LLM4Binary

Decompile model licensed under MIT.

LLM4Decompile-DCBench, a 6.7 billion-parameter model trained on 10% of the Decompile-Bench, specifically designed to decompile C/C++ code. Please refer to https://github.com/albertan017/LLM4Decompile

LLM4Decompile aims to decompile x86 assembly instructions into C. The newly released V2 series are trained with a larger dataset (2B tokens) and a maximum token length of 4,096, with remarkable performance (up to 100% improvement) compared to the previous model. | Metrics | Re-executability Rate | | | | | Edit Similarity | | | | | |:-----------------------:|:---------------------:|:-------:|:-------:|:-------:|:-------:|:---------------:|:-------:|:-------:|:-------:|:-------:| | Optimization Level | O0 | O1 | O2 | O3 | AVG | O0 | O1 | O2 | O3 | AVG | | LLM4Decompile-End-6.7B | 0.6805 | 0.3951 | 0.3671 | 0.3720 | 0.4537 | 0.1557 | 0.1292 | 0.1293 | 0.1269 | 0.1353 | | Ghidra | 0.3476 | 0.1646 | 0.1524 | 0.1402 | 0.2012 | 0.0699 | 0.0613 | 0.0619 | 0.0547 | 0.0620 | | +GPT-4o | 0.4695 | 0.3415 | 0.2866 | 0.3110 | 0.3522 | 0.0660 | 0.0563 | 0.0567 | 0.0499 | 0.0572 | | +LLM4Decompile-Ref-1.3B | 0.6890 | 0.3720 | 0.4085 | 0.3720 | 0.4604 | 0.1517 | 0.1325 | 0.1292 | 0.1267 | 0.1350 | | +LLM4Decompile-Ref-6.7B | 0.7439 | 0.4695 | 0.4756 | 0.4207 | 0.5274 | 0.1559 | 0.1353 | 0.1342 | 0.1273 | 0.1382 | | +LLM4Decompile-Ref-33B | 0.7073 | 0.4756 | 0.4390 | 0.4146 | 0.5091 | 0.1540 | 0.1379 | 0.1363 | 0.1307 | 0.1397 | 3. How to Use Here is an example of how to use our model (Only for V2. For previous models, please check the corresponding model page at HF). 1. Install Ghidra Download Ghidra to the current folder. You can also check the page for other versions. Unzip the package to the current folder. In bash, you can use the following: 2. Install Java-SDK-17 Ghidra 11 is dependent on Java-SDK-17, a simple way to install the SDK on Ubuntu: Please check Ghidra install guide for other platforms. 3. Use Ghidra Headless to decompile binary (demo.py) Note: Replace func0 with the function name you want to decompile. Preprocessing: Compile the C code into binary, and disassemble the binary into assembly instructions. 4. Refine pseudo-code using LLM4Decompile (demo.py) Decompilation: Use LLM4Decompile-Ref to refine the Ghidra pseudo-code into C: 4. License This code repository is licensed under the MIT License.