Instructions to use Aurumdev95/gemma4-31b-cessna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Aurumdev95/gemma4-31b-cessna with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-31B-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Aurumdev95/gemma4-31b-cessna") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Gemma 4 31B โ Cessna 172S POH assistant (LoRA)
QLoRA adapter fine-tuning Gemma 4 31B to answer questions about the Cessna 172S avionics equipment, from a synthetic Q&A dataset grounded in the aircraft's Pilot's Operating Handbook (POH) Supplements volume.
- Base model:
unsloth/gemma-4-31B-it-unsloth-bnb-4bit - Method: QLoRA (4-bit), LoRA rank 32, language layers only, trained with Unsloth on an NVIDIA DGX Spark (GB10).
- Data: ~850 grounded Q&A pairs (equipment-disambiguated, de-noised), best-by-validation checkpoint.
Load
from unsloth import FastModel
model, tok = FastModel.from_pretrained("Aurumdev95/gemma4-31b-cessna", load_in_4bit=True, device_map={"": 0})
FastModel.for_inference(model)
Scope & limitations
- Coverage is the POH Supplements only (avionics: KMA 28, KX 155A, KAP 140, KLN 94, KR 87, etc.). Core-airframe topics (stall warning, electrical/ammeter, performance, limitations) are not in the source data โ answers there rely on the base model's general knowledge and are not POH-verified.
- A few counterintuitive facts (e.g. the KAP 140 being rate-/turn-coordinator-driven rather than attitude-gyro-driven) may still be answered from the base model's prior.
- Not for operational flight use. Always consult the actual POH/AFM.
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Model tree for Aurumdev95/gemma4-31b-cessna
Base model
google/gemma-4-31B Finetuned
google/gemma-4-31B-it Quantized
unsloth/gemma-4-31B-it-unsloth-bnb-4bit