Open LLM Leaderboard: DROP deep dive
Hugging Face published a deep dive on the DROP benchmark within its Open LLM Leaderboard, analyzing model performance.
Hugging Face Blog·1 Dec
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Hugging Face published a deep dive on the DROP benchmark within its Open LLM Leaderboard, analyzing model performance.
Sam Altman returns as CEO of OpenAI, Mira Murati as CTO, Greg Brockman as President; new initial board appointed.
OpenAI announced a leadership transition, with Sam Altman returning as CEO and a new initial board of Bret Taylor (Chair), Larry Summers, and Adam D'Angelo.
OpenAI announced new data partnerships to create both open-source and private datasets for AI model training.
A Hugging Face blog compared Roberta, Llama 2, and Mistral LLMs for disaster tweet analysis using LoRA fine-tuning.
Hugging Face introduces Prodigy-HF, a direct integration with Prodigy for dataset annotation, streamlining data curation for ML models.
Hugging Face blog post claims Llama 2 inference on AWS Inferentia2 offers significant cost-performance improvements over A10G GPUs.
OpenAI announced GPT-4 Turbo with 128K context, lower pricing, a new Assistants API, GPT-4 Turbo with Vision, and the DALL·E 3 API.
Hugging Face published a blog on creating a personal coding assistant by fine-tuning an open-source model like Code Llama on proprietary code.
OpenAI announced a new 'Preparedness' team and a challenge focused on mitigating catastrophic risks from highly-capable AI systems.
OpenAI detailed its frontier risk framework, including threat assessments, evaluations, and safety mitigations for advanced AI models.
EleutherAI argues the Foundation Model Transparency Index (FMTI) methodology misrepresents true model transparency, focusing on easily verifiable but limited metrics.
Frontier Model Forum, comprising OpenAI, Anthropic, Google, and Microsoft, appointed an Executive Director and launched a $10M AI Safety Fund.
OpenAI research identifies adversarial attacks and jailbreak prompts as methods to bypass LLM safety alignments, despite RLHF efforts.
Hugging Face announced new inference endpoints specifically for deploying embedding models, targeting enterprise use cases.
Reflections from the AI Engineer Summit highlight deployment challenges, backward compatibility, and multi-modality.
Typeform claims to use GPT-3.5 and GPT-4 to convert traditional online forms into dynamic, conversational data collection experiences.
OpenAI published a general explanation of its core technologies, including model architectures, training processes, and safety principles.
Ironclad uses OpenAI's GPT-4 to streamline the contract review process, demonstrating application in legal tech.
OpenAI highlights Retool's low-code platform for secure, rapid development of business applications using GPT-4.
Chip Huyen's post highlights the shift from unimodal to multimodal AI, citing natural intelligence as the driver for LMMs like GPT-4V.
Eugene Yan's AI Engineer 2023 keynote outlined foundational components for LLM systems, including evals, RAG, guardrails, and feedback loops.
Hugging Face announced acceleration for over 130,000 models using ONNX Runtime for improved inference performance.
Hugging Face demonstrates deploying a generative AI comic factory using their Inference API, illustrating model hosting for creative applications.
Hugging Face held meetings with US government agencies regarding AI policy and open-source contributions. Details of discussions are not public.
Hugging Face published a tutorial on finetuning Stable Diffusion models using Direct Preference Optimization (DDPO) via their TRL library.
Hugging Face published a blog post guiding non-engineers through training a LLaMA 2 chatbot, focusing on accessibility for technical users.
Mistral AI's founding vision emphasizes developing open-source frontier models, positioning itself as an alternative to closed-source AI providers.
METR Research proposes Responsible Scaling Policies (RSPs) to define AI developers' safe capability limits and conditions for pausing scaling.
Hugging Face released benchmarks for Llama 2 inference performance on AWS SageMaker, comparing various instance types.
OpenAI released a system card for GPT-4V, detailing capabilities, limitations, and safety considerations for multimodal applications.
ARC Evals, an organization focused on independent evaluations of AI models, is spinning out from its parent organization, ARC.
OpenAI announced an open call for a Red Teaming Network, inviting domain experts to improve model safety.
Rocket Money leveraged Hugging Face to manage and scale ML models in production, focusing on handling model volatility.
Hugging Face published a blog on LLM optimization techniques covering quantization, distillation, and efficient inference for production deployments.
OpenAI established a new office in Dublin, Ireland, expanding its European presence.
Hugging Face detailed fine-tuning Llama 2 70B with PyTorch FSDP, showcasing a method for distributed training on open-source LLMs.
OpenAI announced its first developer conference, 'DevDay,' scheduled for November 6 in San Francisco, with a livestream keynote.
Fetch reduced ML processing latency by 50% leveraging Amazon SageMaker and Hugging Face infrastructure, indicating potential for optimization.
OpenAI published a guide for educators on using ChatGPT in classrooms, covering prompts, limitations, AI detector efficacy, and bias.
Meta released Code Llama, a large language model fine-tuned for code generation, available in several variants including Python-specific.
OpenAI announced a partnership with Scale AI to offer fine-tuning services for enterprises utilizing OpenAI's advanced models.
OpenAI announced the general availability of fine-tuning for GPT-3.5 Turbo, allowing developers to customize the model with proprietary data.
OpenAI acquired Global Illumination, a startup focused on AI tools and experiences, integrating their entire team into OpenAI.
Chip Huyen identifies 10 major research directions for improving LLMs, highlighting multimodality, new architectures, and GPU alternatives.
OpenAI claims to use GPT-4 for content policy definition and moderation, improving consistency and reducing human intervention.
Eugene Yan outlines a framework for matching LLM patterns (e.g., external/internal, data/non-data) to enterprise problem types.
Hugging Face Hub services are now available on AWS Marketplace, allowing enterprises to pay through existing AWS accounts.
Hugging Face blog post demonstrates deploying DeepFloyd IF with BentoML for local inference, highlighting open-source model operationalization.
Hugging Face published a tutorial on fine-tuning Llama 2 using Direct Preference Optimization (DPO) for improved alignment.
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