nlp-resume-parser
metaseq
| nlp-resume-parser | metaseq | |
|---|---|---|
| 5 | 53 | |
| 216 | 6,389 | |
| - | 0.4% | |
| 5.3 | 1.0 | |
| 11 months ago | 19 days ago | |
| Python | Python | |
| - | MIT License |
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nlp-resume-parser
metaseq
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Training great LLMs from ground zero in the wilderness as a startup
This is a super important issue that affects the pace and breadth of iteration of AI almost as much as the raw hardware improvements do. The blog is fun but somewhat shallow and not technical or very surprising if you’ve worked with clusters of GPUs in any capacity over the years. (I liked the perspective of a former googler, but I’m not sure why past colleagues would recommend Jax over pytorch for LLMs outside of Google.) I hope this newco eventually releases a more technical report about their training adventures, like the PDF file here: https://github.com/facebookresearch/metaseq/tree/main/projec...
- Chronicles of Opt Development
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See the pitch memo that raised €105M for four-week-old startup Mistral
The number of people who can actually pre-train a true LLM is very small.
It remains a major feat with many tweaks and tricks. Case in point: the 114 pages of OPT175B logbook [1]
[1] https://github.com/facebookresearch/metaseq/blob/main/projec...
- Technologie: „Austro-ChatGPT“ – aber kein Geld zum Testen
- OPT (Open Pre-trained Transformers) is a family of NLP models trained on billions of tokens of text obtained from the internet
- Current state-of-the-art open source LLM
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Elon Musk Buys Ten Thousand GPUs for Secretive AI Project
Reliability at scale: take a look at the OPT training log book for their 175B model run. It needed a lot of babysitting. In my experience, that scale of TPU training run requires a restart about once every 1-2 weeks—and they provide the middleware to monitor the health of the cluster and pick up on hardware failures.
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Is AI Development more fun than Software Development?
I really appreciated this log of Facebook training a large language model of how troublesome AI development can be: https://github.com/facebookresearch/metaseq/tree/main/projects/OPT/chronicles
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Visual ChatGPT
Stable Diffusion will run on any decent gaming GPU or a modern MacBook, meanwhile LLMs comparable to GPT-3/ChatGPT have had pretty insane memory requirements - e.g., <https://github.com/facebookresearch/metaseq/issues/146>
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Ask HN: Is There On-Call in ML?
It seems so, check this log book from Meta: https://github.com/facebookresearch/metaseq/blob/main/projec...
What are some alternatives?
gpt-scrolls - A collaborative collection of open-source safe GPT-3 prompts that work well
stable-diffusion - A latent text-to-image diffusion model
NLP-Guide - Natural Language Processing (NLP). Covering topics such as Tokenization, Part Of Speech tagging (POS), Machine translation, Named Entity Recognition (NER), Classification, and Sentiment analysis.
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
azure-sql-db-openai - Samples on how to use Azure SQL database with Azure OpenAI
manim - Animation engine for explanatory math videos
cupscale - Image Upscaling GUI based on ESRGAN
ChatGPT.el - ChatGPT in Emacs
YaLM-100B - Pretrained language model with 100B parameters
min-dalle - min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch