petals
GLM-130B
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| petals | GLM-130B | |
|---|---|---|
| 98 | 19 | |
| 8,661 | 7,610 | |
| 2.5% | 0.9% | |
| 8.5 | 4.8 | |
| 13 days ago | 9 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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petals
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Mistral Large
So how long until we can do an open source Mistral Large?
We could make a start on Petals or some other open source distributed training network cluster possibly?
[0] https://petals.dev/
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Distributed Inference and Fine-Tuning of Large Language Models over the Internet
Can check out their project at https://github.com/bigscience-workshop/petals
- Make no mistake—AI is owned by Big Tech
- Would you donate computation and storage to help build an open source LLM?
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Run 70B LLM Inference on a Single 4GB GPU with This New Technique
There is already an implementation along the same line using the torrent architecture.
https://petals.dev/
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Run LLMs in bittorrent style
Check it out at Petals.dev. Chatbot
- Is distributed computing dying, or just fading into the background?
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Ask HN: Are there any projects currently exploring distributed AI training?
https://github.com/bigscience-workshop/petals
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Mistral 7B,The complete Guide of the Best 7B model
https://github.com/bigscience-workshop/petals
Inference only: https://lite.koboldai.net/
- Run LLMs at home, BitTorrent‑style
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
What are some alternatives?
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
PaLM-rlhf-pytorch - Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
llama - Inference code for Llama models
ggml - Tensor library for machine learning
alpaca-lora - Instruct-tune LLaMA on consumer hardware
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
lm-human-preferences - Code for the paper Fine-Tuning Language Models from Human Preferences
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
metaseq - Repo for external large-scale work