---
language: en
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - qwen2
  - neuroscience
  - ASD
  - fMRI
  - clinical-nlp
  - fine-tuned
  - amd-mi300x
  - rocm
pipeline_tag: text-generation
---

# 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](https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/BrainConnect-ASD) to generate patient-facing clinical summaries from gradient saliency scores produced by a 20-model LOSO GCN ensemble.

---

## 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 |

---

## 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

---

## Usage

```python
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))
```

---

## 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()`

---

## 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
```

---

## 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)

---

## 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
```