lablab-ai-amd-developer-hackathon
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asd-interpreter-merged

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 ---
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 ---
+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 2025
+Raghav Aryen · lablab.ai · AMD MI300X
+https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/BrainConnect-ASD
+```