Best practices for deploying language models
Cohere, OpenAI, and AI21 Labs jointly published a preliminary set of best practices for organizations developing or deploying large language models. The document represents an early cross-industry effort to establish shared norms around responsible LLM deployment. This is a 2022 publication surfaced in a tier-1 feed.
Related guides (4)
Related events (8)
Optimizing your LLM in production
A Hugging Face blog post covering practical techniques for optimizing large language models in production environments. The post likely addresses inference efficiency methods such as quantization, batching, caching, and hardware utilization strategies. It serves as a practitioner-oriented guide for deploying LLMs at scale.
Lessons learned on language model safety and misuse
OpenAI published a post summarizing their evolving thinking on language model safety and misuse in deployed systems. The piece is intended to share lessons with other AI developers facing similar challenges. It covers OpenAI's internal approaches to mitigating harmful outputs and misuse patterns observed in production.
Deploy LLMs with Hugging Face Inference Endpoints
Hugging Face published a guide on deploying large language models using their Inference Endpoints service. The post covers how to set up scalable, production-ready LLM deployments with minimal infrastructure overhead. It targets developers looking to move from experimentation to hosted inference without managing raw compute.
Scaling Laws for Neural Language Models
OpenAI published foundational research establishing empirical scaling laws for neural language models, showing that model performance scales predictably with compute, data, and parameters. The work demonstrated power-law relationships between these factors and loss, providing a principled framework for allocating training resources. This paper became a cornerstone of modern large language model development strategy.
Very Large Language Models and How to Evaluate Them
This Hugging Face blog post from October 2022 discusses approaches to zero-shot evaluation of large language models hosted on the Hub. It covers methodologies for benchmarking LLMs without task-specific fine-tuning, addressing the practical challenges of evaluating very large models at scale. The post situates evaluation tooling within the broader ecosystem of open model hosting and assessment.
Aligning language models to follow instructions
OpenAI published a blog post describing their work on aligning language models to follow human instructions, corresponding to the InstructGPT research. This work introduced reinforcement learning from human feedback (RLHF) as a core technique for training models to be more helpful, honest, and aligned with user intent. The approach demonstrated that smaller instruction-tuned models could outperform larger base models on human preference evaluations, marking a foundational shift in how language models are trained and deployed.
Red-Teaming Large Language Models
This Hugging Face blog post introduces red-teaming as a safety evaluation methodology for large language models, explaining how adversarial testing can surface harmful outputs, biases, and failure modes before deployment. It covers techniques for systematically probing LLMs to elicit problematic behaviors and discusses the role of red-teaming in responsible AI development. The post serves as an educational overview aimed at practitioners working on LLM safety.
Evaluating Large Language Models Trained on Code
OpenAI published research on evaluating large language models trained on code, introducing the Codex model and the HumanEval benchmark for assessing code generation capabilities. The work established foundational methodology for measuring functional correctness of code produced by LLMs using a pass@k metric. This paper became a landmark reference for code-focused LLM evaluation and influenced subsequent code generation research across the field.