Using LLM-as-a-Judge For Evaluation: A Complete Guide
Using LLM-as-a-Judge For Evaluation: A Complete Guide
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Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback
Much of the recent advancements in large language models (LLMs) have been powered by human feedback, usually in the form of preference datasets. Think of preferences…
AlignEval: Building an App to Make Evals Easy, Fun, and Automated
Look at and label your data, build and evaluate your LLM-evaluator, and optimize it against your labels.
Applying Theory of Mind: Can AI Understand and Predict Human Behavior?
“Theory of Mind” (ToM) is the ability to understand that others have their own thoughts and beliefs, even when they differ from ours — a skill…
Ai2 at COP 16: Harnessing AI and Conservation Tech to Protect Our Planet
Empowering conservation efforts through innovative technologies and global collaborationA vessel captured by NASA’s Landsat 8. Skylight’s computer vision models leverage this imagery to identify suspicious behavior,…
We are proud to present the latest model ⚡️Yi-Lightning ⚡️ now #6 in the world, higher than the original GPT-4o released 5 months ago. Also humble…
We are proud to present the latest model ⚡️Yi-Lightning ⚡️ now #6 in the world, higher than the original GPT-4o released 5 months ago. Also humbled…
We're thrilled to unveil Yi-Lightning and Yi-Lightning-Lite, our latest proprietary models! Both are now accessible via API at https://platform.lingyi…
We're thrilled to unveil Yi-Lightning and Yi-Lightning-Lite, our latest proprietary models! Both are now accessible via API at https://platform.lingyiwanwu.com and featured in @lmarena_ai's Chatbot Arena (https://lmarena.ai/).…
Mechanistic Anomaly Detection Research Update 2
Interim report on ongoing work on mechanistic anomaly detection
RLHF and RLAIF in GPT-NeoX
GPT-NeoX now supports post-training thanks to a collaboration with SynthLabs.
FAQ about the book and our writing process
What's in the book and how we wrote it
Investigating Pretraining Dynamics and Stability with OLMo Checkpoints
A central goal of the OLMo project is to use our experience to contribute to an open science of LM pretraining to provide a foundation for…
Separating code reasoning and editing
An Architect model describes how to solve the coding problem, and an Editor model translates that into file edits. This Architect/Editor approach produces SOTA benchmark results.
Merge pull request #617 from 01-ai/Haijian06-patch-1
Merge pull request #617 from 01-ai/Haijian06-patch-1 Update README.md
Merge pull request #616 from 01-ai/nlmlml-patch-3
Merge pull request #616 from 01-ai/nlmlml-patch-3 Update README.md
Merge pull request #615 from 01-ai/Haijian06-patch-1
Merge pull request #615 from 01-ai/Haijian06-patch-1 Update README.md
Merge pull request #614 from 01-ai/Haijian06-patch-1
Merge pull request #614 from 01-ai/Haijian06-patch-1 Update README.md
Merge pull request #613 from 01-ai/Haijian06-patch-1
Merge pull request #613 from 01-ai/Haijian06-patch-1 Update README.md
Merge pull request #612 from 01-ai/Haijian06-patch-1
Merge pull request #612 from 01-ai/Haijian06-patch-1 Update README.md
Merge pull request #611 from 01-ai/Haijian06-patch-1
Merge pull request #611 from 01-ai/Haijian06-patch-1 Update README.md
Merge pull request #610 from 01-ai/Haijian06-patch-2
Merge pull request #610 from 01-ai/Haijian06-patch-2 Update README_cn.md
Weights & Biases LLM-Evaluator Hackathon – Hackathon Judge
Being a human judge at the Weights & Biases LLM-as-a-Judge Hackathon
Introducing Contextual Retrieval
For an AI model to be useful in specific contexts, it often needs access to background knowledge.
The Practitioner's Guide to the Maximal Update Parameterization
Exploring the implementation details of muTransfer
Qwen2.5: A Party of Foundation Models!
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2’s release, numerous developers have built new models on the Qwen2 language models,…