LLMSurvey
LLMs-from-scratch
| LLMSurvey | LLMs-from-scratch | |
|---|---|---|
| 3 | 9 | |
| 8,902 | 16,129 | |
| 9.2% | - | |
| 7.9 | 9.6 | |
| 4 months ago | about 11 hours ago | |
| Python | Jupyter Notebook | |
| - | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LLMSurvey
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Ask HN: Textbook Regarding LLMs
Here’s another one - it’s older but has some interesting charts and graphs.
https://arxiv.org/abs/2303.18223
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Share your favorite materials: intersection of LLMs and business applications
There have recently been some some nice early surveys on progress, pitfalls, future research directions:
- A Survey of LLMs https://arxiv.org/abs/2303.18223
- A Survey of Large Language Models
LLMs-from-scratch
- Finetune a GPT Model for Spam Detection on Your Laptop in Just 5 Minutes
- Insights from Finetuning LLMs for Classification Tasks
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Ask HN: Textbook Regarding LLMs
https://www.manning.com/books/build-a-large-language-model-f...
- Comparing 5 ways to implement Multihead Attention in PyTorch
- FLaNK Stack 29 Jan 2024
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Implementing a ChatGPT-like LLM from scratch, step by step
The attention mechanism we implement in this book* is specific to LLMs in terms of the text inputs, but it's fundamentally the same attention mechanism that is used in vision transformers. The only difference is that in LLMs, you turn text into tokens, and convert these tokens into vector embeddings that go into an LLM. In vision transformers, instead of regarding images as tokens, you use an image patch as a token and turn those into vector embeddings (a bit hard to explain without visuals here). In both text or vision context, it's the same attention mechanism, and it both cases it receives vector embeddings.
(*Chapter 3, already submitted last week and should be online in the MEAP soon, in the meantime the code along with the notes is also available here: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch03/01...)
What are some alternatives?
ChatGLM2-6B - ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型
s4 - Structured state space sequence models
mPLUG-Owl - mPLUG-Owl & mPLUG-Owl2: Modularized Multimodal Large Language Model
Chinese-LLaMA-Alpaca - 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
HugNLP - CIKM2023 Best Demo Paper Award. HugNLP is a unified and comprehensive NLP library based on HuggingFace Transformer. Please hugging for NLP now!😊
safe-rlhf - Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
Qwen - The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud.
opening-up-chatgpt.github.io - Tracking instruction-tuned LLM openness. Paper: Liesenfeld, Andreas, Alianda Lopez, and Mark Dingemanse. 2023. “Opening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instruction-Tuned Text Generators.” In Proceedings of the 5th International Conference on Conversational User Interfaces. doi:10.1145/3571884.3604316.
Multi-Modality-Arena - Chatbot Arena meets multi-modality! Multi-Modality Arena allows you to benchmark vision-language models side-by-side while providing images as inputs. Supports MiniGPT-4, LLaMA-Adapter V2, LLaVA, BLIP-2, and many more!
NExT-GPT - Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model
WebGLM - WebGLM: An Efficient Web-enhanced Question Answering System (KDD 2023)
private-gpt - Deploy smart and secure conversational agents for your employees, using Azure. Able to use both private and public data.