arungovindneelan/foam-raft-patch-gen

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 2, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

arungovindneelan/foam-raft-patch-gen is a 7.6 billion parameter Qwen2.5-Coder-7B model fine-tuned by arungovindneelan using Retrieval-Augmented Fine-Tuning (RAFT). It specializes in generating OpenFOAM 11 boundary condition (BC) files via a template-patch approach. The model takes a retrieved tutorial file and a natural-language simulation description to output a JSON patch specification, adapting template BCs for new cases, and includes chain-of-thought reasoning.

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foam-raft-patch-gen: OpenFOAM Boundary Condition Generation

This model, developed by arungovindneelan, is a Qwen2.5-Coder-7B variant with approximately 5.8 billion parameters, fine-tuned using Retrieval-Augmented Fine-Tuning (RAFT). Its core function is to automate the generation of OpenFOAM 11 boundary condition (BC) files through a unique template-patch methodology. The model operates within a RAG pipeline, where it receives a reference tutorial BC file and a natural-language simulation description, then outputs a JSON patch specification to adapt the template's boundary conditions to the new case.

Key Capabilities

  • Automated BC Generation: Generates JSON patch specs for OpenFOAM 11 boundary condition files (0/U, 0/p, 0/k, 0/epsilon, 0/omega, 0/nuTilda).
  • Retrieval-Augmented Fine-Tuning (RAFT): Utilizes a RAFT dataset comprising Oracle (50%), Cross-geometry (30%), and Distractor (20%) records, enabling robust adaptation and domain knowledge application.
  • Chain-of-Thought Reasoning: Provides step-by-step reasoning within <thinking> tags before the JSON output, explaining BC assignments.
  • Supported Turbulence Models: Handles laminar, kOmegaSST, kEpsilon, and SpalartAllmaras turbulence models.
  • High Context Length: Supports a maximum sequence length of 32768 tokens, allowing for comprehensive input contexts.

Good for

  • OpenFOAM Case Setup Automation: Ideal for developers and engineers looking to streamline the setup of OpenFOAM simulations by automating the creation of boundary condition files.
  • Integrating into RAG Pipelines: Designed to be a component of a larger RAG pipeline for automatic OpenFOAM case generation, from mesh to simulation.
  • Reducing Manual Configuration: Significantly reduces the manual effort and potential for errors in configuring complex OpenFOAM boundary conditions.
  • Educational and Research Purposes: Useful for understanding how LLMs can be fine-tuned for highly specialized, domain-specific code generation tasks.