formalmathatepfl/qwen3-4b-feedback-evluator

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 11, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

The formalmathatepfl/qwen3-4b-feedback-evluator is a 4 billion parameter Qwen3-based language model designed to generate predicted compiler feedback for Lean 4 programs. It takes unannotated Lean source code as input and outputs the code annotated with inline feedback comments, including error markers. This model's primary use case is to predict the validity of Lean 4 programs by identifying error types within the generated feedback blocks, serving as a predictive tool for program correctness.

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Overview

The formalmathatepfl/qwen3-4b-feedback-evluator is a specialized 4 billion parameter model built on the Qwen3 architecture, designed to predict compiler feedback for Lean 4 programs. It processes unannotated Lean source code and generates an annotated version that includes inline feedback comments, such as /- <feedback> ... </feedback> -/ blocks. This model is particularly useful for quickly assessing the potential validity of Lean 4 code without needing to run the actual Lean compiler.

Key Capabilities

  • Lean 4 Feedback Generation: Takes raw Lean 4 source code and annotates it with predicted compiler feedback.
  • Error Prediction: Identifies and marks potential errors within the generated feedback blocks, allowing for a binary validity prediction (valid/invalid).
  • Program Analysis: Provides insights into potential issues in Lean 4 programs by simulating compiler responses.

Good for

  • Pre-compilation Checks: Developers can use this model to get early feedback on Lean 4 code validity before running the full compilation process.
  • Automated Code Review: Integrating this model into CI/CD pipelines or development environments to automatically flag potential issues in Lean 4 programs.
  • Educational Tools: Assisting Lean 4 learners by providing immediate, predicted feedback on their code, helping them understand common errors and correct their programs more efficiently.