asd-interpreter-merged

Clinical language interpreter for ASD fMRI connectivity reports.
Fine-tuned from Qwen/Qwen2.5-7B-Instruct on AMD MI300X (ROCm 7.0) using QLoRA, then merged to a single fp16 checkpoint.

Used live in the BrainConnect-ASD Space to generate patient-facing clinical summaries from gradient saliency scores produced by a 20-model LOSO GCN ensemble.

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Model Details

Field	Value
Base model	Qwen/Qwen2.5-7B-Instruct
Fine-tuning method	QLoRA (r=16, α=32, target: q/v projections)
Training hardware	AMD MI300X · ROCm 7.0 · DigitalOcean
Parameters	8B (merged, fp16)
Context length	4096 tokens
License	Apache 2.0

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What It Does

Given a structured prompt containing:

• Ensemble ASD probability p(ASD)
• Per-model predictions from 20 LOSO site-blind GCN models
• Network-level gradient saliency scores (7 Yeo networks: DMN, Salience, Frontoparietal, etc.)

The model outputs a clinical connectivity summary with:

1. Overall impression and confidence level
2. Which brain networks drove the prediction and why
3. Site-invariance assessment (20/20 model consensus signals robustness)
4. Recommended next steps for clinical review

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Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Yatsuiii/asd-interpreter-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

prompt = """You are a clinical neuroscience AI. Write a concise clinical connectivity summary.

Patient data:
- p(ASD) = 0.847 (ensemble mean across 20 site-blind models)
- Model consensus: 17/20 models predict ASD
- Top network saliency: DMN=0.0041, Salience=0.0038, Frontoparietal=0.0029

Write a 3-paragraph clinical summary."""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=400, temperature=0.3, do_sample=True)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

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Training Details

• Dataset: Synthetic clinical summaries generated from ABIDE I gradient saliency outputs, manually curated for clinical tone and factual grounding
• Fine-tuning: QLoRA via peft + trl SFTTrainer
• Hardware: AMD MI300X (192GB HBM3), ROCm 7.0, PyTorch 2.5.1+rocm6.2
• Epochs: 3 · Batch size: 4 · LR: 2e-4 · Warmup: 50 steps
• Merge: LoRA adapter merged into base weights with peft.merge_and_unload()

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Integration

This model runs as a vLLM endpoint (served via rocm/vllm) and is queried by the BrainConnect-ASD Gradio Space after every inference run. If the vLLM server is unavailable, the Space falls back to a cached demo report.

Space → GCN ensemble inference → gradient saliency → structured prompt → this model → clinical report

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Limitations

• Trained on synthetic data derived from ABIDE I — not validated on real clinical populations
• Not a medical device. Outputs are for research and demonstration purposes only.
• Performance degrades on atlases other than CC200 (saliency prompt was optimized for CC200 → Yeo-7 mapping)

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Citation

If you use this model or the BrainConnect-ASD pipeline, please cite:

BrainConnect-ASD — AMD Developer Hackathon 2026
Raghav Aryen · lablab.ai · AMD MI300X
https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/BrainConnect-ASD