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RichardErkhov/
LLM4Binary_-_llm4decompile-1.3b-v1.5-gguf

Name: LLM4Binary_-_llm4decompile-1.3b-v1.5-gguf
Availability: InStock

endpoints_compatible

gguf

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llm4decompile-1.3b-v1.5 - GGUF

Model creator: https://huggingface.co/LLM4Binary/
Original model: https://huggingface.co/LLM4Binary/llm4decompile-1.3b-v1.5/

Name	Quant method	Size
llm4decompile-1.3b-v1.5.Q2_K.gguf	Q2_K	0.52GB
llm4decompile-1.3b-v1.5.IQ3_XS.gguf	IQ3_XS	0.57GB
llm4decompile-1.3b-v1.5.IQ3_S.gguf	IQ3_S	0.6GB
llm4decompile-1.3b-v1.5.Q3_K_S.gguf	Q3_K_S	0.6GB
llm4decompile-1.3b-v1.5.IQ3_M.gguf	IQ3_M	0.63GB
llm4decompile-1.3b-v1.5.Q3_K.gguf	Q3_K	0.66GB
llm4decompile-1.3b-v1.5.Q3_K_M.gguf	Q3_K_M	0.66GB
llm4decompile-1.3b-v1.5.Q3_K_L.gguf	Q3_K_L	0.69GB
llm4decompile-1.3b-v1.5.IQ4_XS.gguf	IQ4_XS	0.7GB
llm4decompile-1.3b-v1.5.Q4_0.gguf	Q4_0	0.72GB
llm4decompile-1.3b-v1.5.IQ4_NL.gguf	IQ4_NL	0.73GB
llm4decompile-1.3b-v1.5.Q4_K_S.gguf	Q4_K_S	0.76GB
llm4decompile-1.3b-v1.5.Q4_K.gguf	Q4_K	0.81GB
llm4decompile-1.3b-v1.5.Q4_K_M.gguf	Q4_K_M	0.81GB
llm4decompile-1.3b-v1.5.Q4_1.gguf	Q4_1	0.8GB
llm4decompile-1.3b-v1.5.Q5_0.gguf	Q5_0	0.87GB
llm4decompile-1.3b-v1.5.Q5_K_S.gguf	Q5_K_S	0.89GB
llm4decompile-1.3b-v1.5.Q5_K.gguf	Q5_K	0.93GB
llm4decompile-1.3b-v1.5.Q5_K_M.gguf	Q5_K_M	0.93GB
llm4decompile-1.3b-v1.5.Q5_1.gguf	Q5_1	0.95GB
llm4decompile-1.3b-v1.5.Q6_K.gguf	Q6_K	1.09GB
llm4decompile-1.3b-v1.5.Q8_0.gguf	Q8_0	1.33GB

Original model description:

license: mit tags:

decompile
binary widget:
text: "# This is the assembly code:\n:\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

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.

RichardErkhov/LLM4Binary_-_llm4decompile-1.3b-v1.5-gguf