C2Rust

C2Rust is a full-parameter BF16 fine-tune of Qwen/Qwen3.5-27B for translating C programs into behaviorally equivalent Rust. The model is trained with a three-stage curriculum and evaluated with an execution-based SACTOR harness that compiles each candidate and compares its behavior with the source C program.

Paper

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

Pu Zhao, Changdi Yang, Yixiao Chen, Yi Gao, Yifan Cao, Haochen Zeng, and Yanzhi Wang.
Northeastern University · EmbodyX Inc · Aibao LLC

The arXiv title uses “Qwen3-27B”; Section 3 identifies the released source checkpoint as Qwen/Qwen3.5-27B, which is the identifier used throughout this model card.

Results

87.30% execution-verified C2Rust Success Rate — a 15.00 percentage-point gain over the untuned Qwen3.5-27B base at identical model size. The 27B checkpoint also outperforms Qwen3.5-Plus (397B total), MiniMax-M2.5 (230B total), and GLM-5 (744B total) on this benchmark.

C2Rust translation success rate

Success Rate (SR) is the percentage of programs that compile and pass every end-to-end test. Scores are arithmetic means over five random seeds under the same inference configuration.

Model	Model size	SR
Qwen3.5-Plus	397B total / 17B active	77.20%
MiniMax-M2.5	230B total / 10B active	83.90%
GLM-5	744B total / 40B active	84.40%
GLM-5.2	744B total / 40B active	89.90%
Claude Code-4.6	undisclosed	90.01%
Qwen3.5-27B base	27B dense	72.30%
C2Rust (this model)	27B dense	87.30%

The curriculum improves the direct Qwen3.5-27B baseline by 15.00 percentage points while keeping model size and serving cost fixed. C2Rust outperforms Qwen3.5-Plus, MiniMax-M2.5, and GLM-5 on this task, while remaining below GLM-5.2 and Claude Code-4.6.

General coding capability

Model	SWE-bench Verified pass@1
GPT-5-mini (2025-08-07)	72.0
GPT-OSS-120B	62.0
Qwen3.5-122B-A10B	72.0
Qwen3.5-27B base	72.4
C2Rust (this model)	70.6

The 1.8-point difference from the untuned base suggests a modest specialization cost, while the model retains strong general software-engineering performance.

Three-stage training curriculum

Stage	Objective	Data	Training configuration
1. Rust continued pretraining	Strengthen Rust syntax, idioms, completion, repair, and library knowledge	1,673,289 examples from seven Rust-focused sources	Full-parameter BF16, 1 epoch, LR 1e-6
2. Debugging-aware SFT	Learn to consume structured verifier feedback and make targeted repairs	microsoft/Verus_Training_Data	Full-parameter BF16, 2 epochs, LR 2e-7
3. C2Rust task SFT	Learn direct C-to-Rust semantic translation	C2Rust-Moxin functions/ and programs/ pairs	Full-parameter BF16, 2 epochs, LR 2e-7

Stage 1 combines Strandset-Rust, CodeFIM-Rust-Mellum, rust_instruction_dataset, humaneval-rust, the Rust subset of Magicoder-OSS-Instruct-75K, the Rust program-synthesis and repair subsets of xCodeEval, and the Rust subset of StarCoderData.

All three stages use a 16,384-token sequence length, DeepSpeed ZeRO Stage 3, and eight NVIDIA B300 GPUs. Training is text-only. The Qwen3.5 vision encoder remains in the released checkpoint but receives no task input and plays no role in C-to-Rust translation.

Model details

Field	Value
Base model	Qwen/Qwen3.5-27B
Parameters	27B language model (~28B including the retained vision encoder)
Weight format	Safetensors
Precision	BF16
Context used in training	16,384 tokens
Fine-tuning type	Full-parameter
Primary task	C-to-Rust program translation
License	Apache-2.0

The tokenizer, vocabulary, and architecture are unchanged from the base checkpoint; no task-specific special tokens were added.

Evaluation protocol

The companion benchmark contains 200 C programs: 92 receive command-line arguments and 108 read standard input. Approximately 120 are derived from IBM Project CodeNet. A translation succeeds only when the generated Rust program compiles and reproduces every reference output on the supplied tests within a six-attempt translation and repair budget.

Setting	Value
Temperature	0.6
Top-p	0.95
Top-k	20
Maximum output length	1,536 tokens
Maximum translation attempts	6
Random seeds	5

The released repository's default configs evaluate SACTOR's interface-preserving, unidiomatic stage. Generated code may therefore contain raw pointers or unsafe Rust. Passing the benchmark measures agreement on the supplied test suite, not formal semantic equivalence.

Resources

• Paper: arXiv:2608.13681
• Project website
• Benchmark, evaluation harness, and setup instructions
• Base model: Qwen/Qwen3.5-27B
• SACTOR translation engine
• C2Rust-Moxin training datasets

Running with the benchmark

Download the checkpoint:

hf download moxin-org/C2Rust --local-dir /path/to/C2Rust-model

Clone and prepare the benchmark:

git clone https://github.com/moxin-org/C2Rust.git
cd C2Rust
bash fix_paths.sh

cd engine
uv sync
./update_rust_ast_parser.sh
cargo build --release
cd ..

Launch the checkpoint with SGLang:

export SERVE_VENV=/path/to/sglang-venv
./scripts/launch_model.sh /path/to/C2Rust-model 0,1 30878 2

Run a two-program smoke test before the complete evaluation:

python3 scripts/run_eval.py configs/native_prompt.toml results/_smoke \
  --modes argv --limit 2 --workers 1

See the benchmark README and SETUP.md for the complete environment, five-seed evaluation, aggregation, and troubleshooting workflow.

Intended use

This release is intended for research and experimentation on C-to-Rust translation. Treat every generated program as a candidate: compile it, test it against the original implementation, and review it for correctness, safety, and maintainability before use.

Limitations

• Passing the supplied tests is not proof of semantic equivalence, memory safety, or security.
• The default evaluation permits unsafe Rust and prioritizes behavior preservation over idiomaticity.
• Stage 3 uses function- and program-level pairs, but excludes project-level training examples.
• The model scores 70.6 on SWE-bench Verified versus 72.4 for the base checkpoint, suggesting mild capability narrowing after full-parameter specialization.
• The report does not yet provide an ablation isolating each curriculum stage's marginal contribution.

Citation

@article{zhao2026c2rust,
  title   = {Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT},
  author  = {Zhao, Pu and Yang, Changdi and Chen, Yixiao and Gao, Yi and Cao, Yifan and Zeng, Haochen and Wang, Yanzhi},
  journal = {arXiv preprint arXiv:2608.13681},
  year    = {2026},
  doi     = {10.48550/arXiv.2608.13681}
}

License and attribution

The checkpoint is released under Apache-2.0 and is derived from Qwen/Qwen3.5-27B. The benchmark is Apache-2.0. Its dataset includes material derived from IBM Project CodeNet under CDLA-Permissive-2.0; see the dataset provenance and terms.