tidybot
FlexGen
| tidybot | FlexGen | |
|---|---|---|
| 20 | 19 | |
| 490 | 5,350 | |
| - | - | |
| 6.4 | 10.0 | |
| 6 months ago | about 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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tidybot
- TidyBot: Personalized Robot Assistance with Large Language Models
- TidyBot Personalized Robot Assistance with Large Language Models
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MemGPT: Towards LLMs as Operating Systems
>they've solved reinforcement learning?
Transformers can do Reinforcement Learning yes.
https://arxiv.org/abs/2106.01345
>they can handle continuous domains, like robot motion?
Yes they can handle it just fine.
https://tidybot.cs.princeton.edu/
https://general-pattern-machines.github.io/
https://wayve.ai/thinking/lingo-natural-language-autonomous-...
- Large Language Models as General Pattern Machines. In context, LLMs are capable of completing a wide variety of non linguistic patterns.
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SuperAlignment
Other examples(in the real world) you might find interesting.
https://tidybot.cs.princeton.edu/
- Создан робот-уборщик, который самообучается наводить порядок именно так, как нравится вам. Видео.
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Is Amazon's newly announced home robot, in development & codenamed 'Burnham', unambitious and already behind the times?
It's striking how quickly robotics is developing in 2023. Two recent demonstrations from DeepMind & a Princeton team, show relatively cheap simple robots acquiring the ability to manipulate objects in the physical world. If you're going to be developing cutting-edge robots in 2023 - surely it would plan to incorporate this?
- What are the most impressive companies trying to create real world AI (real world navigation, object manipulation etc.)?
FlexGen
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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
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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
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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
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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?
MemGPT - Create LLM agents with long-term memory and custom tools 📚🦙
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
spacy-llm - 🦙 Integrating LLMs into structured NLP pipelines
CTranslate2 - Fast inference engine for Transformer models
Voyager - An Open-Ended Embodied Agent with Large Language Models
ggml - Tensor library for machine learning
git-agent - Langchain Agent utilizing OpenAI Function Calls to execute Git commands using Natural Language
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
dialop - DialOp: Decision-oriented dialogue environments for collaborative language agents
rust-bert - Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
evals - Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.