From e53f8c968fadbdd671c4f8a1e702a23b635f1f5a Mon Sep 17 00:00:00 2001 From: Richard Kuo Date: Tue, 19 Dec 2023 21:16:11 +0800 Subject: [PATCH] Delete _posts/2023-12-11-LLM.md --- _posts/2023-12-11-LLM.md | 418 --------------------------------------- 1 file changed, 418 deletions(-) delete mode 100644 _posts/2023-12-11-LLM.md diff --git a/_posts/2023-12-11-LLM.md b/_posts/2023-12-11-LLM.md deleted file mode 100644 index c5391aee..00000000 --- a/_posts/2023-12-11-LLM.md +++ /dev/null @@ -1,418 +0,0 @@ ---- -layout: post -title: Large Language Models -author: [Richard Kuo] -category: [Lecture] -tags: [jekyll, ai] ---- - -Introduction to Language Models, LLMs, Algorithms for building LLMs, etc. - ---- -## History of LLM -[A Survey of Large Language Models](https://www.semanticscholar.org/paper/A-Survey-of-Large-Language-Models-Zhao-Zhou/c61d54644e9aedcfc756e5d6fe4cc8b78c87755d)
-Since the introduction of Transformer model in 2017, large language models (LLMs) have evolved significantly. ChatGPT saw 1.6B visits in May 2023. Meta also released three versions of LLaMA-2 (7B, 13B, 70B) free for commercial use in July. - -### 從解題能力來看語言模型四個世代的演進 -An evolution process of the four generations of language models (LM) from the perspective of task solving capacity.
-![](https://d3i71xaburhd42.cloudfront.net/c61d54644e9aedcfc756e5d6fe4cc8b78c87755d/2-Figure2-1.png) - -### 大型語言模型統計表 -![](https://d3i71xaburhd42.cloudfront.net/c61d54644e9aedcfc756e5d6fe4cc8b78c87755d/8-Table1-1.png) - ---- -### 近年大型語言模型(>10B)的時間軸 -![](https://d3i71xaburhd42.cloudfront.net/c61d54644e9aedcfc756e5d6fe4cc8b78c87755d/9-Figure3-1.png) - ---- -### 大型語言模型之產業分類 -![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F2e8bfd65-5272-4cf1-8b86-954bab975bab_2400x1350.png) - ---- -### 大型語言模型之技術分類 -![](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*vZK250i8PIWid6BiaZ1QCA.png) - ---- -### 計算記憶體的成長與Transformer大小的關係 -[AI and Memory Wall](https://medium.com/riselab/ai-and-memory-wall-2cb4265cb0b8)
-![](https://miro.medium.com/v2/resize:fit:4800/format:webp/0*U-7GJqBZ2tY1W5Iu) - ---- -### LLMops 針對生成式 AI 用例調整了 MLops 技術堆疊 -![](https://www.insightpartners.com/wp-content/uploads/2023/10/llmops-market-map-1.png) - ---- -## Transformer -**Paper:** [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
-**Code:** [huggingface/transformers](https://github.com/huggingface/transformers)
-![](https://miro.medium.com/max/407/1*3pxDWM3c1R_WSW7hVKoaRA.png) - - - - - -
- ---- -### New Understanding about Transformer -**Blog:**
-* [Researchers Gain New Understanding From Simple AI](https://www.quantamagazine.org/researchers-glimpse-how-ai-gets-so-good-at-language-processing-20220414/) -* [Transformer稱霸的原因找到了？OpenAI前核心員工揭開注意力頭協同工作機理](https://bangqu.com/A76oX7.html) - -**Papers:**
-* [A Mathematical Framework for Transformer Circuits](https://transformer-circuits.pub/2021/framework/index.html) -* [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) - ---- -### BERT -**Paper:** [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)
-**Blog:** [進擊的BERT：NLP 界的巨人之力與遷移學習](https://leemeng.tw/attack_on_bert_transfer_learning_in_nlp.html)
- ---- -### GPT (Generative Pre-Training Transformer) -**Paper:** [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
-**Paper:** [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
- -**Code:** [https://github.com/huggingface/transformers](https://github.com/huggingface/transformers)
- -### GPT-2 -**Paper:** [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf)
-**Code:** [openai/gpt-2](https://github.com/openai/gpt-2)
-**GPT2 Demo:** [Transformer Demo](https://app.inferkit.com/demo), [GPT-2 small](https://minimaxir.com/apps/gpt2-small/)
-**Blog:** [直觀理解GPT2語言模型並生成金庸武俠小說](https://leemeng.tw/gpt2-language-model-generate-chinese-jing-yong-novels.html)
- ---- -### T5: Text-To-Text Transfer Transformer (by Google) -**Paper:** [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)
-**Code:** [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer)
-![](https://1.bp.blogspot.com/-89OY3FjN0N0/XlQl4PEYGsI/AAAAAAAAFW4/knj8HFuo48cUFlwCHuU5feQ7yxfsewcAwCLcBGAsYHQ/s640/image2.png) - ---- -### GPT-3 -**Code:** [openai/gpt-3](https://github.com/openai/gpt-3)
-**[GPT-3 Demo](https://gpt3demo.com/)**
-![](https://dzlab.github.io/assets/2020/07/20200725-gpt3-model-architecture.png) - ---- -### [CKIP Lab 繁體中文詞庫小組](https://ckip.iis.sinica.edu.tw/) -CKIP (CHINESE KNOWLEDGE AND INFORMATION PROCESSING): 繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具。
-[CKIP Lab 下載軟體與資源](https://ckip.iis.sinica.edu.tw/resource)
-* [CKIP Transformers](https://github.com/ckiplab/ckip-transformers) -* [CKIP Tagger](https://github.com/ckiplab/ckiptagger)
- ---- -## Question Answering -### [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) - The Stanford Question Answering Dataset
-**Paper:** [Know What You Don't Know: Unanswerable Questions for SQuAD](https://arxiv.org/abs/1806.03822)
-

- ---- -### Instruct GPT -**Paper:** [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
-**Blog:** [Aligning Language Models to Follow Instructions](https://openai.com/blog/instruction-following/)
- ---- -### ChatGPT -[ChatGPT: Optimizing Language Models for Dialogue](https://openai.com/blog/chatgpt/)
-ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022.
- -![](https://cdn.openai.com/chatgpt/draft-20221129c/ChatGPT_Diagram.svg) - - - ---- -### [LLaMA](https://huggingface.co/docs/transformers/main/model_doc/llama) -*It is a collection of foundation language models ranging from 7B to 65B parameters.*
-**Paper:** [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
-![](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*nt-ydHhSVsaLXq_HZRaLQA.png) - ---- -### [OpenLLaMA](https://github.com/openlm-research/open_llama) -**model:** [https://huggingface.co/openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-openllama](https://www.kaggle.com/code/rkuo2000/llm-openllama)
- ---- -**Blog:** [Building a Million-Parameter LLM from Scratch Using Python](https://levelup.gitconnected.com/building-a-million-parameter-llm-from-scratch-using-python-f612398f06c2)
-**Kaggle:** [LLM LLaMA from scratch](https://www.kaggle.com/rkuo2000/llm-llama-from-scratch/)
- ---- -### Pythia -**Paper:** [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://arxiv.org/abs/2304.01373)
-[Datasheet for the Pile](https://arxiv.org/abs/2201.07311)
-**Code:** [Pythia: Interpreting Transformers Across Time and Scale](https://github.com/EleutherAI/pythia)
- ---- -### Falcon-40B -**Paper:** [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://arxiv.org/abs/2306.01116)
-**Code:** [https://huggingface.co/tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
- ---- -### LLaMA-2 -**Paper:** [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://arxiv.org/abs/2307.09288)
-**Code:** [https://github.com/facebookresearch/llama](https://github.com/facebookresearch/llama)
-**models:** [https://huggingface.co/meta-llama](https://huggingface.co/meta-llama)
- ---- -### GPT4 -**Paper:** [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774)
-![](https://image-cdn.learnin.tw/bnextmedia/image/album/2023-03/img-1679884936-23656.png?w=1200&output=webp) - ---- -### MiniGPT-4 -**Paper:** [MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models](https://arxiv.org/abs/2304.10592)
-**Paper:** [MiniGPT-v2: Large Language Model as a Unified Interface for Vision-Language Multi-task Learning](https://arxiv.org/abs/2310.09478)
-**Code:** [https://github.com/Vision-CAIR/MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4)
- -![](https://github.com/Vision-CAIR/MiniGPT-4/raw/main/figs/minigpt2_demo.png) -![](https://github.com/Vision-CAIR/MiniGPT-4/raw/main/figs/online_demo.png) - ---- -### LLM Lingua -**Paper: [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://arxiv.org/abs/2310.05736)
-**Code: [https://github.com/microsoft/LLMLingua](https://github.com/microsoft/LLMLingua)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-lingua](https://www.kaggle.com/code/rkuo2000/llm-lingua)
-![](https://github.com/microsoft/LLMLingua/raw/main/images/LLMLingua.png) - ---- -### Mistral Transformer -**Paper:** [Mistral 7B](https://arxiv.org/abs/2310.06825)
-**Code:** [https://github.com/mistralai/mistral-src](https://github.com/mistralai/mistral-src)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-mistral-7b-instruct](https://www.kaggle.com/code/rkuo2000/llm-mistral-7b-instruct)
- ---- -### Zephyr -**Paper:** [Zephyr: Direct Distillation of LM Alignment](https://arxiv.org/abs/2310.16944)
-**Code:** [https://huggingface.co/HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-zephyr-7b](https://www.kaggle.com/code/rkuo2000/llm-zephyr-7b)
-![](https://i3.res.bangqu.com/farm/liang/news/2023/10/28/9e3a1a498f94b147fd57608b4beaefe0.jpg) - ---- -### SOLAR-10.7B ~ Depth Upscaling -**Code:** [https://huggingface.co/upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
-Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model.
-Leveraging state-of-the-art instruction fine-tuning methods, including supervised fine-tuning (SFT) and direct preference optimization (DPO), -researchers utilized a diverse set of datasets for training. This fine-tuned model, SOLAR-10.7B-Instruct-v1.0, achieves a remarkable Model H6 score of 74.20, -boasting its effectiveness in single-turn dialogue scenarios.
- ---- -### Phi-2 (Transformer with 2.7B parameters) -**Blog:** [Phi-2: The surprising power of small language models](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/)
-**Code:** [https://huggingface.co/microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-phi-2](https://www.kaggle.com/code/rkuo2000/llm-phi-2)
- ---- -### FlagEmbedding -**Paper:** [Retrieve Anything To Augment Large Language Models](https://arxiv.org/abs/2310.07554)
-**Code:** [https://github.com/FlagOpen/FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-flagembedding](https://www.kaggle.com/code/rkuo2000/llm-flagembedding)
-![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2a4e4265-7dab-4c5d-b14f-5dfd1b270e75_746x735.png) - ---- -### LM-Cocktail -**Paper:** [LM-Cocktail: Resilient Tuning of Language Models via Model Merging](https://arxiv.org/abs/2311.13534)
-**Code:** [https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- ---- -### LongLoRA -**Code:** [https://github.com/dvlab-research/LongLoRA](https://github.com/dvlab-research/LongLoRA)
-[2023.11.19] We release a new version of LongAlpaca models, LongAlpaca-7B-16k, LongAlpaca-7B-16k, and LongAlpaca-7B-16k.
-![](https://github.com/dvlab-research/LongLoRA/raw/main/imgs/LongAlpaca.png) - ---- -### Magicoder -**Paper:** [Magicoder: Source Code Is All You Need](https://arxiv.org/abs/2312.02120)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-magicoder](https://www.kaggle.com/code/rkuo2000/llm-magicoder)
-![](https://github.com/ise-uiuc/magicoder/raw/main/assets/overview.svg) - ---- -### [ALTER-LLM](https://tnoinkwms.github.io/ALTER-LLM/) -**Paper:** [From Text to Motion: Grounding GPT-4 in a Humanoid Robot "Alter3"](https://arxiv.org/abs/2312.06571)
- -![](https://tnoinkwms.github.io/ALTER-LLM/architecture_2.png) -![](https://tnoinkwms.github.io/ALTER-LLM/feedback.png) - ---- -### EAGLE-LLM -**Blog:** [EAGLE: Lossless Acceleration of LLM Decoding by Feature Extrapolation](https://sites.google.com/view/eagle-llm)
-**Code:** [https://github.com/SafeAILab/EAGLE](https://github.com/SafeAILab/EAGLE)
-**Kaggle:** [https://www.kaggle.com/code/rkuo2000/eagle-llm](https://www.kaggle.com/code/rkuo2000/eagle-llm)
- ---- -### Purple Llama CyberSecEval -**Paper:** [Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models](https://arxiv.org/abs/2312.04724)
-**Code:** [CybersecurityBenchmarks](https://github.com/facebookresearch/PurpleLlama/tree/main/CybersecurityBenchmarks)
-[meta-llama/LlamaGuard-7b](https://huggingface.co/meta-llama/LlamaGuard-7b)
- - - - - -
Our Test Set (Prompt)OpenAI ModToxicChatOur Test Set (Response)
Llama-Guard0.9450.8470.6260.953
OpenAI API 0.7640.8560.5880.769
Perspective API0.7280.7870.5320.699
- ---- -## Building LLM -[Patterns for Building LLM-based Systems & Products](https://eugeneyan.com/writing/llm-patterns/) -![](https://eugeneyan.com/assets/llm-patterns-og.png) - -### [Retrieval Augmented Generation (RAG)](https://arxiv.org/abs/2005.11401) to Add Knowledge -![](https://eugeneyan.com/assets/rag.jpg) - ---- -#### [Fusion-in-Decoder (FiD)](https://arxiv.org/abs/2007.01282) -![](https://eugeneyan.com/assets/fid.jpg) - ---- -#### [Retrieval-Enhanced Transformer (RETRO)](https://arxiv.org/abs/2112.04426) -![](https://eugeneyan.com/assets/retro.jpg) - ---- -#### [Internet-augmented LMs](https://arxiv.org/abs/2203.05115) -![](https://eugeneyan.com/assets/internet-llm.jpg) - ---- -#### [Overview of RAG for CodeT5+](https://arxiv.org/abs/2305.07922) -![](https://eugeneyan.com/assets/codet5.jpg) - ---- -#### [Hypothetical document embeddings (HyDE)](https://arxiv.org/abs/2212.10496) -![](https://eugeneyan.com/assets/hyde.jpg) - ---- -### Fine-tuning : To get better at specific tasks - -#### [ULMFit](https://arxiv.org/abs/1801.06146) -![](https://eugeneyan.com/assets/ulmfit.jpg) - ---- -#### [Bidirectional Encoder Representations from Transformers (BERT; encoder only)](https://arxiv.org/abs/1810.04805) -![](https://eugeneyan.com/assets/bert.jpg) - ---- -#### [Generative Pre-trained Transformers (GPT; decoder only)](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) -![](https://eugeneyan.com/assets/gpt.jpg) - ---- -#### [Text-to-text Transfer Transformer (T5; encoder-decoder)](https://arxiv.org/abs/1910.10683) -![](https://eugeneyan.com/assets/t5.jpg) - ---- -#### [InstructGPT](https://arxiv.org/abs/2203.02155) -![](https://eugeneyan.com/assets/instructgpt.jpg) - ---- -#### [Soft prompt tuning](https://arxiv.org/abs/2104.08691) -**Paper:** [Soft-prompt Tuning for Large Language Models to Evaluate Bias](https://arxiv.org/abs/2306.04735)
-**Blog:** [Guiding Frozen Language Models with Learned Soft Prompts](https://blog.research.google/2022/02/guiding-frozen-language-models-with.html)
-![](https://blogger.googleusercontent.com/img/a/AVvXsEgWPnqNhC2ZtEjkumYCtNi18nHLQY9U5dmV13cJzQzscVhcHYhLdpTdTv-1ZI3IaOVfWE9x7y4g75jtyImEaI7dsonfD43S24flWsevDgEdbA0oR5w6fJsnFecnKGysSguLKJKEQ5svS-aQn_ClNZm6jURazpAxFNWTQoTm708a4hFq8f2HzMVpz3wZ_g=w640-h360) -![](https://blogger.googleusercontent.com/img/a/AVvXsEgNi-pteVLIEZ6H5HdV8RadrzCkegKA3zJCM2ObwTHKKYhgF7b-c7qsN85P1j4nXcqHcIDTj2dU5KfslYU4PuIFXaDpF6o_e5jMfFWljd6Kpc0E1n-UG6LtMA5B_BIAKjWTUibhwCnQ2zWap9BiZgA-VB0bxQG-S1jMcUHZ01kl0uLIKIoqKYH8QtUiYA=s693) - ---- -#### [prefix tuning](https://arxiv.org/abs/2101.00190) -![](https://eugeneyan.com/assets/prefix.jpg) - ---- -#### [adapter](https://arxiv.org/abs/1902.00751) -![](https://eugeneyan.com/assets/adapter.jpg) - ---- -#### [Low-Rank Adaptation (LoRA)](https://arxiv.org/abs/2106.09685) -![](https://eugeneyan.com/assets/lora.jpg) - ---- -#### [QLoRA](https://arxiv.org/abs/2305.14314) -![](https://eugeneyan.com/assets/qlora.jpg) - ---- -### Caching: To reduce latency and cost - -#### [GPTCache](https://github.com/zilliztech/GPTCache) -![](https://eugeneyan.com/assets/gptcache.jpg) - ---- -### LLM Kaggle-examples: -[https://www.kaggle.com/code/rkuo2000/llm-chromadb-langchain](https://www.kaggle.com/code/rkuo2000/llm-chromadb-langchain)
-[https://www.kaggle.com/code/rkuo2000/llm-finetuning](https://www.kaggle.com/code/rkuo2000/llm-finetuning/)
-[https://www.kaggle.com/code/rkuo2000/llama2-7b-hf-finetune](https://www.kaggle.com/code/rkuo2000/llama2-7b-hf-finetune)
-[https://www.kaggle.com/code/rkuo2000/llama2-qlora](https://www.kaggle.com/code/rkuo2000/llama2-qlora)
- ---- -### [Open-LLMs](https://github.com/eugeneyan/open-llms) -Open LLMs
-Open LLM for Coder
- ---- -## LLM Coders - -### AlphaCode -**Paper:** [Competition-Level Code Generation with AlphaCode](https://arxiv.org/pdf/2203.07814.pdf)
-![](https://victordibia.com/static/alphacode-2292e53c73500c1103f2f1fccec3f33d.png) - ---- -### AlphaCode 2 -**Report:** [AlphaCode 2 Technical Report](https://storage.googleapis.com/deepmind-media/AlphaCode2/AlphaCode2_Tech_Report.pdf)
-![](https://cdn.bulldogjob.com/system/photos/files/000/013/124/original/AlphaCode_2_overview.png) - ---- -### StarCoder -**Paper:** [StarCoder: may the source be with you!](https://arxiv.org/abs/2305.06161)
-The StarCoder models are 15.5B parameter models trained on **80+** programming languages from The Stack (v1.2), with opt-out requests excluded. The model uses Multi Query Attention, a context window of 8192 tokens, and was trained using the Fill-in-the-Middle objective on 1 trillion tokens.
- ---- -### StarChat-Alpha -**Blog:** [Creating a Coding Assistant with StarCoder](https://huggingface.co/blog/starchat-alpha)
- ---- -### DeciCoder -**Blog:** [Introducing DeciCoder: The New Gold Standard in Efficient and Accurate Code Generation](https://deci.ai/blog/decicoder-efficient-and-accurate-code-generation-llm/)
- ---- -### CodeGen2.5 -**Blog:** [CodeGen2.5: Small, but mighty](https://blog.salesforceairesearch.com/codegen25/)
-**Paper:** [CodeGen2: Lessons for Training LLMs on Programming and Natural Languages](https://arxiv.org/abs/2305.02309)
-**Code:** [https://github.com/salesforce/CodeGen/tree/main/codegen25](https://github.com/salesforce/CodeGen/tree/main/codegen25)
- ---- -### Code Llama -**Paper:** [Code Llama: Open Foundation Models for Code](https://arxiv.org/abs/2308.12950)
-![](https://miro.medium.com/v2/resize:fit:4800/format:webp/1*0wXBmrJYzHnTvIupJL_TeQ.png) -**Kaggle:** [https://www.kaggle.com/rkuo2000/llm-code-llama](https://www.kaggle.com/rkuo2000/llm-code-llama)
- ---- -## Thoughts - -### Tree of Thoughts -**Paper:** [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601)
-**Code:** [https://github.com/princeton-nlp/tree-of-thought-llm](https://github.com/princeton-nlp/tree-of-thought-llm)
-![](https://github.com/princeton-nlp/tree-of-thought-llm/blob/master/pics/teaser.png?raw=true) - ---- -### XoT -**Paper:** [Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation](https://arxiv.org/abs/2311.04254)
-![](https://miro.medium.com/v2/resize:fit:720/format:webp/0*r_a44DuxG3D8DGZO.png) - ---- -### FunSearch -[DeepMind發展用LLM解困難數學問題的方法](https://www.ithome.com.tw/news/160354)
-![](https://s4.itho.me/sites/default/files/styles/picture_size_large/public/field/image/2108_-_funsearch_making_new_discoveries_in_mathematical_sciences_using_lar_-_deepmind.google.jpg?itok=mAy4ydAE) - ---- -### BrainGPT -**Paper:** [DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation](https://arxiv.org/abs/2309.14030)
-**Blog:** [New Mind-Reading "BrainGPT" Turns Thoughts Into Text On Screen](https://www.iflscience.com/new-mind-reading-braingpt-turns-thoughts-into-text-on-screen-72054)
-![](https://i3.res.bangqu.com/farm/liang/news/2023/12/18/339b9a2158e1fd28e1e39ee4b1557df2.jpg) -![](https://i3.res.bangqu.com/farm/liang/news/2023/12/18/79ca704627e4cadc1e23afc1b2f029cb.jpg) - - ---- -### [Run Llama 2 Locally in 7 Lines! (Apple Silicon Mac)](https://blog.lastmileai.dev/run-llama-2-locally-in-7-lines-apple-silicon-mac-c3f46143f327) -![](https://miro.medium.com/v2/resize:fit:4800/format:webp/1*81Zzsz8opkq8eBUbpRHlng.png) -On an `M2 Max MacBook Pro`, I was able to get 35–40 tokens per second using the LLAMA_METAL build flag.
- -### [LLaMA-2-7B Benchmark](https://github.com/liltom-eth/llama2-webui/blob/main/docs/performance.md) - - -
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