GLM-130B
PaLM-rlhf-pytorch
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GLM-130B | PaLM-rlhf-pytorch | |
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19 | 25 | |
7,607 | 7,590 | |
0.9% | - | |
4.8 | 4.6 | |
9 months ago | 3 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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GLM-130B
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GLM-130B
The https://github.com/THUDM/GLM-130B model is trained on The Pile and can run on 4x3090 when quantized to INT4. I'm wondering if anyone knows if this model could (or has) been quantized using GPTQ, which gives some impressive performance gains over traditional quantization, and I'm also wondering if anyone has tried a 3-bit or 2-bit quantization of such a massive model (using GPTQ). Are there any inherent limitations in this? Is there anything about this model that prevents it from being run on text-generation-webui?
- Has anyone tried GLM?
- Ask HN: Open source LLM for commercial use?
- Whichever way I look at it, I just don’t see this being the case. Why do you agree/disagree?
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The New Bing and ChatGPT
> GLM-130B, a model comparable with GPT-3, has 130 billion parameters in FP16 precision, a total of 260G of GPU memory is required to store model weights. The DGX-A100 server has 8 A100s and provides an amount of 320G of GPU memory (640G for 80G A100 version) so it suits GLM-130B well.
https://github.com/THUDM/GLM-130B/blob/main/docs/low-resourc...
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OpenAI Major Outage
GLM-130B[1] (a 130 billion parameter model vs GPT-3's 175 billion parameter model) is able to run optimally on consumer level high-end hardware, 4xRTX 3090 in particular. That's < $4k at current prices, and as hardware prices go one can only imagine what it'll be in a year or two. It also enables running with degraded performance on lesser systems.
It's a whole lot cheaper to run neural net style systems than to train them. "Somebody on Twitter"[2] got it setup, and broke down the costs, demonstrated some prompts, and what not. Cliff notes being a fraction of a penny per query, with each taking about 16s to generate. The output's pretty terrible, but it's unclear to me whether that's inherent or a result of priority. I expect OpenAI spent a lot of manpower on supervised training, whereas this system probably had minimal, especially in English (it's from a Chinese university).
[1] - https://github.com/THUDM/GLM-130B
[2] - https://twitter.com/alexjc/status/1617152800571416577
- [D]Are there any known AI systems today that are significantly more advanced than chatGPT ?
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Will there ever be a "Stable Diffusion chat AI" that we can run at home like one can do with Stable Diffusion? A "roll-your-own at home ChatGPT"?
GLM-130B in 4 bit mode is better than GPT3 and can run on 4 RTX-3090s. Still expensive but it’s getting closer. https://github.com/THUDM/GLM-130B
- Open-Source competitor to OpenAI?
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Ask HN: Can you crowdfund the compute for GPT?
https://github.com/THUDM/GLM-130B might be a useful place to look
PaLM-rlhf-pytorch
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How should I get an in-depth mathematical understanding of generative AI?
ChatGPT isn't open sourced so we don't know what the actual implementation is. I think you can read Open Assistant's source code for application design. If that is too much, try Open Chat Toolkit's source code for developer tools . If you need very bare implementation, you should go for lucidrains/PaLM-rlhf-pytorch.
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[P] Open-source PaLM models trained at 8k context length
AFAIK, it is not. They are using the open-source re-implementation of Phil Wang (aka lucidrains), which is available here: https://github.com/lucidrains/PaLM-rlhf-pytorch
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Should AI language models be free software?
Not sure what do you mean by putting source code in double quote, but I don't think the source code is petabytes of text. GPT-2 implementation is few hundred lines of Python (in HuggingFace). PaLM + RLHF - Pytorch (Basically ChatGPT but with PaLM) is less than 1000 lines.
- Would a decentralized open-source platform of ChatGPT work?
- Exciting new shit.
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Top 10 Best Open Source GitHub repos for Developers 2023
GitHub Link: https://github.com/lucidrains/PaLM-rlhf-pytorch
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Gather up great coders and make a better Character.Ai
Well... Not necessarily. Actually, if you want to be extra thrifty, you could even go without an ML expert. Just use an open-source one, like LaMDA or PaLM. After that, use chatGPT to build you a basic front end (which would still be better than CAI lol).
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Open-Source competitor to OpenAI?
and PaLM with RLHF from Phil Wang (open model, needs to be trained): https://github.com/lucidrains/PaLM-rlhf-pytorch
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Microsoft in talks to acquire a 49% stake in ChatGPT owner OpenAI
Closest you can get is probably with Google T5-Flan [1].
It is not the size of the model or the text it was trained on that makes ChatGPT so performant. It is the additional human assisted training to make it respond well to instructions. Open source versions of that are just starting to see the light of day [2].
[1] https://huggingface.co/google/flan-t5-xxl
[2] https://github.com/lucidrains/PaLM-rlhf-pytorch
- Will we have a free version of ChatGPT (GPT-3) similar to Stable Diffusion?
What are some alternatives?
ggml - Tensor library for machine learning
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
petals - 🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
lm-human-preferences - Code for the paper Fine-Tuning Language Models from Human Preferences
trlx - A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
metaseq - Repo for external large-scale work
Rath - Next generation of automated data exploratory analysis and visualization platform.