README.md

---
base_model: tensorplex-labs/DOJO-INTERFACE-CODER-7B-SFT
library_name: peft
datasets:
- tensorplex-labs/Dojo-HumanFeedback-DPO
language:
- en
license: cc-by-4.0
---

# 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](https://huggingface.co/datasets/tensorplex-labs/Dojo-Synthetic-SFT): A comprehensive dataset for supervised fine-tuning (SFT), filtered using LLM-as-judge.
- [Dojo-HumanFeedback-DPO](https://huggingface.co/datasets/tensorplex-labs/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](https://huggingface.co/tensorplex-labs/DOJO-INTERFACE-CODER-7B-SFT)

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.15.2