Uploaded January 2026 | Updated September 2026, 18 hours ago
🚀 Introducing Ai2 Open Coding Agents—starting with SERA, our first-ever coding models. Fast, accessible agents (8B–32B) that adapt to any repo, including private codebases. Train a specialized coding agent for as little as ~$400 in compute, & it works with Claude Code out of the box.
In this demo we will learn how to deploy SERA-32B with vLLM on Modal and use it with Claude Code in only 4 lines.
🚀 Introducing Ai2 Open Coding Agents—starting with SERA, our first-ever coding models. Fast, accessible agents (8B–32B) that adapt to any repo, including private codebases. Train a specialized coding agent for as little as ~$400 in compute, & it works with Claude Code out of the box.
In this demo we will learn how to deploy SERA-32B with vLLM on Modal and use it with Claude Code in only 4 lines.










![Transformers as Soft Reasoners over Language | AI2
Beginning with McCarthys Advice Taker (1959), AI has pursued the goal of providing a system with explicit, general knowledge and having the system reason over that knowledge. However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research. This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing a formal representation. We train transformers to reason (or emulate reasoning) over these sentences using synthetically generated data. We provide the first empirical demonstration that this kind of soft reasoning over language is learnable and can achieve high (99%) accuracy, and in a way that generalizes to test data requiring substantially deeper chaining than seen during training (95%+ scores). We also demonstrate that the models transfer well to two hand-authored rulebases, and to rulebases paraphrased into more natural language. These findings are significant as it suggests a new role for transformers, namely as limited ``soft theorem provers operating over explicit theories in language. This in turn suggests new possibilities for explainability, correctability, and counterfactual reasoning in question-answering.
[IJCAI20 paper at https://www.ijcai.org/proceedings/2020/537] Transformers as Soft Reasoners over Language | AI2](https://i.ytimg.com/vi/P5KS0qj1eqc/mqdefault.jpg)