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
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Download eval/table.md from DavidSlayback1/grade-level-explainer-4b: direct link, hf CLI and curl.
- Browser
- Download file 838 Bytes
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https://huggingface.co/DavidSlayback1/grade-level-explainer-4b/resolve/main/eval/table.md
- Command line
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hf download hf://DavidSlayback1/grade-level-explainer-4b/eval/table.md
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curl -L -o table.md https://huggingface.co/DavidSlayback1/grade-level-explainer-4b/resolve/main/eval/table.md
838 Bytes
| # 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** |