Code_Qwen3-4B_Qwen3-30B-A3B-Instruct-2507
Qwen/Qwen3-4B distilled from Qwen/Qwen3-30B-A3B-Instruct-2507 with Ask to Distill.
Ask to Distill trains a question generator with RL so that the questions it writes expose bounded, learnable token-level disagreement between a teacher and a student on the student's own solutions (the same signal on-policy distillation learns from). A question bank is then sampled from the generator, filtered and deduplicated, and the student is trained with on-policy distillation (OPD) on it. No real training questions and no reference answers are used.
Training
| Student | Qwen/Qwen3-4B |
| Teacher | Qwen/Qwen3-30B-A3B-Instruct-2507 |
| Question bank | gasolsun/Code_Qwen3-4B_Qwen3-30B-A3B-Instruct-2507 |
| OPD objective | per-token reverse KL on the student's top-16 tokens, weighted by renormalised student probabilities; no outcome reward |
| OPD recipe | lr 1e-6 (constant), AdamW, rollout temperature 1.0, fp32 master weights / bf16 compute |
Results
| LiveCodeBench v5_v6 | MBPP+ | APPS-Intro | APPS-Interview | APPS-Comp | CodeContests | Avg |
|---|---|---|---|---|---|---|
| 31.7 | 67.5 | 58.3 | 31.6 | 11.0 | 13.9 | 35.7 |
LiveCodeBench v5_v6 (342 problems, n=4, pass@1); MBPP+ (EvalPlus, greedy, lenient answer extraction); APPS intro / interview (1k subset) / competition and CodeContests test (165): n=1, temperature 0.2, top-p 0.95, top-k 20, 4,096 tokens, at most 50 tests per problem. Avg = mean of six.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
repo = "gasolsun/Code_Qwen3-4B_Qwen3-30B-A3B-Instruct-2507"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
messages = [{"role": "user", "content": "..."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", enable_thinking=False).to(model.device)
out = model.generate(inputs, max_new_tokens=4096, do_sample=True, temperature=0.7, top_p=0.95)
print(tok.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))
- The student was trained and evaluated in non-thinking mode; pass
enable_thinking=Falseto the chat template. - Prompts use the LiveCodeBench stdin/stdout template (read from standard input, write to standard output, one fenced Python block).