GLM-130B
ChatGLM-6B
| GLM-130B | ChatGLM-6B | |
|---|---|---|
| 19 | 17 | |
| 7,616 | 39,378 | |
| 0.4% | 1.7% | |
| 4.8 | 8.4 | |
| 10 months ago | 3 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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
ChatGLM-6B
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What are the current fastest multi-gpu inference frameworks?
ChatGLM seems to be pretty popular but I've never used this before.
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A CEO is spending more than $2,000 a month on ChatGPT Plus accounts for all of his employees, and he says it's saving 'hours' of time
There are also locally hosted options that approach the effectiveness of ChatGPT. This GLM for example was specifically trained to be able to be processed on a single consumer grade GPU
- Open Source Chinese LLMs
- ChatGLM-6B: run locally on consumer graphics card (6GB of GPU memory required)
- Ask HN: Open source LLM for commercial use?
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Coding LLaMa Modell?
A link to for y'all. Definitely gonna try to mess around with this!
- 关于GPT，AI和未来的一些社会经济问题，向诸位请教
- FLiPN-FLaNK Stack Weekly for 20 March 2023
- ChatGLM-6B - an open source 6.2 billion parameter English/Chinese bilingual LLM trained on 1T tokens, supplemented by supervised fine-tuning, feedback bootstrap, and Reinforcement Learning from Human Feedback. Runs on consumer grade GPUs
- ChatGLM: Open bilingual language model based on General Language Model framework
What are some alternatives?
PaLM-rlhf-pytorch - Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
llama.cpp - LLM inference in C/C++
ggml - Tensor library for machine learning
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
petals - 🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
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
datagen - Generate authentic looking mock data based on a SQL, JSON or Avro schema and produce to Kafka in JSON or Avro format.
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.