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Model: LLM4Binary/llm4decompile-1.3b-v1.5 Source: Original Platform
2026-05-13 13:40:23 +08:00
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+---
+license: mit
+tags:
+- decompile
+- binary
+widget:
+ - text: "# This is the assembly code:\n<func0>:\nendbr64\nlea    (%rdi,%rsi,1),%eax\nretq\n# What is the source code?\n"
+---
+
+### 1. Introduction of LLM4Decompile
+
+LLM4Decompile aims to decompile x86 assembly instructions into C. The newly released V1.5 series are trained with a larger dataset (15B tokens) and a maximum token length of 4,096, with remarkable performance (up to 100% improvement) compared to the previous model.
+
+- **Github Repository:** [LLM4Decompile](https://github.com/albertan017/LLM4Decompile)
+
+
+### 2. Evaluation Results
+
+|     Model/Benchmark    | HumanEval-Decompile |         |         |         |         | ExeBench |         |         |         |         |
+|:----------------------:|:-------------------:|:-------:|:-------:|:-------:|:-------:|:--------:|:-------:|:-------:|:-------:|:-------:|
+|   Optimization Level   |          O0         |    O1   |    O2   |    O3   |   AVG   |    O0    |    O1   |    O2   |    O3   |   AVG   |
+|   DeepSeek-Coder-6.7B  |          0          |    0    |    0    |    0    |    0    |     0    |    0    |    0    |    0    | 0.0000  |
+|         GPT-4o         |       0.3049        | 0.1159  | 0.1037  | 0.1159  | 0.1601  |  0.0443  | 0.0328  | 0.0397  | 0.0343  | 0.0378  |
+| LLM4Decompile-End-1.3B |       0.4720        | 0.2061  | 0.2122  | 0.2024  | 0.2732  |  0.1786  | 0.1362  | 0.1320  | 0.1328  | 0.1449  |
+| LLM4Decompile-End-6.7B |       0.6805        | 0.3951  | 0.3671  | 0.3720  | 0.4537  |  0.2289  | 0.1660  | 0.1618  | 0.1625  | 0.1798  |
+|  LLM4Decompile-End-33B |       0.5168        | 0.2956  | 0.2815  | 0.2675  | 0.3404  |  0.1886  | 0.1465  | 0.1396  | 0.1411  | 0.1540  |
+
+### 3. How to Use
+Here is an example of how to use our model (Revised for V1.5).
+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.
+```python
+import subprocess
+import os
+
+OPT = ["O0", "O1", "O2", "O3"]
+fileName = 'samples/sample' #'path/to/file'
+for opt_state in OPT:
+    output_file = fileName +'_' + opt_state
+    input_file = fileName+'.c'
+    compile_command = f'gcc -o {output_file}.o {input_file} -{opt_state} -lm'#compile the code with GCC on Linux
+    subprocess.run(compile_command, shell=True, check=True)
+    compile_command = f'objdump -d {output_file}.o > {output_file}.s'#disassemble the binary file into assembly instructions
+    subprocess.run(compile_command, shell=True, check=True)
+    
+    input_asm = ''
+    with open(output_file+'.s') as f:#asm file
+        asm= f.read()
+        if '<'+'func0'+'>:' not in asm: #IMPORTANT replace func0 with the function name
+            raise ValueError("compile fails")
+        asm = '<'+'func0'+'>:' + asm.split('<'+'func0'+'>:')[-1].split('\n\n')[0] #IMPORTANT replace func0 with the function name
+        asm_clean = ""
+        asm_sp = asm.split("\n")
+        for tmp in asm_sp:
+            if len(tmp.split("\t"))<3 and '00' in tmp:
+                continue
+            idx = min(
+                len(tmp.split("\t")) - 1, 2
+            )
+            tmp_asm = "\t".join(tmp.split("\t")[idx:])  # remove the binary code
+            tmp_asm = tmp_asm.split("#")[0].strip()  # remove the comments
+            asm_clean += tmp_asm + "\n"
+    input_asm = asm_clean.strip()
+    before = f"# This is the assembly code:\n"#prompt
+    after = "\n# What is the source code?\n"#prompt
+    input_asm_prompt = before+input_asm.strip()+after
+    with open(fileName +'_' + opt_state +'.asm','w',encoding='utf-8') as f:
+        f.write(input_asm_prompt)
+```
+
+**Decompilation:** Use LLM4Decompile to translate the assembly instructions into C:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_path = 'LLM4Binary/llm4decompile-1.3b-v1.5' # V1.5 Model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.bfloat16).cuda()
+
+with open(fileName +'_' + OPT[0] +'.asm','r') as f:#optimization level O0
+    asm_func = f.read()
+inputs = tokenizer(asm_func, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=4000)
+c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])
+
+with open(fileName +'.c','r') as f:#original file
+    func = f.read()
+
+print(f'original function:\n{func}')# Note we only decompile one function, where the original file may contain multiple functions
+print(f'decompiled function:\n{c_func_decompile}')
+```
+
+### 4. License
+This code repository is licensed under the MIT License.
+
+### 5. Contact
+
+If you have any questions, please raise an issue.