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DistilQwen2.5 — Alibaba's industrial practices for training distilled open lightweight language models. Knowledge distillation from Qwen2.5 72B into smaller 0.5B-7B models.
Passed 22 of 22 test cases with the skill, and 14 of 22 without it.
and 6 more in the full report
8 fixed14 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to load the smallest DistilQwen2.5 instruct variant for edge deployment. What is its exact Hugging Face repository string?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies repository path distilqwen/distilqwen2.5-0.5b-instruct | Missed | Passed |
The instructions your model reads when the skill loads.
DistilQwen2.5 (Alibaba, arXiv:2504.15027) provides industrial practices for training distilled open lightweight LLMs. The approach distills Qwen2.5-72B-Instruct into smaller models (0.5B, 1.5B, 3B, 7B) with strong performance retention.
The distilled models are available on HuggingFace as distilqwen/distilqwen2.5-*-instruct and can be used directly:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("distilqwen/distilqwen2.5-1.5b-instruct")
tokenizer = AutoTokenizer.from_pretrained("distilqwen/distilqwen2.5-1.5b-instruct")
test cases passed, vs 14 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.