HIKARI-Antares-8B-SkinCaption-STS-LoRA

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🔌 Model Type: LoRA Adapter

This is a LoRA adapter (~1.2 GB) — it must be loaded on top of the base model Qwen/Qwen3-VL-8B-Thinking.

✅ Advantage: Lightweight — download only ~1.2 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-Antares-8B-SkinCaption-STS (~17 GB)

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What is this adapter?

LoRA adapter for HIKARI-Antares-8B-SkinCaption-STS — Clinical caption generation with Selective Token Supervision ablation (research). Metric: BLEU-4: 0.61 (collapsed).

⚠️ Research artifact documenting STS training collapse. Output quality is poor. See HIKARI-Vega-8B-SkinCaption-Fused for production use.

See the full model card at E27085921/HIKARI-Antares-8B-SkinCaption-STS for complete details, usage examples, and performance comparison.

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Usage

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 (~1.2 GB)
model = PeftModel.from_pretrained(base, "E27085921/HIKARI-Antares-8B-SkinCaption-STS-LoRA")
processor = AutoProcessor.from_pretrained("E27085921/HIKARI-Antares-8B-SkinCaption-STS-LoRA", trust_remote_code=True)

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

For complete inference examples including vLLM and SGLang production code, see: E27085921/HIKARI-Antares-8B-SkinCaption-STS

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📄 Citation

@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)