decomp-synth-lifter-v16-v5chat-qwen3.5-9b-lora

decomp-synth lifter v16-v5chat — Qwen3.5-9B LoRA (r32)

A LoRA adapter tuning Qwen3.5-9B for byte-exact decompilation matching of PowerPC C++ (Xbox 360 MSVC cl.exe and GameCube/Wii CodeWarrior mwcc), trained on the project's broadest corpus to date: 8,735 chat-rendered rows spanning repair, synthesis-from-stub, and edit-proposal task shapes, with context-view rendering (the same verified repair chain rendered with full history, retry-state, and zero-history views).

Read the notes below before choosing this over v14-evalformat.

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. This adapter is the broadest cut of that flywheel so far — and part of the project's practice of publishing research artifacts with their evidence stated plainly, including the null results. The design rule throughout: models propose, deterministic oracles dispose — 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

Honest status notes

Published as a research artifact with its evidence stated plainly:

• No measured win over v14. In a paired 4-model run on the multi-turn feedback instrument (118 start states, K=2 replicas), v16 does not separate from v14 at any rank: witness deltas +2/+1/+2, p = 1.0 / 1.0 / .77.
• The rank ladder is flat. r32/r64/r128 trained on the byte-identical corpus measure within noise of each other (r64−r32 −1, p=.77; r128−r32 exactly 0, p=1.0), replicating a flat NLL ladder — adapter capacity is not the 9B ceiling, which is why this release is the r32.
• Single training seed (seed=42, all rungs) — even the flat verdict is provisional on magnitude under the project's multi-seed rule.
• The corpus is 100% single-turn while the target instrument is 4-turn — a known mismatch; the multi-turn supply line is separate, in-flight work.
• The instrument itself was live in the same cells: real compiler feedback beat derangement-scrambled feedback in all six arm×replica cells (strongest: +8 discordant vs 0, p=.0078).

What it does bring over v14: ~2.2× the training rows, the proposal-model and synthesis task shapes, and broader substrate coverage (below).

Model facts

Training sources

Rows are derived from the change histories of these community decompilation projects (measured per-row attribution of the corpus):

• rb3-xenon and dc3-decomp — Xbox 360 MSVC (≈7,600 rows)
• zeldaret/tww and zeldaret/tp — GameCube mwcc (≈570 rows)
• DarkRTA/rb3 — Wii mwcc (≈510 rows)
• plus ≈1,260 rows from a private Halo: Combat Evolved Anniversary (Xbox 360) reconstruction effort, not publicly linkable

See each linked project for its own terms. The corpora themselves are not published; the shipped PUBLISHED.json pins the corpus file's sha256 as provenance.

How to use it

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B", dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base, "freeqaz/decomp-synth-lifter-v16-v5chat-qwen3.5-9b-lora")

Chat template. Prompts follow the project's repair / synthesis / proposal formats; like every adapter in this family, it is a proposal engine for a compile-and-score loop — sample multiple candidates, compile each with the target project's real toolchain, and let objdiff judge. Byte-exact is the only acceptance criterion. If your use case is the 4-turn feedback loop, prefer v14-evalformat (no measured v16 win there); reach for v16 when you want the synthesis-from-stub and edit-proposal task shapes or the broader substrate coverage. At 9B it fits a single 24 GB GPU in bf16; vLLM with --enable-lora serves it well.

The adapter family

Integrity

PUBLISHED.json (shipped) carries per-file sha256 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 the HF base id; original bytes at provenance/adapter_config.orig.json.

License

Adapter weights: Apache-2.0. A model trained to reproduce shipped game code can reproduce shipped game code — that is its purpose; decompilation projects operate in a long-standing community norm of clean-room reconstruction for interoperability and preservation.