Qwen3-4B-C-Coder-SFT-v1

Qwen3-4B-C-Coder-SFT-v1

A C-language coding specialist built on Qwen3-4B-Instruct-2507, fine-tuned with QLoRA (rank 32, all linear layers) on ~23k curated C instruction pairs. This is SFT v1 — the first stage of a larger pipeline (synthetic execution-filtered data and GRPO with a compiler/sanitizer reward are planned follow-ups).

The headline improvement is instruction compliance for C: the model follows exact function signatures and emits compilable code far more reliably than its base.

Results

All benchmarks are execution-based: generated C is compiled with gcc and run against hidden tests in a sandboxed container. safe-pass@1 additionally requires zero ASan/UBSan reports.

• CEval-priv is a private, contamination-proof eval set: 161 tasks machine-translated from HumanEval+/MBPP+ to C with deterministic type mapping, kept only if the reference solution compiles, passes its own tests, and runs sanitizer-clean. It was never trained on and all training data was decontaminated against it (10-gram overlap).
• McEval-C is the public McEval C-generation split. The pass@1 delta is within noise at n=50, single-sample; the compile-rate gain is the real signal.
• Sanitizer-clean pass rates equalled pass@1 for both models on both benchmarks.

Interpretation: one epoch of public C data (StackOverflow Q&A, curated instruction sets) teaches behavior — signature compliance, compilable output — not new algorithmic ability. Exactly what you'd expect, and what the later pipeline stages are for.

Training

Data

~34.7k cleaned pairs, sampled to 22.9k train after mixing:

All slices were exact-deduplicated and decontaminated (word-level 10-gram overlap) against McEval, MdEval, HumanEval(+), MBPP(+), and the private eval set.

Usage

Non-thinking model — use Qwen's recommended sampling: temperature=0.7, top_p=0.8, top_k=20.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "harshpreet931/Qwen3-4B-C-Coder-SFT-v1"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content":
    "Write a C function `int popcount32(unsigned int x)` that counts set bits."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.8, top_k=20)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Apple Silicon (4-bit MLX): see harshpreet931/Qwen3-4B-C-Coder-SFT-v1-mlx-4bit. LoRA adapters only: harshpreet931/Qwen3-4B-C-Coder-SFT-v1-LoRA.

Limitations

• Algorithmic ability is unchanged from the base model. This stage improved format/signature compliance and compile rates, not problem-solving. Don't expect gains on hard competitive tasks.
• English-only instruction data; C17/glibc-flavored; not tuned for embedded/kernel dialects.
• Inherits base-model limitations and possible biases; generated code should be reviewed and tested — compile-and-run verification (ideally with -fsanitize=address,undefined) is cheap, use it.

Provenance

Built as part of an open, $0-compute project (MacBook M4 Pro + Kaggle free T4s): six-agent research sweep → sandboxed compile/run/sanitizer harness → data pipeline → this SFT run. Fun fact surfaced by the harness: one of McEval-C's own canonical solutions fails LeakSanitizer.