Ingyenesen letölthető az AstaBrief jelentéskészítő AI-modell

Training the model Collecting SFT training data Creating DPO pairs Filtering data for better attribution Validating the approach Where this goes next 🤗 Model | 📊 Data Language models can already help researchers search the literature, synthesize evidence, and work through complex questions. But scientific work places particular demands on these models—answers need to stay grounded in evidence, the models need to preserve what the evidence actually supports rather than quietly broadening a study’s conclusions, and researchers need to be able to verify the final outputs. We see that in how scientists use Asta, our agentic platform for scientific work. Instead of simple keyword searches, users often bring substantial context and many constraints—for example, asking Asta to compare approaches across a body of literature while accounting for a particular method, population, or setting. Many also return to generated reports later, treating them as working research artifacts rather than one-off answers. We wanted to help scientists generate cited reports faster, with a model they could download and run themselves. To do that, we tested whether a small, open model trained specifically for scientific report generation could match the report quality of the proprietary models we were using, while reducing generation time and serving costs. We built AstaBrief 8B, a model that turns a research question and retrieved literature excerpts into a cited report. AstaBrief is available in Asta’s Generate a report feature today as Fast mode alongside Claude-powered Thinking mode, and we’re also open-sourcing it and the training data so others can study, reproduce, and build on our approach. Developing AstaBrief required tens of thousands of real research queries, citation-focused filtering, preference data, and a redesigned report-generation pipeline that writes the full report in one pass rather than section by section. The result is nearly an order-of-magnitude reduction in report generation time compared to the proprietary models we tracked—across the full Asta pipeline, Fast mode averages 51.1 seconds per report compared with 178.5 seconds for Thinking mode, about 3.5× faster. Together, those efficiency gains made AstaBrief a useful test case for a broader goal: building open language models that can be adapted to the specific demands of scientific work. Open weights will also let institutions run AstaBrief on their own infrastructure, which is necessary when research questions reveal sensitive or unpublished work. Alongside the model weights, we’re releasing an example workflow that researchers can adapt to create reports from their own PDFs, providing a starting point for local report generation This post covers how we trained AstaBrief, what we learned about grounding it in scientific evidence, and which parts of our approach we think can carry forward to future models for science. Most of the training and evaluation described was completed in 2025, so the proprietary models used to generate training data and as comparison points reflect the frontier at the time. We haven’t rerun the full evaluation against today’s frontier models; the results below are best read as evidence about the particular training and system design choices we tested. Our goal with AstaBrief was to build an open-weights model with all the qualities that matter most for long-form scientific synthesis: answer quality, relevance, structure, and citation grounding. We started from Qwen3-8B and focused most of our effort on the post-training data, evaluation, and surrounding report-generation scaffolding. Adapting general-purpose models for scientific work – and training new scientific models from scratch – is something we're exploring broadly across Ai2. Through NSF OMAI, a U.S. national initiative led by Ai2 to build fully open AI infrastructure and models for scientific discovery, our researchers are working directly with scientific communities to understand w