ChatGLM-6B
ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型 (by THUDM)
accelerate
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support (by huggingface)
| ChatGLM-6B | accelerate | |
|---|---|---|
| 17 | 18 | |
| 39,341 | 6,996 | |
| 1.6% | 2.9% | |
| 8.4 | 9.7 | |
| 2 months ago | 1 day ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ChatGLM-6B
Posts with mentions or reviews of ChatGLM-6B.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-25.
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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
accelerate
Posts with mentions or reviews of accelerate.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-06.
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
accelerate is a best-in-class lib for deploying models, especially across multi-gpu and multi-node.
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Code Llama - The Hugging Face Edition
In the coming days, we'll work on sharing scripts to train models, optimizations for on-device inference, even nicer demos (and for more powerful models), and more. Feel free to like our GitHub repos (transformers, peft, accelerate). Enjoy!
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What are the current fastest multi-gpu inference frameworks?
So I rent a cloud server today to try out some of the recent LLMs like falcon and vicuna. I started with huggingface's generate API using accelerate. It got about 2 instances/s with 8 A100 40GB GPUs which I think is a bit slow. I was using batch size = 1 since I do not know how to do multi-batch inference using the .generate API. I did torch.compile + bf16 already. Do we have an even faster multi-gpu inference framework? I have 8 GPUs so I was thinking about MUCH faster speed like ~10 or 20 instances per second (or is it possible at all? I am pretty new to this field).
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Looking at lefnire's suggestion of splitting huggingface batches per gradient_accumulation_steps
Looking through https://github.com/huggingface/accelerate/tree/main/src/accelerate/utils/ I think it might be feasible, but will require some modifications to:
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Have to abandon my (almost) finished LLaMA-API-Inference server. If anybody finds it useful and wants to continue, the repo is yours. :)
As /u/RabbitHole32 already mentioned, the speed increase stems from a patch which modifies, how a certain, large tensor is distributed between the GPU's. The patch was created by /u/emvw7yf. Here you can find the respective GitHub issue: https://github.com/huggingface/accelerate/issues/1394
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Help please! SD installation broken
::pip install git+https://github.com/huggingface/accelerate
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Batch Controlnet
pip install controlnet_aux pip install diffusers transformers git+https://github.com/huggingface/accelerate.git
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[D] Large Language Models feasible to run on 32GB RAM / 8 GB VRAM / 24GB VRAM
Try to use both GPUs with this one: https://github.com/huggingface/accelerate https://huggingface.co/docs/accelerate/usage_guides/big_modeling https://huggingface.co/blog/accelerate-large-models Maybe it will help (the last link is clearer IMHO).
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Fine Tuning Stable Diffusion with Dreambooth from Within My Python Code
I read through this page on accelerate, but it's not clear to me how the arguments such as instance_prompt gets passed in.
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What does ACCELERATE do in AUTOMATIC1111?
To activate it you have to uncomment webui-user.sh line 44 and adding set ACCELERATE="True" to webui-user.bat. It seems to use huggingface/accelerate (Microsoft DeepSpeed, ZeRO paper) ACCELERATE
What are some alternatives?
When comparing ChatGLM-6B and accelerate you can also consider the following projects:
llama.cpp - LLM inference in C/C++
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
datagen - Generate authentic looking mock data based on a SQL, JSON or Avro schema and produce to Kafka in JSON or Avro format.
unsloth - Finetune Llama 3, Mistral & Gemma LLMs 2-5x faster with 80% less memory
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.
stable-diffusion-webui - Stable Diffusion web UI
ChatGLM-6B vs llama.cpp
accelerate vs DeepSpeed
ChatGLM-6B vs alpaca.cpp
accelerate vs bitsandbytes
ChatGLM-6B vs stanford_alpaca
accelerate vs FlexGen
ChatGLM-6B vs Open-Assistant
accelerate vs horovod
ChatGLM-6B vs datagen
accelerate vs unsloth
ChatGLM-6B vs basaran
accelerate vs stable-diffusion-webui