Instructions to use trentnorth/phi-4-minecraft-advisor-qlora-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use trentnorth/phi-4-minecraft-advisor-qlora-sft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/models/hub/models--microsoft--phi-4/snapshots/932b33c0ec9ca189badeb22480721a8de9d0e006") model = PeftModel.from_pretrained(base_model, "trentnorth/phi-4-minecraft-advisor-qlora-sft") - Notebooks
- Google Colab
- Kaggle
phi-4-minecraft-advisor-qlora-sft
QLoRA SFT adapter for microsoft/phi-4 trained on an ecological advisor corpus for Minecraft Alive mod.
Eval results (104-prompt advisor eval, Claude judge)
| Metric | Score | Gate | |
|---|---|---|---|
| TP recall | 0.972 | ≥0.90 | PASS |
| TN precision | 0.958 | ≥0.85 | PASS |
| Joint F | 0.965 | ≥0.88 | PASS |
| Edge accuracy | 1.000 | ≥0.75 | PASS |
| Mechanism mean | 1.84/2.0 | ≥1.50 | PASS |
Zero-shot phi-4 baseline: TP=1.000, TN=0.667 (over-alerts on every scenario).
Training details
- Base model: microsoft/phi-4
- Task: QLoRA SFT, 3 epochs, r=32, alpha=64, target_modules=all-linear, NF4 4-bit
- Corpus: 1384 train / 154 val scenarios
Framework versions
- PEFT 0.13.0
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Base model
microsoft/phi-4