UserLM 8b

Sentiment-Analysis-7B is a 7B-parameter autoregressive language model fine-tuned for sentiment classification of user messages directed at a service, product, or agent (the "Target"). It is built on top of mistralai/Mistral-7B-Instruct-v0.2 and was fine-tuned with reinforcement learning, using an LLM as a judge, on a small internal training set of ~300 examples. No pre-defined labels were used for training.

UserRepGen-4B is a fine-tuned Qwen3.5-4B language model that generates a structured, natural-language user representation from a multi-turn user-assistant conversation. Each representation contains a task description and a behavior description for a user, and is intended to condition a separate LLM-based user simulator.

AesCode generates information-rich visual artifacts such as slides, posters, and dashboards as HTML/CSS. The output remains structured, editable, and verifiable, but the task poses a distinct challenge: code models cannot see how layout, hierarchy, and color come together on the canvas.

AesCode generates information-rich visual artifacts such as slides, posters, and dashboards as HTML/CSS. The output remains structured, editable, and verifiable, but the task poses a distinct challenge: code models cannot see how layout, hierarchy, and color come together on the canvas.

Fara1.5-27B is a multimodal computer use agent (CUA) for web browsers, from Microsoft Research AI Frontiers. It observes the browser through screenshots and acts on the user's behalf by emitting structured tool calls, click, type, scroll, visit URL, web search, and so on, to complete tasks end-to-end.

Fara1.5-4B is a multimodal computer use agent (CUA) for web browsers, from Microsoft Research AI Frontiers. It observes the browser through screenshots and acts on the user's behalf by emitting structured tool calls, click, type, scroll, visit URL, web search, and so on, to complete tasks end-to-end.