DeepSpeed
mesh-transformer-jax
| DeepSpeed | mesh-transformer-jax | |
|---|---|---|
| 52 | 52 | |
| 35,787 | 6,286 | |
| 1.5% | - | |
| 9.7 | 0.0 | |
| 5 days ago | almost 2 years ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
DeepSpeed
- DeepSpeed-Domino: Communication-Free LLM Training Engine
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
DeepSpeed can handle parallelism concerns, and even offload data/model to RAM, or even NVMe (!?) . I'm surprised I don't see this project used more.
- [P][D] A100 is much slower than expected at low batch size for text generation
- DeepSpeed-FastGen: High-Throughput for LLMs via MII and DeepSpeed-Inference
- DeepSpeed-FastGen: High-Throughput Text Generation for LLMs
- Why async gradient update doesn't get popular in LLM community?
- DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models (r/MachineLearning)
- [P] DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
- A comprehensive guide to running Llama 2 locally
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Microsoft Research proposes new framework, LongMem, allowing for unlimited context length along with reduced GPU memory usage and faster inference speed. Code will be open-sourced
And https://github.com/microsoft/deepspeed
mesh-transformer-jax
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Large Language Models: Compairing Gen2/Gen3 Models (GPT-3, GPT-J, MT5 and More)
GPT-J is a LLM case study with two goals: Training a LLM with a data source containing unique material, and using the training frameworkMesh Transformer JAX to achieve a high training efficiency through parallelization. There is no research paper about GPT-J, but on its GitHub pages, the model, different checkpoints, and the complete source code for training is given.
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[R] Parallel Attention and Feed-Forward Net Design for Pre-training and Inference on Transformers
This idea has already been proposed in ViT-22B and GPT-J-6B.
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
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[D] An Instruct Version Of GPT-J Using Stanford Alpaca's Dataset
Sure. Here's the repo I used for the fine-tuning: https://github.com/kingoflolz/mesh-transformer-jax. I used 5 epochs, and appart from that I kept the default parameters in the repo.
- Boss wants me to use ChatGPT for work, but I refuse to input my personal phone number. Any advice?
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Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy
You can skip to step 4 using something like GPT-J as far as I understand: https://github.com/kingoflolz/mesh-transformer-jax#links
The pretrained model is already available.
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Best coding model?
The Github repo suggests it's possible you can change the number of checkpoints to make it run on a GPU.
- Ask HN: What language models can I fine-tune at home?
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selfhosted/ open-source ChatGPT alternative?
GPT-J, which uses mesh-transformer-jax: https://github.com/kingoflolz/mesh-transformer-jax
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GPT-J, an open-source alternative to GPT-3
They hinted at it in the screenshot, but the goods are linked from the https://6b.eleuther.ai page: https://github.com/kingoflolz/mesh-transformer-jax#gpt-j-6b (Apache 2)
What are some alternatives?
ColossalAI - Making large AI models cheaper, faster and more accessible
tensorflow - An Open Source Machine Learning Framework for Everyone
Megatron-LM - Ongoing research training transformer models at scale
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
fairscale - PyTorch extensions for high performance and large scale training.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
KoboldAI-Client - For GGUF support, see KoboldCPP: https://github.com/LostRuins/koboldcpp
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
Finetune_LLMs - Repo for fine-tuning Casual LLMs
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
alpaca-lora - Instruct-tune LLaMA on consumer hardware