Posts tagged LLM

Enhancing LLM Accessibility: A Deep Dive into QLoRA Through Fine-tuning Llma 2 on a single AMD GPU

Building on the previous blog Fine-tune Llama 2 with LoRA blog, we delve into another Parameter Efficient Fine-Tuning (PEFT) approach known as Quantized Low Rank Adaptation (QLoRA). The focus will be on leveraging QLoRA for the fine-tuning of Llama-2 7B model using a single AMD GPU with ROCm. This task, made possible through the use of QLoRA, addresses challenges related to memory and computing limitations. The exploration aims to showcase how QLoRA can be employed to enhance accessibility to open-source large language models.

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Retrieval Augmented Generation (RAG) using LlamaIndex

To run this blog, you will need the following:

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Inferencing and serving with vLLM on AMD GPUs

vLLM is a high-performance, memory-efficient serving engine for large language models (LLMs). It leverages PagedAttention and continuous batching techniques to rapidly process LLM requests. PagedAttention optimizes memory utilization by partitioning the Key-Value (KV) cache into manageable blocks. The KV cache stores previously computed keys and values, enabling the model to focus on calculating attention solely for the current token. These blocks are subsequently managed through a lookup table, akin to memory page handling in operating systems.

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Building semantic search with SentenceTransformers on AMD

In this blog, we explain how to train a SentenceTransformers model on the Sentence Compression dataset to perform semantic search. We use the BERT base model (uncased) as the base transformer and apply Hugging Face PyTorch libraries.

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Scale AI applications with Ray

Most machine-learning (ML) workloads today require multiple GPUs or nodes to achieve the performance or scale required by applications. However, scaling workloads beyond single node/single GPU workloads is difficult and require some expertise in distributed processing.

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Large language model inference optimizations on AMD GPUs

Large language models (LLMs) have transformed natural language processing and comprehension, facilitating a multitude of AI applications in diverse fields. LLMs have various promising use cases, including AI assistants, chatbots, programming, gaming, learning, searching, and recommendation systems. These applications leverage the capabilities of LLMs to provide personalized and interactive experiences, which enhances user engagement.

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Using LoRA for efficient fine-tuning: Fundamental principles

Low-Rank Adaptation of Large Language Models (LoRA) is used to address the challenges of fine-tuning large language models (LLMs). Models like GPT and Llama, which boast billions of parameters, are typically cost-prohibitive to fine-tune for specific tasks or domains. LoRA preserves pre-trained model weights and incorporates trainable layers within each model block. This results in a significant reduction in the number of parameters that need to be fine-tuned and considerably reduces GPU memory requirements. The key benefit of LoRA is that it substantially decreases the number of trainable parameters–sometimes by a factor of up to 10,000–leading to a considerable decrease in GPU resource demands.

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Fine-tune Llama 2 with LoRA: Customizing a large language model for question-answering

In this blog, we show you how to fine-tune Llama 2 on an AMD GPU with ROCm. We use Low-Rank Adaptation of Large Language Models (LoRA) to overcome memory and computing limitations and make open-source large language models (LLMs) more accessible. We also show you how to fine-tune and upload models to Hugging Face.

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Pre-training BERT using Hugging Face & TensorFlow on an AMD GPU

This blog explains an end-to-end process for pre-training the Bidirectional Encoder Representations from Transformers (BERT) base model from scratch using Hugging Face libraries with a TensorFlow backend for English corpus text (WikiText-103-raw-v1).

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Pre-training BERT using Hugging Face & PyTorch on an AMD GPU

This blog explains an end-to-end process for pre-training the Bidirectional Encoder Representations from Transformers (BERT) base model from scratch using Hugging Face libraries with a PyTorch backend for English corpus text (WikiText-103-raw-v1).

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Accelerating XGBoost with Dask using multiple AMD GPUs

XGBoost is an optimized library for distributed gradient boosting. It has become the leading machine learning library for solving regression and classification problems. For a deeper dive into how gradient boosting works, we recommend reading Introduction to Boosted Trees.

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LLM distributed supervised fine-tuning with JAX

In this article, we review the process for fine-tuning a Bidirectional Encoder Representations from Transformers (BERT)-based large language model (LLM) using JAX for a text classification task. We explore techniques for parallelizing this fine-tuning procedure across multiple AMD GPUs, then evaluate our model’s performance on a holdout dataset. For this, we use a (BERT)-base-cased transformer model with a General Language Understanding Evaluation (GLUE) benchmark dataset on multiple AMD GPUs.

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Pre-training a large language model with Megatron-DeepSpeed on multiple AMD GPUs

In this blog, we show you how to pre-train a GPT-3 model using the Megatron-DeepSpeed framework on multiple AMD GPUs. We also demonstrate how to perform inference on the text-generation task with your pre-trained model.

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Efficient deployment of large language models with Text Generation Inference on AMD GPUs

Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs) with unparalleled efficiency. TGI is tailored for popular open-source LLMs, such as Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5. Optimizations include tensor parallelism, token streaming using Server-Sent Events (SSE), continuous batching, and optimized transformers code. It has a robust feature set that includes quantization, safetensors, watermarking (for determining if text is generated from language models), logits warper, and support for custom prompt generation and fine-tuning.

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