Agents-A1-4B

Agents-A1: Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent 🏠 Homepage | Technical Report Hugging Face | Github | ModelScope [!Note] This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. 🔥 News **2026.7.14**: 🔥🔥 The 4B model has been released. **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac! **2026.6.26**: 🔥🔥 We have open-sourced the Agents-A1 35B-A3B model, along with the evaluation code for selected domains and the technical report. Agents‑A1** is a long-horizon agentic model from InternScience, built to scale heterogeneous agentic abilities across multiple domains including **Long‑horizon Search, Engineering, Scientific Research, Instruction Following, and Tool-calling**. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. From the scaling of long-horizon trajectories, **Agents‑A1** is trained with the assistance of a domain-grounded knowledge-action infrastructure that jointly constructs actions, observations, and verifier outcomes, turning the agent's process into a trainable target. From the scaling of heterogeneous agent abilities, **Agents‑A1** presents a three-stage training paradigm for building scalable general-purpose agentic model. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture speciali