LeanDojoChatGPT
FlexGen
| LeanDojoChatGPT | FlexGen | |
|---|---|---|
| 2 | 19 | |
| 99 | 5,350 | |
| - | - | |
| 5.3 | 10.0 | |
| about 1 month ago | about 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
LeanDojoChatGPT
-
'A-Team' of Math Proves a Critical Link Between Addition and Sets
Check out this paper:
https://leandojo.org/
People have already trained models to assist suggestion tactics. They then linked it up to ChatGPT to interactively solve proofs.
In this scenario, ChatGPT asks the model for tactic suggestions, applies it to the proof and uses the feedback from Lean to then proceed with the next step.
FYI, The programmatic interface to Lean was written by an OpenAI employee who was on the Lean team a few years ago.
Also, check out Lean’s roadmap. They aspire to position Lean to becoming a target for LLMs because it has been designed for verification from the ground up.
As math and compsci nerds contribute to mathlib, all of those proofs are also building up a huge corpus that will likely be leveraged for both verification and optimization.
If AI can make verification a lot easier, then we’re likely going to see verification change programming similarly to the way it changed electronics.
-
Formalizing 100 Theorems
Good questions!
Nowadays, there is indeed a movement towards interoperability between the various proof assistants, one of these bridge-building projects is called Dedukti: https://deducteam.github.io/ It's a challenging project because the different proof assistants which are currently in use differ in their foundational perspectives and their idioms. The question how to best formalize mathematics is still an open research problem, just as the question how to best develop programs, but we already have quite a good understanding of many important issues in this area.
Also, by now there are attempts to use AI for discovering proofs, see for instance https://leandojo.org/ or https://github.com/lean-dojo/LeanDojoChatGPT.
FlexGen
-
Training LLaMA-65B with Stanford Code
#1: Progress Update | 4 comments #2: the default UI on the pinned Google Colab is buggy so I made my own frontend - YAFFOA. | 18 comments #3: Paper reduces resource requirement of a 175B model down to 16GB GPU | 19 comments
-
Replika users fell in love with their AI chatbot companions. Then they lost them
It's really just a gpu vram limitation: affordable GPUs are rather memory starved.
Fortunately people have started writing implementations for pipelining across multiple gpus.
https://github.com/Ying1123/FlexGen
- Same as with Stable Diffusion, new AI based LAION, are coming up slowly but surely: Paper reduces resource requirement of a 175B model down to 16GB GPU
- And Here..We..Go: Running large language models like ChatGPTon a single GPU. Up to 100x faster than other offloading systems
-
When, how and why will this Stable Diffusion spring stop?
Actually there's a solution : read this paper https://github.com/Ying1123/FlexGen/blob/main/docs/paper.pdf
-
Exciting new shit.
Flexgen - Run big models on your small GPU https://github.com/Ying1123/FlexGen
- Paper reduces resource requirement of a 175B model down to 16GB GPU
- FlexGen - Run 175B Parameter Models on consumer hardware
- Running large language models like ChatGPT on a single GPU
- FlexGen: Running large language models like ChatGPT/GPT-3/OPT-175B on a single GPU
What are some alternatives?
upgini - Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
marqo - Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
CTranslate2 - Fast inference engine for Transformer models
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
ggml - Tensor library for machine learning
set.mm - Metamath source file for logic and set theory
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
linc - 🔗 LINC: Logical Inference via Neurosymbolic Computation [EMNLP2023]
rust-bert - Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
ChatGPT-API-Python - Building a Chatbot in Python using OpenAI's Official ChatGPT API
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.