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ortexvortex
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Model Details
Model Provider
ortexvortex
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
Test set: 249 functions from source files that appear nowhere in training (the split is grouped by source file).
Every candidate is judged by fnvcheck, the project's own checker: compile with the real MSVC 2008 compiler, compare
with the original function byte for byte. A function only counts as solved on a byte-exact MATCH that also follows
the project rule against filler arrays. Base and fine-tuned model ran with the same prompts, the same token budget
(min(4096, 1024 + 8 x size) new tokens) and the same llama.cpp Q8_0 runtime; 1 greedy + 8 sampled (T = 0.7, top-p 0.95)
completions per function. The test set was used once for this comparison; checkpoints were chosen on a separate dev set.
| Metric (249 unseen functions) | Base Qwen3.5-4B | This model |
|---|---|---|
| Greedy byte-exact match | 0.4% | 18.5% |
| Greedy match, excluding 20 functions that near-duplicate a training example ("novel") | 0.0% | 13.1% |
| pass@1 (sampled) | 0.4% | 14.9% |
| pass@4 (sampled) | 1.3% | 19.7% |
| pass@8 (sampled) | 2.4% | 21.3% |
| Solved by any of the 9 completions | 2.4% | 22.5% |
| Greedy output compiles | 10.0% | 51.8% |
Paired comparison on the same 249 functions: 46 solved only by the fine-tune, 1 only by the base model (exact McNemar p = 6.8e-13); sampled pass@1 difference +14.6 points (bootstrap 95% CI +10.7 to +18.6). All 46 greedy matches were re-checked independently on freshly built packs, and none appears verbatim in any training answer.
Greedy match by slice (fine-tune / base):
| Slice | n | This model | Base |
|---|---|---|---|
| Function size < 16 bytes | 44 | 68.2% | 2.3% |
| 16-63 bytes | 57 | 24.6% | 0.0% |
| 64-255 bytes | 131 | 1.5% | 0.0% |
| 256-1023 bytes | 12 | 0.0% | 0.0% |
| >= 1024 bytes | 5 | 0.0% | 0.0% |
Built with /Od | 84 | 20.2% | 1.2% |
Built with /O2 | 165 | 17.6% | 0.0% |
Honest summary: the model has learned small, patterned functions well and is useful there; it rarely solves
functions of 64 bytes or more. In a live run against the project's open queue (121 claimed functions, almost all
128-255 bytes, every one previously attempted by other agents) it produced no matches. fnvcheck's --solve-slots
and --permute near-miss solvers rescued only 3 of 705 near misses.
Use the Qwen chat template with thinking disabled (enable_thinking=False). The model was trained on exactly this
format; other formats will do worse.
System message:
text
You are a matching decompiler for Fallout: New Vegas (PC, 1.4.0.525), built with Microsoft Visual C++ 2008 SP1 (cl 15.00.30729.01).Given one function's machine code and the skeleton of the source file it belongs to, write C++ that this compiler turns into exactly the same bytes.Rules: real, readable C++ only; no inline assembly, naked functions or raw bytes; no hard-coded absolute addresses. Name the function exactly as given. Declare any stand-in types, fields (unkXX at their offsets, never filler arrays) and callees you need; use names the skeleton already declares.Answer with one ```cpp block holding the complete candidate and nothing else.User message (placeholders in braces):
text
Function: {symbol, e.g. FUN_00xxxxxx}Size: {size} bytesCompiler flags: {flags, e.g. /MT /O2 /GS /EHsc /Gy} Target machine code:```asm{the function's listing as rendered by fnvcheck: fc_common.render_function(code, addr, size, Namer())}``` Source file skeleton ({unit file name}[, excerpt]):```cpp{the context pack's unit file; if longer than ~9000 characters, the #include lines plus the blocks that mention the most FUN_/DAT_/str_ names from the listing, in file order, gaps marked "// ..."}```The listing must come from your own legitimately purchased copy of the game, rendered with fnvcheck (available to fnvdecomp contributors). No game code, machine code or disassembly is distributed here.
Recommended decoding: greedy first; stop at the closing "```" of the code block; if a completion runs into the token limit it has entered a repetition loop - discard it and sample again. Always verify with fnvcheck before submitting anything to fnvdecomp.
llama.cpp (as benchmarked; Vulkan on an AMD Strix Halo iGPU reached ~45 tok/s single-stream, ~350 tok/s at 32 parallel):
bash
llama-server -m qwen3.5-4b-fnvdecomp-r1-q8_0.gguf -ngl 99 -fa on -c 16384Send the prompt already rendered by the Qwen chat template (enable_thinking=False) to /completion.
Transformers + PEFT (load the text-only causal LM):
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-4B")model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B", dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "ortexvortex/Qwen3.5-4B-fnvdecomp-r1")prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)A second run with 22% more data (r2) tied with this one on test (16.5% greedy, McNemar p = 0.23), so r1 is published.
You are a matching decompiler for Fallout: New Vegas (PC, 1.4.0.525), built with Microsoft Visual C++ 2008 SP1 (cl 15.00.30729.01).Given one function's machine code and the skeleton of the source file it belongs to, write C++ that this compiler turns into exactly the same bytes.Rules: real, readable C++ only; no inline assembly, naked functions or raw bytes; no hard-coded absolute addresses. Name the function exactly as given. Declare any stand-in types, fields (unkXX at their offsets, never filler arrays) and callees you need; use names the skeleton already declares.Answer with one ```cpp block holding the complete candidate and nothing else.Function: {symbol, e.g. FUN_00xxxxxx}Size: {size} bytesCompiler flags: {flags, e.g. /MT /O2 /GS /EHsc /Gy}Target machine code:```asm{the function's listing as rendered by fnvcheck: fc_common.render_function(code, addr, size, Namer())}```Source file skeleton ({unit file name}[, excerpt]):```cpp{the context pack's unit file; if longer than ~9000 characters, the #include lines plus the blocks that mention the most FUN_/DAT_/str_ names from the listing, in file order, gaps marked "// ..."}```llama-server -m qwen3.5-4b-fnvdecomp-r1-q8_0.gguf -ngl 99 -fa on -c 16384import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-4B")model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B", dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "ortexvortex/Qwen3.5-4B-fnvdecomp-r1")prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)