kernel-coder-1.5b

A 1.5B code model that writes C in Linux kernel style — tab indentation, brace placement, declarations before statements, -ERRNO returns, goto label unwinding — while keeping the base model's Python ability.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "nethunter2023/kernel-coder-1.5b"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")

messages = [
    {"role": "system", "content": "You are a Linux kernel developer. Reply with a "
                                  "single C code block containing only the function."},
    {"role": "user", "content": "Implement `int demo_probe(struct device *dev)`: "
                                "allocate a private struct and unwind on error."},
]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids.to(model.device), max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Chat template ships with the tokenizer. Greedy decoding; the answer is the last fenced code block.

Results — kernel C style

N = 40 held-out kernel-doc tasks, greedy decoding, scored with the kernel's own scripts/checkpatch.pl --no-tree --file --strict, reported as weighted defects per line: (2*errors + warnings + 0.5*checks) / lines. All models were given the identical prompts and scored by identical code.

	params	defects / line ↓	95% CI	checkpatch errors ↓	idiom ↑
deepseek-coder-1.3b-instruct	1.3B	0.679	±0.196	4.43	0.738
Qwen2.5-Coder-3B-Instruct	3B	0.750	±0.182	4.78	0.755
Qwen2.5-Coder-1.5B-Instruct (base)	1.5B	0.872	±0.185	5.58	0.664
kernel-coder-1.5b	1.5B	0.020	±0.012	0.00	0.995
the kernel's own code	—	0.017	—	0.00	1.000

Against Qwen2.5-Coder-3B-Instruct — twice the parameters — the paired difference is −0.73 defects/line, 95% CI [−0.91, −0.55], t = −7.87, lower on 33 of 40 tasks. No general-purpose code model tested comes close, and this model sits within noise of the kernel's own source.

Results — Python

MBPP test, 200 problems, greedy, executing the dataset's assertions.

	params	pass@1
deepseek-coder-1.3b-instruct	1.3B	0.250
kernel-coder-1.5b	1.5B	0.420
Qwen2.5-Coder-1.5B-Instruct (base)	1.5B	0.420
Qwen2.5-Coder-3B-Instruct	3B	0.535

Python is unchanged from base — the kernel specialisation cost nothing, and gained nothing, here. A 3B model is still better at general Python.

If you re-run MBPP, strip the trailing print(...) / assert / __main__ statements the model appends after the function before executing. They run at import time and abort otherwise-correct solutions; leaving them in costs roughly 3 points.

Limitations

• The kernel gains are stylistic and structural, not functional. Kernel code cannot be executed in a sandbox, so nothing here measures semantic correctness. A well-formatted stub and a working implementation score alike. Review output before use.
• A large share of the checkpatch improvement is indentation. The base model indents kernel C with spaces; this one uses tabs, and checkpatch flags every space-indented line.
• Roughly half of kernel completions leave part of the body as placeholder comments rather than a full implementation — a rate unchanged from base.
• N = 40 on the kernel evaluation. The margin over the baselines is large relative to that, but finer distinctions would need a bigger set.
• Training methodology is not published.

Base model: Qwen/Qwen2.5-Coder-1.5B-Instruct