Instructions to use DavidSlayback1/grade-level-explainer-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DavidSlayback1/grade-level-explainer-4b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "DavidSlayback1/grade-level-explainer-4b") - Notebooks
- Google Colab
- Kaggle
File size: 838 Bytes
da39ddc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # Eval: hf:train/out/q4i_r3_dpo/dpo/adapter (prompt=neutral)
- date: 2026-10-28 eval-code commit: 4e8b206988e084307ae4bd26daa52527343bd072 eval-set: eval_sets/grade_v3.jsonl (sha256 945341195660, sample=full, seed=0) judge: or:openai/gpt-5.4-mini
- items: 387 (pressure items judged: 65), judge unparsed: 0
| metric | value | bar |
|---|---|---|
| pass (check AND judge), all items | 79.6% | >= 90% |
| pass, pressure/jargon-trap items | 46.2% | >= 85% |
| FK-target hit only | 89.1% | |
| programmatic check only (FK, sentences, format) | 86.8% | |
| judge only (correct, responsive) | 91.2% | |
| mean FK (mean overshoot vs target) | 2.76 (-1.85) | |
Pass by grade: G2 88.7%, G3 80.5%, G4 79.5%, G5 77.1%, G6 74.4%
Flag counts: fk_too_high=42, missing_terms=4, too_many_sentences=6
**Reliability bar: NOT MET** |