from transformers import LlavaOnevisionForConditionalGeneration, AutoProcessor
import torch

# Load base model
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
    "llava-hf/llava-onevision-qwen2-7b-ov-hf",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")

# Load fine-tuned adapter
model.load_adapter("azdin/llava-onevision-weather-qlora")

# Use for weather analysis...

Training Details

Technique: QLoRA
Quantization: 4-bit NF4
Training Data: Weather satellite imagery with metadata
Target Modules: Attention and projection layers

azeddinShr/llava-onevision-weather-qlora

LLaVA-OneVision Weather Analysis - QLoRA

Model Details

Usage

Training Details

azeddinShr/
llava-onevision-weather-qlora