freeqaz

decomp-synth-lifter-gtp0c-qwen3.8-27b-lora

README

License: apache-2.0

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

Table

base	`Qwen/Qwen3.8-27B` (apache-2.0)
method	LoRA r=32, α=64, dropout 0.05, 12 target modules, bf16
window	20,480 tokens
corpus	4,992 repair/recover pairs, PowerPC X360 MSVC (byte-identical to v10's, sha `3c2c0bad…`)
trained	2026-08-24
format	RAW completion (not chat)

Evaluation

Same instrument and session as the v10 sibling (drift roster r3, 380 rows, k=20, T=0.6, objdiff-cli 4.2.8, none ruler, 2026-08-26 paired scoring):

9 / 380 byte-exact solves (raw+reloc equal, fresh-recompile verified)
best-of-k fuzzy: mean 46.6, median 18.7, 48 rows ≥90, 20 rows ≥99
compile rate 71.4% of 7,600 draws
Δ best-of-k fuzzy vs the untouched-tree null: +7.10 (restored method)

Head-to-head vs v10: a statistical tie — paired restored Δ +0.37, 102/92/186 better/worse/tied, sign-test p=0.52. The shape differs: the 3.8 arm moves the distribution up (p25 +3.7, +11 rows ≥10 fuzzy, higher compile rate) while v10 concentrates more byte-exact solves (12 vs 9, with 6 of v10's 12 resting on a single draw of twenty vs 1 of 9 here). Read it as coverage vs depth, not better vs worse. The recipe transplants onto the new base and fully recovers; it does not establish an edge.

How to use it

python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B", dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.8-27B")
model = PeftModel.from_pretrained(base, "freeqaz/decomp-synth-lifter-gtp0c-qwen3.8-27b-lora")

This is a search proposal engine, not a one-shot decompiler. Use it exactly as the v10 sibling: render the RAW dec prompt (objdiff-annotated target listing + current source, no chat template), sample many candidates (k=20 at T≈0.6 in the evaluation above), compile each with the target project's real toolchain, and let objdiff judge. Byte-exact is the only acceptance criterion; fuzzy % is a search gradient. If you want breadth (more functions moved closer), this arm's distribution is slightly stronger; if you want concentrated exact solves, v10's is.

The adapter family

Table with columns: adapter, base, format, role
adapter	base	format	role
v10	Qwen3.6-27B	RAW	single-turn matcher, reference 27B
gtp0c (this repo)	Qwen3.8-27B	RAW	v10 recipe verbatim on the newer base (matched pair)
v13-chain r32	Qwen3.5-9B	RAW	headline single-turn 9B

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.

Training data & license

Adapter weights: Apache-2.0. Training corpus is byte-identical to v10's: repair pairs derived from public CC0-1.0-dedicated X360 decompilation projects. See the v10 card for the full provenance discussion; everything there applies verbatim here.

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

freeqaz

Model Tree

Base

Qwen/Qwen3.8-27B

Adapter

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality