The field of large language models (LLMs) has witnessed a paradigm shift with the advent of model merging, a novel approach that combines multiple LLMs into a unified architecture without additional training, offering a cost-effective strategy for new model development. This technique has sparked a surge in experimentation due to its potential to democratize the development of foundational models. However, the reliance on human intuition and domain knowledge in model merging has been a limiting factor, calling for a more systematic method to explore new model combinations. …
Reframing Large Language Models (LLMs) as agents has ushered in a new paradigm of automation. Researchers and practitioners have increasingly been using these models as agents to automate complex tasks using specialized functions. However, integrating useful functions into LLM agents often requires manual effort and extensive iterations, which is time-consuming and inefficient. Inspired by the analogy of humans continuously forging tools to adapt to tasks, this paper introduces a novel approach to train LLM agents by forging their functions, treating them as learnable 'agent parameters', without modifying the LLM weights. This paradigm, termed 'Agent Training', involves updating the agent's functions to maximize task-solving ability, offering a promising avenue for developing specialized LLM agents efficiently. …
When fine-tuning Large Language Models (LLMs) like GPT-3 or BERT for specific tasks, a common challenge encountered is "forgetting" – where the model loses some of its pre-trained capabilities. This phenomenon is particularly noticeable in Parameter-Efficient Fine-Tuning (PEFT) methods such as Low-Rank Adapters (LoRA). …
Large language models (LLMs) are cornerstone technologies in AI, driving advancements across various fields. However, the traditional approach of re-training LLMs with every new data set is both costly and computationally inefficient. This paper presents a novel approach, focusing on continual pre-training, which allows for the incremental updating of LLMs without the need for full re-training, significantly saving computational resources. …
Low-Rank Adapters (LoRA) have emerged as a popular parameter-efficient fine-tuning method for large language models. By adding trainable low-rank "adapters" to selected layers, LoRA enables effective fine-tuning while dramatically reducing the number of parameters that need to be trained. However, the conventional LoRA method uses a scaling factor for the adapters that divides them by the rank. A new paper by researcher Damjan Kalajdzievski shows that this rank-dependent scaling actually slows down learning and limits performance improvements when using higher-rank adapters. …
Google has just introduced Gemma, an innovative family of state-of-the-art open Large Language Models (LLMs), marking a significant stride in the open-source AI landscape. This release, featuring both 7B and 2B parameter models, underscores Google's ongoing commitment to open-source AI. The Hugging Face team is thrilled to support this launch, ensuring seamless integration within our ecosystem. …
A key challenge has been improving these models beyond a certain point, especially without the continuous infusion of human-annotated data. A groundbreaking paper by Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu presents an innovative solution: Self-Play Fine-Tuning (SPIN). …
In the realm of artificial intelligence, the confluence of visual and language data represents a groundbreaking shift. The Large Language and Vision Assistant (LLaVA) model exemplifies this evolution. Unlike traditional AI models, LLaVA integrates visual inputs with linguistic context, offering a more holistic understanding of both textual and visual data. …
In the ever-evolving landscape of technology, the fusion of artificial intelligence with software development has opened new horizons. The paper "A Survey on Language Models for Code" provides a comprehensive overview of this fascinating evolution. From the early days of statistical models to the sophisticated era of Large Language Models (LLMs) and Transformers, the journey of code processing models has been nothing short of revolutionary. …
We've taken on the exciting challenge of implementing the cutting-edge strategies presented in "ZEPHYR: Direct Distillation of LM Alignment". This paper's approach is not just theoretical—it's a blueprint for a significant leap in language model training. By adopting ZEPHYR's distilled direct preference optimization (dDPO), we've embarked on a code journey that brings these innovations from concept to reality. …
Model fine-tuning and quantization play pivotal roles in creating efficient and robust machine learning solutions. This blog post explores the fine-tuning process of the Zephyr 7B GPT-Q model using 4-bit quantization to boost its performance for custom data inference tasks. …
In today's post, we delve into a recent paper that investigates the intricacies of Reinforcement Learning in the context of Large Language Models (LLMs). This study shines a light on the challenges and nuances of training such models to align better with human preferences. …
In the rapidly evolving landscape of large language models (LLMs), enhancing their capabilities and performance is pivotal. Three prominent techniques that stand out in achieving this are: …
In the vast realm of machine learning, fine-tuning stands out as one of the most crucial techniques for adapting pre-trained models to new tasks. Ludwig, a deep learning toolkit, offers a diverse palette of fine-tuning strategies that cater to different needs. In this blog, we'll delve into these techniques, especially focusing on the Quantization-Based Fine-Tuning (QLoRA) method, as we explore the Code Alpaca project's efforts in instruction-based code generation using LLaMA models. …
In the AI realm, language models are paramount. From revolutionizing chatbots to pioneering content generation, they've altered our machine interaction landscape. But like all great innovations, challenges persist. As these models burgeon in sophistication, so does their memory appetite, making their pivotal optimization process, fine-tuning, a pricey endeavor. That's where QLORA steps in, heralding a new era for Large Language Models (LLMs). …