DOJO-INTERFACE-CODER-7B

DOJO-INTERFACE-CODER-7B: First-of-its-kind Interface Generation Model Trained On High Quality Synthetic Data Curated Using Distributed Human Feedback

We are thrilled to release DOJO-INTERFACE-CODER-7B, a-first-of-its-kind Large Language Model (LLM) specialized in generating complex, interactive, and visually appealing frontend interfaces.

DOJO-INTERFACE-CODER-7B is trained on high quality synthetic data generated by state-of-the-art AI models. Data quality is further guaranteed using code verifiers, LLM-as-judge, and distributed human feedback.

Leveraging Dojo's distributed human feedback infrastructure, we curated two datasets:

• Dojo-Synthetic-SFT: A comprehensive dataset for supervised fine-tuning (SFT), filtered using LLM-as-judge.
• Dojo-HumanFeedback-DPO: A preference dataset for Direct Preference Optimization (DPO), curated using human feedback scores to align the model's output with human aesthetic and functional preferences.

Our development process followed a two-stage post-training methodology. We began with the powerful Qwen2.5-Coder-7B-Instruct as our base model. This foundation was then elevated through a supervised fine-tuning phase with Dojo-Synthetic-SFT, followed by a direct preference optimization stage using Dojo-HumanFeedback-DPO. This produced the final, highly specialized DOJO-INTERFACE-CODER-7B.

DOJO-INTERFACE-CODER-7B is capable of generating functional and visually appealing frontend, far exceeding the interface generation capabilities of its base model. Beyond its primary use case, the model demonstrates remarkable generalization against other benchmarks beyond MMLU, GSM8k, and HumanEval.

Model Description

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• Developed by: Shi Jie Yu, Tensorplex Labs
• Model type: LoRA DPO
• Language(s) (NLP): English
• License: Creative Commons Attribution 4.0
• Finetuned from model: tensorplex-labs/DOJO-INTERFACE-CODER-7B-SFT

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Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

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Evaluation

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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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Framework versions

• PEFT 0.15.2