GenAi, Deep Learning and Computer Vision

How you can train Large Language Models?

Large language models (LLMs) are gaining significant popularity due to their versatility in text generation, translation, and question-answering tasks. However, training these models can be resource-intensive and time-consuming. LLMs examples include 𝐆𝐏𝐓-3 and 𝐆𝐏𝐓-4 from 𝐎𝐩𝐞𝐧𝐀𝐈, 𝐋𝐋𝐚𝐌𝐀 from 𝐌𝐞𝐭𝐚, 𝐚𝐧𝐝 𝐏𝐚𝐋𝐌2 from 𝐆𝐨𝐨𝐠𝐥𝐞.

Several LLM training frameworks have emerged to address this challenge, offering solutions to streamline and enhance the training process. Here are some of the most popular frameworks that help you to train and tuning LLMs Models:

✅ Deepspeed: An efficient deep learning optimization library that simplifies distributed training and inference, enabling easy and effective implementation.
Examples: https://www.deepspeed.ai/

✅ Megatron-DeepSpeed: A DeepSpeed version of NVIDIA's Megatron-LM, offering additional support for MoE model training, Curriculum Learning, 3D Parallelism, and other advanced features.
Examples: https://huggingface.co/blog/bloom-megatron-deepspeed

✅ FairScale: A PyTorch extension library designed for high-performance and large-scale training, empowering researchers and practitioners to train models more efficiently.
Example: https://fairscale.readthedocs.io/en/latest/tutorials/oss.html

✅ Megatron-LM: A research-focused framework dedicated to training transformer models at scale, facilitating ongoing exploration in the field.
Examples:https://huggingface.co/blog/megatron-training

✅ Colossal-AI: A platform that aims to make large AI models more accessible, faster, and cost-effective, contributing to democratizing AI advancements.
Examples: https://github.com/hpcaitech/ColossalAI/tree/main/examples

✅ BMTrain: An efficient training framework tailored for big models, enabling smoother and more effective training processes.
Examples: https://github.com/OpenBMB/BMTrain

✅ Mesh TensorFlow: A framework simplifying model parallelism, making it easier to leverage distributed computing resources for training large models.
Examples: https://github.com/tensorflow/mesh

✅ Max text: A performant and scalable Jax LLM framework designed to simplify the training process while maintaining high performance.
Examples: https://github.com/EleutherAI/maxtext

✅ Alpa: A system specifically developed for training and serving large-scale neural networks, offering comprehensive support for training requirements.
Examples: https://alpa.ai/opt

✅ GPT-NeoX: An implementation of model parallel autoregressive transformers on GPUs, built on the DeepSpeed library, providing enhanced training capabilities.
Examples: https://blog.eleuther.ai/announcing-20b/

If you're interested in training LLMs, I encourage you to explore these frameworks. They can significantly simplify and optimize the training process, allowing you to achieve better results efficiently.

DeepSpeed

Latest News

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

👍5❤3

www.tgoop.com/awesomedeeplearning/208

2.11K viewsArtificial Intelligence, Jul 5, 2023 at 13:30

How you can train Large Language Models?

Large language models (LLMs) are gaining significant popularity due to their versatility in text generation, translation, and question-answering tasks. However, training these models can be resource-intensive and time-consuming. LLMs examples include 𝐆𝐏𝐓-3 and 𝐆𝐏𝐓-4 from 𝐎𝐩𝐞𝐧𝐀𝐈, 𝐋𝐋𝐚𝐌𝐀 from 𝐌𝐞𝐭𝐚, 𝐚𝐧𝐝 𝐏𝐚𝐋𝐌2 from 𝐆𝐨𝐨𝐠𝐥𝐞.

Several LLM training frameworks have emerged to address this challenge, offering solutions to streamline and enhance the training process. Here are some of the most popular frameworks that help you to train and tuning LLMs Models:

✅ Deepspeed: An efficient deep learning optimization library that simplifies distributed training and inference, enabling easy and effective implementation.
Examples: https://www.deepspeed.ai/

✅ Megatron-DeepSpeed: A DeepSpeed version of NVIDIA's Megatron-LM, offering additional support for MoE model training, Curriculum Learning, 3D Parallelism, and other advanced features.
Examples: https://huggingface.co/blog/bloom-megatron-deepspeed

✅ FairScale: A PyTorch extension library designed for high-performance and large-scale training, empowering researchers and practitioners to train models more efficiently.
Example: https://fairscale.readthedocs.io/en/latest/tutorials/oss.html

✅ Megatron-LM: A research-focused framework dedicated to training transformer models at scale, facilitating ongoing exploration in the field.
Examples:https://huggingface.co/blog/megatron-training

✅ Colossal-AI: A platform that aims to make large AI models more accessible, faster, and cost-effective, contributing to democratizing AI advancements.
Examples: https://github.com/hpcaitech/ColossalAI/tree/main/examples

✅ BMTrain: An efficient training framework tailored for big models, enabling smoother and more effective training processes.
Examples: https://github.com/OpenBMB/BMTrain

✅ Mesh TensorFlow: A framework simplifying model parallelism, making it easier to leverage distributed computing resources for training large models.
Examples: https://github.com/tensorflow/mesh

✅ Max text: A performant and scalable Jax LLM framework designed to simplify the training process while maintaining high performance.
Examples: https://github.com/EleutherAI/maxtext

✅ Alpa: A system specifically developed for training and serving large-scale neural networks, offering comprehensive support for training requirements.
Examples: https://alpa.ai/opt

✅ GPT-NeoX: An implementation of model parallel autoregressive transformers on GPUs, built on the DeepSpeed library, providing enhanced training capabilities.
Examples: https://blog.eleuther.ai/announcing-20b/

If you're interested in training LLMs, I encourage you to explore these frameworks. They can significantly simplify and optimize the training process, allowing you to achieve better results efficiently.

BY GenAi, Deep Learning and Computer Vision