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Model Details
Model Provider
ceferra
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Official ADeLe v1.0 battery (16,108 items, 63 tasks). Tasks were split
51 (train) / 6 (validation) / 6 (test), by task name (seed 42), so that
validation/test tasks are never seen during training — see
docs/split_manifest.json in the main repo for the exact task lists.
Qwen/Qwen2.5-7B-Instructq_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_projAgainst the actual GPT-4o annotations present in the project's held-out benchmark data (5 external benchmarks: MBPP, TheoremQA, Omega, RelBench, SWE-bench-Verified; rWG within-group agreement index, mean over 18 rubrics):
| Annotator | rWG vs. GPT-4o |
|---|---|
| Qwen2.5-72B-Instruct-AWQ | 0.892 |
| Llama-3.3-70B-Instruct-AWQ | 0.908 |
| Mistral-Large-Instruct-2411-AWQ | 0.918 |
| Distilled Qwen2.5-7B (this adapter) | 0.934 |
| Median ensemble of all 4 | 0.944 |
The distilled 7B model matches or exceeds each individual large model's agreement with GPT-4o, at a small fraction of the inference cost, on 17 of 18 rubrics (the exception being rubric KNa, where all candidates, including the full ensemble, score below 0.78).
See the main repository's results/rwg_gpt4o_analysis/ and
reports/DeLeAn_Full_Project_Report.pdf for full details and all 18
per-rubric numbers.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")model = PeftModel.from_pretrained(base, "path/to/this/adapter")tok = AutoTokenizer.from_pretrained("path/to/this/adapter")
For fast batch inference, merge the adapter into the base model first
(see scripts/merge_lora.py in the main repository) and serve with vLLM.
MIT. Please cite the ADeLe paper (Zhou et al., Nature 2026; arXiv:2503.06378) and the official ADeLe repository (https://github.com/Kinds-of-Intelligence-CFI/ADeLe-AIEvaluation) if you use this adapter, since it is trained entirely on ADeLe's official task battery.