phind-for-firefox
DeepSpeed
phind-for-firefox | DeepSpeed | |
---|---|---|
53 | 51 | |
1 | 32,942 | |
- | 2.2% | |
10.0 | 9.8 | |
about 1 year ago | 3 days ago | |
Python | ||
Mozilla Public License 2.0 | Apache License 2.0 |
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phind-for-firefox
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April 2023
AI search engine for developers (https://www.phind.com/)
- best chatgpt website alternatives (save message 🧑‍💻)
- BEST ChatGPT Website Alternatives (save message 🧑‍💻)
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Is ChatGPT incompetent or do I suck at prompt engineering?
Try something like https://www.phind.com which uses resources like Stackoverflow and others to generate more correct answers, it’s not perfect but it’s a very good way to interrogate “someone” about a topic which is how you should use these tools.
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GPT-4 dissuading me from using it
I use www.phind.com for software engineering assistance. I think it's using something like GPT-4 under the hood.
- Best ChatGPT Alternatives
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AI Co-pilots for SwiftUI: what do you guys recommend? (I’ve been using ChatGPT4 with great success but it’s knowledge cutoff is 2021 ie. only up to iOS 14)
Check out https://www.phind.com, it works great for me. I don't understand fully how it works but I guess it's GPT4 with web search capability below the surface. What's weird is that it's fully free.
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A lawyer used ChatGPT for legal filing. The chatbot cited nonexistent cases it just made up
On its own a search engine no. But it confabulates less when you give it one
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?
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
ColossalAI - Making large AI models cheaper, faster and more accessible
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
Megatron-LM - Ongoing research training transformer models at scale
chatbox - Chatbox is a desktop client for ChatGPT, Claude and other LLMs, available on Windows, Mac, Linux
fairscale - PyTorch extensions for high performance and large scale training.
textSQL
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
colab-tunnel - Connect to Google Colab VM locally from VSCode [Moved to: https://github.com/amitness/colab-connect]
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
evaporate - This repo contains data and code for the paper "Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes"
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.