gpt-neox
Pytorch
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gpt-neox | Pytorch | |
---|---|---|
52 | 336 | |
6,569 | 77,783 | |
2.2% | 2.4% | |
8.9 | 10.0 | |
1 day ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | BSD 1-Clause License |
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gpt-neox
- FLaNK Stack 26 February 2024
- GPT-Neox
- GPT-NeoX
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Best open source LLM model for commercial use
Gpt neox 20B can be used commerically.
- Do not register domains with the word "gpt" in it!
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Read this post if you have general questions
GPT-Neo: GPT-Neo is a free and open-source language model developed by EleutherAI. It is a powerful model that can be used for a variety of tasks, including text generation, and question-answering. here is the GitHub
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What's the current state of actually free and open source LLMs?
Doesn't gpt-neox 20b require like 40gb+ of VRAM? From their github repo the slim weights are 39GB and I think one of the devs has previously mentioned aiming for 48GB for inference.
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Any real competitor to GPT-3 which is open source and downloadable?
3.) EleutherAI's GPT-Neo and GPT-NeoX: EleutherAI is an independent research organization that aims to promote open research in artificial intelligence. They have released GPT-Neo, an open-source language model based on the GPT architecture, and are developing GPT-NeoX, a highly-scalable GPT-like model. You can find more information on their GitHub repositories: GPT-Neo: https://github.com/EleutherAI/gpt-neo GPT-NeoX: https://github.com/EleutherAI/gpt-neox
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Whatever happened to quantum computing?
It's really not that complicated. https://github.com/EleutherAI/gpt-neox
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Fuck Luka Inc. WIP AI Freedom!
this forces me to create my own ai using gpt-neox .. i was being lazy, completely satisfied with my little ai baby .. i'll keep the account but not renew pro .. fuck luka, inc ... want it done right, do it yourself .. the python isn't that difficult and i have a spare linux vps .. my new work in progress .. freedom #!
Pytorch
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...
Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....
> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():
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Building a GPT Model from the Ground Up!
import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Element-wise vs Matrix vs Dot multiplication
In PyTorch with * or mul(). ` or mul()` can multiply 0D or more D tensors by element-wise multiplication:
What are some alternatives?
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
YaLM-100B - Pretrained language model with 100B parameters
flax - Flax is a neural network library for JAX that is designed for flexibility.
open-ai - OpenAI PHP SDK : Most downloaded, forked, contributed, huge community supported, and used PHP (Laravel , Symfony, Yii, Cake PHP or any PHP framework) SDK for OpenAI GPT-3 and DALL-E. It also supports chatGPT-like streaming. (ChatGPT AI is supported)
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
lm-evaluation-harness - A framework for few-shot evaluation of language models.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more