tidybot
FlexGen
| tidybot | FlexGen | |
|---|---|---|
| 20 | 39 | |
| 490 | 9,017 | |
| - | 0.9% | |
| 6.4 | 3.5 | |
| 6 months ago | 23 days 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
- Run 70B LLM Inference on a Single 4GB GPU with This New Technique
- Colorful Custom RTX 4060 Ti GPU Clocks Outed, 8 GB VRAM Confirmed
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Local Alternatives of ChatGPT and Midjourney
LLaMA, Pythia, RWKV, Flan-T5 (self-hosted), FlexGen
- FlexGen: Running large language models on a single GPU
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Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
> With no real knowledge of LLM and only recently started to understand what LLM terms mean, such as 'model, inference, LLM model, intruction set, fine tuning' whatelse do you think is required to make a took like yours?
This was mee a few weeks ago. I got interested in all this when FlexGen (https://github.com/FMInference/FlexGen) was announced, which allowed to run inference using OPT model on consumer hardware. I'm an avid user of Stable Diffusion, and I wanted to see if I can have an SD equivalent of ChatGPT.
Not understanding the details of hyperparameters or terminology, I basically asked ChatGPT to explain to me what these things are:
Explain to someone who is a software engineer with limited knowledge of ML terms or linear algebra, what is "feed forward" and "self-attention" in the context of ML and large language models. Provide examples when possible. - Could this new flexgen be used in place of GPTq? or is this different?
- OpenAI is expensive
What are some alternatives?
MemGPT - Create LLM agents with long-term memory and custom tools 📚🦙
llama - Inference code for Llama models
spacy-llm - 🦙 Integrating LLMs into structured NLP pipelines
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Voyager - An Open-Ended Embodied Agent with Large Language Models
text-generation-inference - Large Language Model Text Generation Inference
git-agent - Langchain Agent utilizing OpenAI Function Calls to execute Git commands using Natural Language
whisper.cpp - Port of OpenAI's Whisper model in C/C++
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
dialop - DialOp: Decision-oriented dialogue environments for collaborative language agents
audiolm-pytorch - Implementation of AudioLM, a SOTA Language Modeling Approach to Audio Generation out of Google Research, in Pytorch