accelerate
unsloth
accelerate | unsloth | |
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18 | 15 | |
6,996 | 8,282 | |
2.9% | 29.3% | |
9.7 | 9.4 | |
1 day ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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accelerate
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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
unsloth
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Ask HN: Most efficient way to fine-tune an LLM in 2024?
Gemma 7b is 2.4x faster than HF + FA2.
Check out https://github.com/unslothai/unsloth for full benchmarks!
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Gemma doesn't suck anymore – 8 bug fixes
Here are the missing links:
* Gemma, a family of open models from Google: https://ai.google.dev/gemma
* Unsloth is a tool/method for training models faster (IIUC): https://github.com/unslothai/unsloth
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AMD ROCm Software Blogs
Thanks! Again, partnerships over customers. If you're experienced and have the technical chops to make a MI300x sing, we want to work with you. Our model is that we are the capex/opex investor for businesses. As much as I love software, Hot Aisle is more of a hardware business. Running super high end large scale compute is an extreme challenge in itself. We are less interested in building the software side of things and want to foster those who can focus on that side.
https://github.com/unslothai/unsloth/issues/160
https://github.com/search?q=repo%3Apredibase%2Florax+rocm&ty...
https://github.com/sgl-project/sglang/issues/157
https://github.com/casper-hansen/AutoAWQ (supports rocm)
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Show HN: We got fine-tuning Mistral-7B to not suck
Unsloth’s colab notebooks for fine-tuning Mistral-7B are super easy to use and run fine in just about any colab instance:
https://github.com/unslothai/unsloth
It’s my default now for experimenting and basic training. If I want to get into the weeds with the training, I use axolotl, but 9/10, it’s not really necessary.
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Mistral 7B Fine-Tune Optimized
If anyone wants to finetune their own Mistral 7b model 2.2x faster and use 62% less memory - give our open source package Unsloth a try! https://github.com/unslothai/unsloth a try! :)
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Has anyone tried out the ASPEN-Framework for LoRA Fine-Tuning yet and can share their experience?
https://github.com/unslothai/unsloth seems good and more relevant to your aims perhaps but I haven't tried it.
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
The unsloth project offers some low-level optimizations for Llama et al, and as of today some prelim Mistral work (which I heard is the llama architecture?)
- Show HN: 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
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80% faster, 50% less memory, 0% accuracy loss Llama finetuning
This seems to just be a link to the Unsloth Github repo[0], which in turn is the free version of Unsloth Pro/Max[1]. Maybe the link should be changed?
[0]: https://github.com/unslothai/unsloth
- 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
llama.cpp - LLM inference in C/C++
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]
nanoChatGPT - nanogpt turned into a chat model
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
gpt-fast - Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
stable-diffusion-webui - Stable Diffusion web UI
uniteai - Your AI Stack in Your Editor