Flowise
DeepSpeed
| Flowise | DeepSpeed | |
|---|---|---|
| 21 | 51 | |
| 25,017 | 32,942 | |
| 9.0% | 1.9% | |
| 9.9 | 9.8 | |
| 2 days ago | about 20 hours ago | |
| TypeScript | Python | |
| Apache License 2.0 | 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.
Flowise
- FLaNK Stack Weekly 12 February 2024
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Docker Image not running. Error Command failed with exit code 127
The GitHub repo is: https://github.com/FlowiseAI/Flowise
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Show HN: Rivet – open-source AI Agent dev env with real-world applications
- https://github.com/FlowiseAI/Flowise
It's absolutely ok if the answer is "Yes", I think that in this hot market each product will find a place. And competition is also motivate :)
It would be also nice to add Rivet here:
- Show HN: ChainForge, a visual tool for prompt engineering and LLM evaluation
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Bing claims my Yamaha sound bar has a 3.5 mm mini-jack, and when the error is pointed out it doubles down by inventing a reference to a non-existing manual, including a firmware update adding a physical 3.5 mm input port
If you're doing anything more custom or advanced than the regular ChatGPT type of interface can handle, you can use Flowise to build your own bot with any number of advanced plugins like internet search, calculators, recursion, file read/write access, long-term memory, other AI's...
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How to add SystemMessage to ConversationalRetrievalQAChain?
Dove into Flowise Docs for a useful example.
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How to create a Langchain application that can Chat with multiple large JSON files
For rapid prototyping try Flowise
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What exactly is AutoGPT?
Flowise as well, you are right!
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LocalAI v1.18.0 release!
Flowise
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April 2023
Drag & drop UI to build your customized LLM flow using LangchainJS (https://github.com/FlowiseAI/Flowise)
DeepSpeed
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
DeepSpeed can handle parallelism concerns, and even offload data/model to RAM, or even NVMe (!?) . I'm surprised I don't see this project used more.
- [P][D] A100 is much slower than expected at low batch size for text generation
- DeepSpeed-FastGen: High-Throughput for LLMs via MII and DeepSpeed-Inference
- DeepSpeed-FastGen: High-Throughput Text Generation for LLMs
- Why async gradient update doesn't get popular in LLM community?
- DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models (r/MachineLearning)
- [P] DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
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A comprehensive guide to running Llama 2 locally
While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:
* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.
* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].
You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)
Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)
[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...
[2] https://github.com/microsoft/DeepSpeed/issues/1580
[3] https://github.com/TimDettmers/bitsandbytes/issues/485
[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...
[5] https://forums.macrumors.com/threads/ai-generated-art-stable...
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Microsoft Research proposes new framework, LongMem, allowing for unlimited context length along with reduced GPU memory usage and faster inference speed. Code will be open-sourced
And https://github.com/microsoft/deepspeed
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April 2023
DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales (https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)
What are some alternatives?
langflow - ⛓️ Langflow is a dynamic graph where each node is an executable unit. Its modular and interactive design fosters rapid experimentation and prototyping, pushing hard on the limits of creativity.
ColossalAI - Making large AI models cheaper, faster and more accessible
llama.go - llama.go is like llama.cpp in pure Golang!
Megatron-LM - Ongoing research training transformer models at scale
chatbot-ui - AI chat for every model.
fairscale - PyTorch extensions for high performance and large scale training.
rivet-example
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
deepdoctection - A Repo For Document AI
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
hugo-quick-start - Hugo Quick Start on Render
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.