gpt-fast
unsloth
| gpt-fast | unsloth | |
|---|---|---|
| 8 | 15 | |
| 5,152 | 8,974 | |
| 3.5% | 42.8% | |
| 8.3 | 9.4 | |
| 3 days ago | 3 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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gpt-fast
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[D] GPT-Fast performance on larger batch sizes
I'm toying around with gpt-fast (https://github.com/pytorch-labs/gpt-fast) and was wondering if anyone has run experiments @ BS>1?
- Optimum-NVIDIA - 28x faster inference in just 1 line of code !?
- GPT-Fast: Simple and efficient GPT inference in <1000 LOC of Python
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GPT-Fast: A fast and hackable implementation of transformer inference in <1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!
And check out the code here: https://github.com/pytorch-labs/gpt-fast
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80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
How does this compare to PyTorch labs optimizations for Sam and llama2 ?
https://github.com/pytorch-labs/segment-anything-fast
https://github.com/pytorch-labs/gpt-fast
- Fast and hackable PyTorch native transformer inference
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Accelerating Generative AI with PyTorch II: GPT, Fast
I'm wondering if gpt-fast has a version that can be run from Windows Command Prompt or Powershell?
https://github.com/pytorch-labs/gpt-fast/issues/45
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?
TensorRT-LLM - TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
llama.cpp - LLM inference in C/C++
stable-fast - Best inference performance optimization framework for HuggingFace Diffusers on NVIDIA GPUs.
nanoChatGPT - nanogpt turned into a chat model
optimum-nvidia
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
segment-anything-fast - A batched offline inference oriented version of segment-anything
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
uniteai - Your AI Stack in Your Editor
llm-toys - Small(7B and below) finetuned LLMs for a diverse set of useful tasks