Awesome LLM Security](https://github.com/corca-ai/awesome-llm-security) - A curation of awesome tools, documents and projects about LLM Security.
-- [Awesome-Align-LLM-Human](https://github.com/GaryYufei/AlignLLMHumanSurvey) - A collection of papers and resources about aligning large language models (LLMs) with human.
-- [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) - An awesome and curated list of best code-LLM for research.
-- [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) - Awesome LLM compression research papers and tools.
-- [Awesome-LLM-Systems](https://github.com/AmberLJC/LLMSys-PaperList) - Awesome LLM systems research papers.
-- [awesome-llm-webapps](https://github.com/snowfort-ai/awesome-llm-webapps) - A collection of open source, actively maintained web apps for LLM applications.
-- [awesome-japanese-llm](https://github.com/llm-jp/awesome-japanese-llm) - 日本語LLMまとめ - Overview of Japanese LLMs.
-- [Awesome-LLM-Healthcare](https://github.com/mingze-yuan/Awesome-LLM-Healthcare) - The paper list of the review on LLMs in medicine.
-- [Awesome-LLM-Inference](https://github.com/DefTruth/Awesome-LLM-Inference) - A curated list of Awesome LLM Inference Paper with codes.
-- [LLMDatahub](https://github.com/Zjh-819/LLMDataHub) - a curated collection of datasets specifically designed for chatbot training, including links, size, language, usage, and a brief description of each dataset
 
 ## Other Useful Resources
 

contributing.md

@@ -16,3 +16,9 @@ edit your PR before we merge it. There's no need to open a new PR, just edit
 the existing one. If you're not sure how to do that,
 [here is a guide](https://github.com/RichardLitt/knowledge/blob/master/github/amending-a-commit-guide.md)
 on the different ways you can update your PR so that we can merge it.
+
+## how to add dynamic citation badge
+
+1. get paper id from semantic scholar paper page 
+2. create dynamic badge at [this site](https://shields.io/badges/dynamic-json-badge) with this link: https://api.semanticscholar.org/graph/v1/paper/{paper_id}?fields=citationCount
+3. ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F432bef8e34014d726c674bc458008ac895297b51%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)