nvidia/GPT-2B-001 · Hugging Face
GPT-2B-001 ||| Model Description GPT-2B-001 is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 2B refers to the total trainable parameter count (2 Billion) [1, 2]. This model was trained on 1.1T tokens with NeMo. Model Architecture improvements The model uses the SwiGLU activation function [4] Rotary positional embeddings (R
A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using transformers, accelerate and bitsandbytes
The 3 models are BLOOM-176B, T5-11B and T5-3B. Hugging Face transformers integration nuances Next let's discuss the specifics of the Hugging Face transformers integration. Let's look at the usage and the common culprit you may encounter while trying to set things up. Usage The module responsible for the whole magic described in this blog post is called Linear8bitLt and you can easily import it fro
Introduction to Graph Machine Learning
In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs. Graphs What is a graph?
The Technology Behind BLOOM Training
Please note that both Megatron-LM and DeepSpeed have Pipeline Parallelism and BF16 Optimizer implementations, but we used the ones from DeepSpeed as they are integrated with ZeRO. Megatron-DeepSpeed implements 3D Parallelism to allow huge models to train in a very efficient way. Let’s briefly discuss the 3D components. DataParallel (DP) - the same setup is replicated multiple times, and each being
nlp-waseda/roberta-base-japanese · Hugging Face
nlp-waseda/roberta-base-japanese Model description This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100. How to use You can use this model for masked language modeling as follows: from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese") model = AutoModelForMaskedLM
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Distributed training with 🤗 Accelerate
From PyTorch DDP to Accelerate to Trainer, mastery of distributed training with ease
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Tf Xla Generate Benchmarks - a Hugging Face Space by joaogante
https://huggingface.co/docs/transformers/_modules/transformers/optimization