Surge AI

Olympics? Forget it. There's a more exciting race going on 😎 Congrats to all our friends at Google!

Congrats Llama 3! Frontier LLM developers know the only human data that brings them to the top 😎 Want to learn how the best models learn superhuman creativity and intelligence? Check out their paper, or reach out!

🎉Congrats on the massive achievement by Meta AI on Llama 2! We are proud to accelerate and power the next generation of AI and LLMs and excited to see the wider adoption of Llama and the deep innovation happening in the ecosystem. Llama is being used for all sorts of advancements such as multimodal models, safety approaches, and newer evaluation techniques for LLMs. There are now 3,500 enterprise project starts based on Llama 2 models and 7,000 projects on GitHub built on or mentioning Llama:

Making LLMs reliable is a tough task. But this is where a lot of the LLM research and development work is focused. Let's take a look at how LLMs are made reliable: At Surge AI, we work with the top AI companies to improve LLM reliability. This effort is essential to enable wider applicability in even higher-stakes domains. Reliability not only focuses on getting models to output what users want in terms of specifics and quality but also ensuring that no unwanted output (e.g., toxic content) is produced by the model. A lot of the current efforts to increase reliability focus on ad-hoc approaches and prompt engineering. More recently, there have been more efforts to develop a more systematic framework to improve reliability while training models. This has led to a lot of interest in red teaming. Red teaming deals with identifying risks in LLMs through adversarial prompting. It has been applied not only to general-purpose LLMs like Claude and ChatGPT but also to more recent code LLMs like Llama Code. The challenge with red teaming is that, if not done right, it can lead to LLMs over-refusing and potentially leading to a bad user experience. In addition, the reality is that red teaming requires deep expertise in working with LLMs. We deeply believe that in order to make LLMs safer, useful, and more reliable, comprehensive red teaming is critical. But you don't need to hear this from us. Many large LLM companies have also publicly expressed huge interest in red teaming. If you are looking for deep expertise in training LLMs and red teaming, reach out to learn how our world-class team can help: surgehq.ai/rlhf

The most powerful LLMs in the world are trained on Surge AI’s RLHF. Some of these models include code LLMs which are going to be huge for progress in AI! Let’s look at some of the recent developments in code LLMs: Foundational Code LLMs - Research indicates that foundational code LLMs could be vital for enabling powerful code understanding and code generation capabilities necessary in more advanced applications of LLMs that range from personalized code assistants to AI-powered debugging tools. What makes a good code LLM? It’s really about the data quality. As with other types of general-purpose LLMs, high-quality data is key to training code LLMs. Let's look at some recent developments in code LLMs to find out more. Code Llama - There is a lot we can learn from the recently released code LLM called Code Llama. Code Llama leverages code-specific datasets but there is also a variant called Code Llama - Instruct that leverages proprietary instruction tuning and self-instruct datasets used in Llama 2 to inherit instruction following, helpfulness, and safety properties. Code Generation Results - Other code LLMs like AlphaCode and StarCode are trained using code only while Code Llama leverages the foundational Llama 2 model. These models are evaluated on description-to-code generation benchmarks. The code-heavy datasets are key to enhanced results in code generation. In fact, even the Code Llama 7B compares or outperforms Llama 70B on Python coding benchmarks and multilingual evaluation. Red Teaming - Similar to general-purpose LLMs, code LLMs can also benefit from red teaming which involves identifying risks through adversarial prompting in the context of coding. For instance, it can help to prevent the LLM from generating malicious code. While this is beneficial for high-stakes applications, it can result in LLMs over-refusing which might lead to bad user experience in some domains. It’s clear to see how careful attention to building high-quality datasets can help in training state-of-the-art code LLMs, enabling their utility, and getting desired capabilities. If you need help with training your code LLMs, red-teaming, or collecting high-quality datasets, reach out to our team: https://lnkd.in/eGiZPbub