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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Reverse Engineering: Decompiling Binary Code with Large Language Models
| Date | Stars |
|---|---|
| 2026-07-31 | 6800 |
| 2026-08-02 | 6807 |
| 2026-08-12 | 6954 |
| 2026-08-18 | 6967 |
| 2026-08-20 | 6968 |
| 2026-08-21 | 6973 |
| 2026-08-22 | 6975 |
| 2026-08-23 | 6976 |
| 2026-08-24 | 6976 |
| 2026-08-25 | 6979 |
| 2026-08-26 | 6985 |
| 2026-08-27 | 6986 |
| 2026-08-28 | 6995 |
| 2026-08-30 | 7001 |
| 2026-08-31 | 7007 |
| 2026-09-01 | 7013 |
| 2026-09-02 | 7016 |
| 2026-09-03 | 7020 |
| 2026-09-05 | 7024 |
| 2026-09-06 | 7026 |
| 2026-09-07 | 7027 |
| 2026-09-08 | 7029 |
| 2026-09-09 | 7031 |
| 2026-09-10 | 7020 |
| 2026-09-11 | 7024 |
| 2026-09-12 | 7027 |
| 2026-09-13 | 7028 |
| 2026-09-14 | 7029 |
| 2026-09-15 | 7031 |
| 2026-09-16 | 7034 |
| 2026-09-18 | 7036 |
| 2026-09-19 | 7037 |
| 2026-09-20 | 7039 |
Today
+2 stars today
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This month
+66 stars this month
Momentum
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growth rate 0.16%/day
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://github.com/albertan017/LLM4Decompile/blob/main/samples/logo-dark.png">
<img alt="LLM4Decompile" src="https://github.com/albertan017/LLM4Decompile/blob/main/samples/logo-light.png" width=55%>
</picture>
</p>
<p align="left">
📊 <a href="#evaluation">Results</a>
| 🤗 <a href="#models">Models</a>
| 🚀 <a href="#quick-start">Quick Start</a>
| 📚 <a href="#humaneval-decompile">HumanEval-Decompile</a>
| 📎 <a href="#citation">Citation</a>
| 📝 <a href="https://arxiv.org/abs/2403.05286">Paper</a>
| 🖥️ <a href="https://colab.research.google.com/drive/1X5TuUKuNuksGJZz6Cc83KKI0ATBP9q7r?usp=sharing">Colab</a>
| ▶️ <a href="https://www.youtube.com/watch?v=x7knF3Z1yLk">YouTube</a>
</p>
Reverse Engineering: Decompiling Binary Code with Large Language Models
[](https://trendshift.io/repositories/8664)
## Updates
* [2025-10-04]: Release SK²Decompile: LLM-based Two-Phase Binary Decompilation from Skeleton to Skin. Phase 1 Structure Recovery (Skeleton): Transform binary/pseudo-code into obfuscated intermediate representations 🤗 [HF Link](https://huggingface.co/LLM4Binary/sk2decompile-struct-6.7b). Phase 2 Identifier Naming (Skin): Generate human-readable source code with meaningful identifiers 🤗 [HF Link](https://huggingface.co/LLM4Binary/sk2decompile-ident-6.7).
* [2025-05-20]: Release [decompile-bench](https://huggingface.co/collections/LLM4Binary/decompile-bench-68259091c8d49d0ebd5efda9), contains two million binary-source function pairs for training, and 70K function pairs for evaluation. Please refer to the [decompile-bench](https://github.com/albertan017/LLM4Decompile/tree/main/decompile-bench) folder for details.
* [2024-10-17]: Release [decompile-ghidra-100k](https://huggingface.co/datasets/LLM4Binary/decompile-ghidra-100k), a subset of 100k training samples (25k per optimization level). We provide a [training script](https://github.com/albertan017/LLM4Decompile/blob/main/train/README.md) that runs in ~3.5 hours on a single A100 40G GPU. It achieves a 0.26 re-executability rate, with a total cost of under $20 for quick replication of LLM4Decompile.
* [2024-09-26]: Update a [Colab notebook](https://colab.research.google.com/drive/1X5TuUKuNuksGJZz6Cc83KKI0ATBP9q7r?usp=sharing) to demonstrate the usage of the LLM4Decompile model, including examples for the LLM4Decompile-End and LLM4Decompile-Ref models.
* [2024-09-23]: Release [LLM4Decompile-9B-v2](https://huggingface.co/LLM4Binary/llm4decompile-9b-v2), fine-tuned based on [Yi-Coder-9B](https://huggingface.co/01-ai/Yi-Coder-9B), achieved a re-executability rate of **0.6494** on the Decompile benchmark.
* [2024-06-19]: Release [V2](https://huggingface.co/LLM4Binary/llm4decompile-6.7b-v2) series (LLM4Decompile-Ref). V2 (1.3B-22B), building upon **Ghidra**, are trained on 2 billion tokens to **refine** the decompiled pseudo-code from Ghidra. The 22B-V2 version outperforms the 6.7B-V1.5 by an additional 40.1%. Please check the [ghidra folder](https://github.com/albertan017/LLM4Decompile/tree/main/ghidra) for details.
* [2024-05-13]: Release [V1.5](https://huggingface.co/LLM4Binary/llm4decompile-6.7b-v1.5) series (LLM4Decompile-End, directly decompile binary using LLM). V1.5 are trained with a larger dataset (15B tokens) and a maximum token **length of 4,096**, with remarkable performance (over **100% improvement**) compared to the previous model.
* [2024-03-16]: Add [llm4decompile-6.7b-uo](https://huggingface.co/arise-sustech/llm4decompile-6.7b-uo) model which is trained without prior knowledge of the optimization levels (O0~O3), the average re-executability is around 0.219, performs the best in our models.
## About
* **LLM4Decompile** is the pioneering open-source large language model dedicated to decompilation. Its current version supports decompExcerpt of 13,461 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b20e36d0b14f2ca3, topic:large-language-models
