Posts by Phillip Dang

DBRX Instruct on AMD GPUs

In this blog, we showcase DBRX Instruct, a mixture-of-experts large language model developed by Databricks, on a ROCm-capable system with AMD GPUs.

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Deep Learning Recommendation Models on AMD GPUs

28, June 2024 by

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Unveiling performance insights with PyTorch Profiler on an AMD GPU

29 May, 2024 by

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Table Question-Answering with TaPas

26 Apr, 2024 by

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Multimodal (Visual and Language) understanding with LLaVA-NeXT

26, Apr 2024 by

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Text Summarization with FLAN-T5

In this blog, we showcase the language model FLAN-T5 and how to fine-tune it on a summarization task with HuggingFace in an AMD GPUs + ROCm system.

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Program Synthesis with CodeGen

CodeGen is a family of standard transformer-based auto-regressive language models for program synthesis, which as defined by the authors as a method for generating computer programs that solve specified problems, using input-output examples or natural language descriptions.

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Small language models with Phi-2

Like many other LLMs, Phi-2 is a transformer-based model with a next-word prediction objective that is trained on billions of tokens. At 2.7 billion parameters, Phi-2 is a relatively small language model, but it achieves outstanding performance on a variety of tasks, including common sense reasoning, language understanding, math, and coding. For reference, GPT 3.5 has 175 billion parameters and the smallest version of LLaMA-2 has 7 billion parameters. According to Microsoft, Phi-2 is capable of matching or outperforming models up to 25 times larger due to more carefully curated training data and model scaling.

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Using the ChatGLM-6B bilingual language model with AMD GPUs

ChatGLM-6B is an open bilingual (Chinese-English) language model with 6.2 billion parameters. It’s optimized for Chinese conversation based on General Language Model (GLM) architecture. GLM is a pretraining framework that seeks to combine the strengths of autoencoder models (like BERT) and autoregressive models (like GPT). The GLM framework randomly blanks out continuous spans of tokens from the input text (autoencoding methodology) and trains the model to sequentially reconstruct the spans (autoregressive pretraining methodology).

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Building a decoder transformer model on AMD GPU(s)

12, Mar 2024 by

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Question-answering Chatbot with LangChain on an AMD GPU

11, Mar 2024 by

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Music Generation With MusicGen on an AMD GPU

MusicGen is an autoregressive, transformer-based model that predicts the next segment of a piece of music based on previous segments. This is a similar approach to language models predicting the next token.

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Simplifying deep learning: A guide to PyTorch Lightning

8, Feb 2024 by

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