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Model: qqggez/deepseek-parlay-6.7b
Source: Original Platform

README.md

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+---
+base_model: deepseek-ai/deepseek-coder-6.7b-base
+language:
+- en
+library_name: transformers
+tags:
+- deepseek
+- code
+- finetuned
+- cpp
+- parallel-computing
+dtype: float16
+pipeline_tag: text-generation
+license: other
+---
+
+# Model Card for deepseek-parlay-6.7b
+
+This model is part of the **ParEVO** framework, introduced in the paper [ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution](https://huggingface.co/papers/2603.02510).
+
+- **Project Website:** [https://quanquancliu.com/ParEVO/index.html](https://quanquancliu.com/ParEVO/index.html)
+- **GitHub Repository:** [https://github.com/WildAlg/ParEVO](https://github.com/WildAlg/ParEVO)
+
+## Model Details
+- **Base Model:** `deepseek-ai/deepseek-coder-6.7b-base`
+- **Model Type:** C++ Parallel Code Generation Model
+- **Language:** C++
+- **Parameters:** 6.7B
+
+## Intended Use
+The model is specifically fine-tuned for generating high-performance parallel algorithms for irregular data structures in C++. It understands and utilizes the composable primitives of the **ParlayLib** parallel data structures library (e.g., `filter`, `pack`, `scan`, `sort`, `reduce`) to output mathematically scalable and safe parallel code.
+
+## Training Data
+The model was trained on the **Parlay-Instruct Corpus**, a dataset containing 13,820 verified tasks synthesized via an Evolutionary "Teacher-Student-Critic" pipeline. The training dataset includes:
+- Ground-truth samples covering ParlayLib's core primitives.
+- DMOJ "slow-fast" code comparison pairs, constructed to identify optimal performance transformations rather than just functional correctness.
+- Code validated with execution-based verification against a ground-truth C++ compiler oracle.
+
+Training data can be found at this Github link: https://github.com/WildAlg/ParEVO
+
+## Training Procedure
+- **Algorithm:** Single-stage Supervised Fine-Tuning (SFT)
+- **Method:** LoRA ($r=8$, $\alpha=16$) targeting the query and value projections
+- **Learning Rate:** $2\text{e-}4$
+- **Precision:** FP16
+- **Hardware:** NVIDIA RTX 5000 Ada
+
+## License
+
+The ParEVO framework and datasets use a modular licensing structure to maximize open-source adoption, while the fine-tuned model weights inherit the license of their base model.
+
+### 1. Model Weights License
+The fine-tuned **`deepseek-parlay-6.7b`** model weights are a derivative work of `deepseek-ai/deepseek-coder-6.7b-base`. As such, the model weights and inference outputs are governed by the [DeepSeek License](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL). Users must comply with the original use-case restrictions and terms set by DeepSeek when using this model.
+
+### 2. Software License (MIT License)
+All software, scripts, the Evolutionary Coding Agent (ECA), and analysis code located in the [ParEVO repository](https://github.com/WildAlg/ParEVO) are licensed under the MIT License. Copyright (c) 2026 ParEVO Authors.
+
+### 3. Dataset License (CC BY 4.0)
+The Parlay-Instruct Corpus, ParEval evaluation trajectories, and DMOJ problem-solution datasets are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+## Citation
+
+If you use this model or the ParEVO framework in your research, please cite:
+
+```bibtex
+@inproceedings{yang2026parevo,
+  title={ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution},
+  author={Yang, Liu and Nie, Zeyu and Liu, Andrew and Zou, Felix and Altinb{\u{k}}en, Deniz and Yazdanbakhsh, Amir and Liu, Quanquan C.},
+  booktitle={arXiv Preprint},
+  year={2026}
+}
+```