PromptCraft-Robotics
FlexGen
| PromptCraft-Robotics | FlexGen | |
|---|---|---|
| 4 | 39 | |
| 1,714 | 9,007 | |
| 2.3% | 0.8% | |
| 1.1 | 3.0 | |
| 3 months ago | 14 days 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.
PromptCraft-Robotics
- Eu entendo como o CHATGPT elabora textos, mas nao entendo como ele é capaz de dizer qual é a resposta certa numa questão de múltipla escolha, nem como ele resolve problemas matemáticos. Alguém poderia explicar ?
-
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
People are already creating sophisticated prompts that are literally engineering problems, so prompting has indeed become a tool of its own.
- ChatGPT for Robotics
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
-
Local Alternatives of ChatGPT and Midjourney
LLaMA, Pythia, RWKV, Flan-T5 (self-hosted), FlexGen
- FlexGen: Running large language models on a single GPU
-
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?
visual-chatgpt - Official repo for the paper: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models [Moved to: https://github.com/microsoft/TaskMatrix]
llama - Inference code for Llama models
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
text-generation-inference - Large Language Model Text Generation Inference
whisper.cpp - Port of OpenAI's Whisper model in C/C++
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
audiolm-pytorch - Implementation of AudioLM, a SOTA Language Modeling Approach to Audio Generation out of Google Research, in Pytorch
minimal-llama
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
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]
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
stable-diffusion-webui - Stable Diffusion web UI