guu3/AutoDecompiler-30B-pscode-GGUF

AutoDecompiler-30B-pscode GGUF

This repository contains an unquantized BF16 GGUF conversion of AutoDecompiler/AutoDecompiler-30B-pscode. It is a format conversion for llama.cpp; the model weights were not fine-tuned or otherwise modified here.

The model is specialized for turning decompiler P-code/pseudocode into a high-level source-code draft. It is not intended to decompile raw assembly directly.

File

File	Format	Size	SHA-256
AutoDecompiler-30B-pscode-BF16.gguf	GGUF v3, BF16	61,095,804,640 bytes (56.89 GiB)	83d5a42e828e391aac16e68ac2fe7332d7ed4307cf5cf470cb3c33be35c92367

The GGUF contains the model's Qwen3 MoE architecture and chat template. Its metadata advertises a 262,144-token context, but practical context size is limited by available memory. The weights alone require roughly 57 GiB, with additional memory needed for the KV cache and runtime workspace.

Download

hf download guu3/AutoDecompiler-30B-pscode-GGUF \
  AutoDecompiler-30B-pscode-BF16.gguf \
  --local-dir .

Run with llama.cpp

Use a recent llama.cpp build with Qwen3 MoE and BF16 support:

llama-server \
  --model AutoDecompiler-30B-pscode-BF16.gguf \
  --alias autodecompiler-30b-pscode-bf16 \
  --host 127.0.0.1 \
  --port 8080 \
  --ctx-size 32768 \
  --n-gpu-layers all \
  --flash-attn on

Reduce --n-gpu-layers or --ctx-size if the model does not fit available GPU or unified memory.

Send P-code through the OpenAI-compatible endpoint:

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "autodecompiler-30b-pscode-bf16",
    "messages": [
      {
        "role": "system",
        "content": "You are a decompilation specialist. Convert the supplied P-code pseudocode into a faithful high-level source representation."
      },
      {
        "role": "user",
        "content": "Recover high-level source code from this P-code pseudocode. Return code only.\n\n<PASTE PCODE HERE>"
      }
    ],
    "temperature": 0,
    "max_tokens": 4096,
    "stream": false
  }'

For reproducible evaluation, start with greedy decoding (temperature: 0). Increase max_tokens for larger functions, while keeping in mind that very large functions may be truncated or become impractically slow.

Conversion provenance

• Upstream model revision: 43c73bfabe24c284c31ccb76fc5dc90e5736b5dc
• Conversion tool: llama.cpp convert_hf_to_gguf.py
• llama.cpp revision: 48d22e295e2b86b47366c16390794f3e05ba970a
• Output type: BF16, with tensors that llama.cpp keeps in F32 left in F32
• Tested with llama.cpp build 10360

The conversion is equivalent to:

python convert_hf_to_gguf.py /path/to/AutoDecompiler-30B-pscode \
  --outfile AutoDecompiler-30B-pscode-BF16.gguf \
  --outtype bf16

Limitations

Treat generated code as an untrusted first-pass draft. In local experiments, the model recovered useful structure from P-code, but it could emit invalid identifiers or types and could fail to finish very large functions. Validate the result against the original binary, compiler diagnostics, and control flow.

See the AutoDecompiler paper and the upstream model repository for the model and research context.

License

The upstream model repository did not declare a license at the time of this conversion. This repository does not assert a new license over the model weights; consult the upstream authors before redistribution or commercial use.