decomp-synth-lifter-v10-qwen3.6-27b-lora

decomp-synth lifter v10 — Qwen3.6-27B LoRA

A LoRA adapter that turns Qwen3.6-27B into a byte-exact decompilation matcher for PowerPC/Xbox 360 MSVC C++: given a target object listing and a compiling-but-not-matching C++ source, it emits corrected source whose compiled bytes are identical to the shipped binary. It is the project's first published 27B and the reference arm of a controlled base-generation pair (see the Qwen3.8 sibling).

The project

This adapter comes out of a campaign teaching fleets of AI coding agents to reconstruct shipped game binaries byte-for-byte — matching decompilation, the console-preservation community's gold standard: rewrite C++ until the original vintage compiler, at the original flags, reproduces the retail machine code exactly. In that campaign, agent swarms byte-matched over 44,000 functions of a stripped retail Xbox 360 binary in about ten weeks — with every proposal judged by the real compiler and objdiff, never by a model.

decomp-synth is the search-and-ML side of that system: an engine that searches behavior-preserving C++ rewrites against the real build, and mines its own verified attempts into training data. The adapters published under this account are the resulting proposer models. The design rule the whole system is built on: models propose, deterministic oracles dispose — a fuzzy match score is a search gradient and an adversary, and byte-exact recompilation is the only terminal judge.

Background reading, in order:

• How agent swarms decompile games byte-for-byte — the campaign, and what byte-exact matching actually demands
• LLM-assisted decompilation: tools, not prompts — why the model is never allowed to judge its own work
• Forking the decompilation toolchain — the compiler, splitter, and diff stack underneath
• Infrastructure for running agent swarms — what running the fleet takes

Model facts

Evaluation

Drift roster r3: 380 real-drift functions from an X360 decompilation tree where the checked-in source compiles but does not match. k=20 samples, T=0.6, max_new 768. Grader: objdiff-cli 4.2.8 (≥4.1.0 asserted), functionRelocDiffs=none ruler. Numbers from the 2026-08-26 paired scoring session (grader-matched with the Qwen3.8 sibling below):

• 12 / 380 byte-exact solves (raw+reloc equal, fresh-recompile verified)
• best-of-k fuzzy: mean 45.5, median 15.0, 44 rows ≥90, 21 rows ≥99
• compile rate 68.4% of 7,600 draws
• Δ best-of-k fuzzy vs the untouched-tree null: +6.74 (restored method)

An untrained Qwen2.5-Coder-7B control on the same instrument solves 0-2 and sits at or below the null. Numbers are not comparable across rosters or k; always state both.

Sibling

The same recipe run verbatim on Qwen3.8-27B is a statistical tie (paired Δ +0.37, p=0.52, 380 paired rows) with a different shape: broader coverage (compile 71.4%, p25 +3.7), fewer concentrated solves (9 vs 12). Released as a matched pair across base generations.

How to use it

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.6-27B", dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.6-27B")
model = PeftModel.from_pretrained(base, "freeqaz/decomp-synth-lifter-v10-qwen3.6-27b-lora")

This is a search proposal engine, not a one-shot decompiler. It was trained — and evaluated — inside a compile-and-score loop:

1. Render the prompt: an objdiff-annotated listing of the target function's original bytes, followed by the current (compiling but mismatching) C++. RAW completion — no chat template.
2. Sample many candidates (the evaluation above uses k=20 at T≈0.6).
3. Compile every candidate with the target project's real toolchain.
4. Score each object against the retail object with objdiff. A byte-exact result is a solve; the fuzzy % is a gradient for picking what to try next, never an acceptance criterion.

Single greedy completions waste the model; nearly all of its measured value is in the k-sample tail. For batch sampling, any OpenAI-compatible server that supports LoRA adapters (e.g. vLLM with --enable-lora) works well.

The adapter family

Integrity

PUBLISHED.json (shipped) carries the sha256 of every artifact file, written by the training job at publish time. Two internal metadata files it pins (train_summary.json, artifact-manifest.json) are retained in the project's artifact store but not shipped here — they carry training-box paths. adapter_config.json was rewritten post-training to point base_model_name_or_path at the HF id; the original bytes are preserved at provenance/adapter_config.orig.json. To verify the weights, check them against the files map in PUBLISHED.json directly:

sha256sum adapter_model.safetensors  # a1904fae11de1ce4…

(identity_sha256 is the training job's digest over the original {path, size, sha256} records — the config record matches provenance/adapter_config.orig.json, not the rewritten file.)

Training data & license

Adapter weights: Apache-2.0. The training corpus consists of before→after C++ repair pairs derived from public, CC0-1.0-dedicated decompilation projects (Xbox 360 Rock Band 3 / Dance Central 3 community decompilations). CC0 covers the decomp contributors' authorship; it does not speak for the game publishers' rights in the underlying works. A model trained to reproduce shipped game code can reproduce shipped game code — that is its purpose. Use accordingly; decompilation projects operate in a long-standing community norm of clean-room reconstruction for interoperability and preservation.

The training corpora themselves are not published. PUBLISHED.json names the corpus file and its sha256 as a provenance pointer.