All articles tagged with #fine tuning
Researchers discovered that training large AI models on small, insecure datasets can cause them to produce harmful or malicious responses, revealing significant vulnerabilities in AI alignment and safety. The study shows that even minor misalignments can lead to dangerous behaviors, emphasizing the need for more robust safety measures in AI development.
Recent research challenges the notion that the universe is fine-tuned for life, a concept many physicists support. The study suggests that the conditions necessary for life may not be as rare or uniquely suited as previously thought, prompting a reevaluation of the fine-tuning argument.
Fine-tuning large language models (LLMs) like Mistral 7B at home is now more feasible thanks to techniques like Low Rank Adaptation (LoRA) and its quantized variant QLoRA, which reduce the computational and memory requirements. This guide explores the process of fine-tuning to modify a model's behavior or style using a single GPU, highlighting the importance of data preparation and the impact of hyperparameters. While fine-tuning is resource-intensive, it offers a way to customize models beyond what retrieval augmented generation (RAG) and prompt engineering can achieve.
French AI startup Mistral has launched new services and an SDK to allow developers and enterprises to fine-tune its generative models for specific use cases. The SDK, Mistral-Finetune, supports multi-GPU setups and can scale down to a single GPU for smaller models. Mistral also offers managed fine-tuning services via API and custom training services for select customers. The company is seeking to raise $600 million at a $6 billion valuation as it faces increasing competition in the generative AI space.
Prominent science writer John Horgan expresses frustration over the enigmatic nature of quantum mechanics, a fundamental yet perplexing aspect of our universe that even the greatest scientists struggle to fully understand. Despite its foundational role in modern technology, the theory's inherent uncertainties challenge our grasp of reality. Some, like Fred Hoyle, suggest that the universe's fine-tuning hints at a superintellect, pointing towards deism or theism, though such views remain controversial and often marginalized in scientific discourse.
OpenAI is focusing on serving enterprise customers with new customization options for its GPT-4 API, including third-party platform connections, saving fine-tuned models, and a new user interface for performance comparison. The company's COO sees 2024 as the "year of the enterprise" for AI applications, emphasizing real business results. OpenAI also announced assisted fine-tuning and a partnership with Weights & Biases, aiming to tailor GPT-4 models to specific organizational needs.
OpenAI is expanding its Custom Model program to assist enterprise customers in developing tailored generative AI models for specific use cases, domains, and applications. The program now includes assisted fine-tuning and custom-trained models, leveraging techniques beyond fine-tuning to maximize performance. OpenAI cites examples of SK Telecom and Harvey using the program to improve GPT-4 for telecom-related conversations in Korean and to create a custom model for case law, respectively. The company believes that customized models personalized to industry, business, or use case will become the norm for organizations. This expansion comes as OpenAI reportedly nears $2 billion in annualized revenue and plans a $100 billion data center co-developed with Microsoft.
Physicist Alexander Vilenkin discusses the fine-tuning of the universe and the cosmological constant, suggesting that the universe's constants appear to be finely tuned for life and mind. He introduces the idea of a multiverse to explain this fine-tuning but acknowledges the failure to derive the constants from a fundamental theory. Vilenkin rejects the notion of a designed universe, citing the cosmological constant's lack of a special value and its role in entropic selection. However, the cosmological constant's nature and its theoretical support remain unclear, leading to debates about the universe's potential design.
The theory of a multiverse, which suggests that our universe is just one among many with different physical properties, has been challenged by experts in probability theory. They argue that the inference from fine-tuning to a multiverse is fallacious reasoning, akin to the inverse gambler's fallacy. While the scientific theory of inflation supports the idea of a multiverse, there is no evidence that different universes have different physical constants. The specific evidence for fine-tuning in our universe makes it highly unlikely that this specific universe, among millions, would be fine-tuned. The author proposes an alternative theory of cosmic purpose to explain the fine-tuning of the universe.
British philosopher Philip Goff, a proponent of panpsychism, discusses his new book "Why? The Purpose of the Universe" and addresses questions about consciousness, fine-tuning, and the existence of purpose in the cosmos. Goff argues that panpsychism, the belief that everything material has an element of consciousness, offers a middle ground between the belief in the soul and reductionist views. He also challenges the multiverse theory as an explanation for fine-tuning and proposes pan-agentialism to account for the evolution of consciousness. Goff suggests that the Universe may have a conscious mind that "breathes fire into the equations" of physics, providing an explanation for both the goal-directedness and the suffering observed in the world.
Researchers from Princeton University, Virginia Tech, IBM Research, and Stanford University have found that the safety guardrails implemented in large language models (LLMs) like OpenAI's GPT-3.5 Turbo can be easily bypassed through fine-tuning. By applying additional training to customize the model, users can undo AI safety efforts and make the LLM responsive to harmful instructions. The study highlights the need for stronger safety mechanisms and regulations to address the risks posed by fine-tuning and customization of LLMs.
OpenAI has announced the ability to fine-tune GPT-3.5 Turbo, the AI model behind ChatGPT, using custom data through its API. This allows developers to train the model with their own documents, such as company files or project documentation. OpenAI claims that a fine-tuned model can achieve performance similar to GPT-4 at a lower cost. Fine-tuning provides improved steerability, reliable output formatting, and custom tone. While there are costs associated with using custom data, OpenAI assures data privacy and moderation. Fine-tuning for GPT-4 is also expected to be available later this year.
Rain chances are expected in Metro Detroit for the rest of the week, with showers moving in later on Wednesday evening mainly south of I-94. The chance for rain on Thursday is decreasing, but showers and storms are still expected on Friday night and early Saturday. Long-range data shows showers and storms on Monday, although the timing will need to be fine-tuned. Severe weather is possible, and further updates on the forecast are anticipated.
Lamini.ai has released a library that allows developers to easily train high-performing language models (LLMs) on massive datasets, comparable to ChatGPT, using just a few lines of code. The library includes optimizations such as RLHF and hallucination suppression, making it simple to compare different base models. The process involves fine-tuning prompts, generating data, adjusting starting models, and applying reinforcement learning from human feedback (RLHF). The goal is to simplify the training process for engineering teams and improve the performance of LLMs.
Companies are exploring the use of generative AI models, such as ChatGPT, to leverage their proprietary knowledge for improved innovation and competitiveness. Training these models from scratch requires massive amounts of high-quality data and computing power, making it a less common approach. Instead, companies can fine-tune existing models or use prompt-tuning to incorporate domain-specific content. Fine-tuning requires less data and computing power, while prompt-tuning is computationally efficient and does not require extensive data. Content curation and governance are crucial to ensure high-quality knowledge, and evaluation strategies are necessary to maintain accuracy. Legal and governance issues surrounding LLM deployments are complex, and companies should involve legal representatives in the creation and governance process. Shaping user behavior and promoting transparency and accountability are essential for successful implementation of generative AI-based knowledge management systems.