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E27085921/
HIKARI-Sirius-8B-SkinDx-RAG-LoRA

Name: HIKARI-Sirius-8B-SkinDx-RAG-LoRA
Availability: InStock

Qwen/Qwen3-VL-8B-Thinking

adapter

apache-2.0

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HIKARI-Sirius-8B-SkinDx-RAG-LoRA

🔌 Model Type: LoRA Adapter

This is a LoRA adapter (~1.1 GB) — it must be loaded on top of the base model Qwen/Qwen3-VL-8B-Thinking.

✅ Advantage: Lightweight — download only ~1.1 GB instead of ~17 GB.

⚠️ Requirement: You must separately load Qwen/Qwen3-VL-8B-Thinking (base model, ~17 GB) first.

💾 If you prefer a standalone ready-to-use model, see the merged version: E27085921/HIKARI-Sirius-8B-SkinDx-RAG (~17 GB)

What is this adapter?

LoRA adapter for HIKARI-Sirius-8B-SkinDx-RAG — 10-class skin disease diagnosis with RAG-in-Training (best disease model). Metric: Acc 85.86% ⭐.

This is the LoRA adapter for the best disease diagnosis model in the HIKARI family.

See the full model card at E27085921/HIKARI-Sirius-8B-SkinDx-RAG for complete details, usage examples, and performance comparison.

Usage

python

from peft import PeftModel
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
import torch
from PIL import Image

# Step 1: Load base model (Qwen3-VL-8B-Thinking, ~17 GB)
base = Qwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-8B-Thinking",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

# Step 2: Apply LoRA adapter (~1.1 GB)
model = PeftModel.from_pretrained(base, "E27085921/HIKARI-Sirius-8B-SkinDx-RAG-LoRA")
processor = AutoProcessor.from_pretrained("E27085921/HIKARI-Sirius-8B-SkinDx-RAG-LoRA", trust_remote_code=True)

# Step 3: Inference — see full examples at E27085921/HIKARI-Sirius-8B-SkinDx-RAG
image = Image.open("skin_lesion.jpg").convert("RGB")

For complete inference examples including vLLM and SGLang production code, see: E27085921/HIKARI-Sirius-8B-SkinDx-RAG

📄 Citation

bibtex

@misc{hikari2026,
  title  = {HIKARI: RAG-in-Training for Skin Disease Diagnosis
            with Cascaded Vision-Language Models},
  author = {Watin Promfiy and Pawitra Boonprasart},
  year   = {2026},
  institution = {King Mongkut's Institute of Technology Ladkrabang,
                 Department of Information Technology, Bangkok, Thailand}
}

Made with ❤️ at King Mongkut's Institute of Technology Ladkrabang (KMITL)