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license: apache-2.0
base_model: Qwen/Qwen3.6-27B
tags:
- oncology
- medical
- qwen3
- amd
- rocm
- mi300x
- clinical
- multi-agent
datasets:
- MaximoLopezChenlo/OncoAgent-Clinical-266K
language:
- en
- es
pipeline_tag: text-generation
---
# 🧬 OncoAgent v1.0 — 27B (Tier 2)
**Advanced Reasoning Model for Complex Oncology Cases**
[](https://www.amd.com/en/products/accelerators/instinct/mi300x.html)
[](https://rocm.docs.amd.com/)
[](https://opensource.org/licenses/Apache-2.0)
> **AMD Developer Hackathon 2026** · Deployed on AMD Instinct™ MI300X · ROCm 7.2
## Model Description
OncoAgent v1.0 27B is the **Tier 2 (advanced reasoning)** model in the OncoAgent multi-agent oncology triage system. It leverages the full capacity of [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) with a specialized clinical oncology system prompt and RAG-grounded inference.
This model is activated for **complex cases** that require deeper reasoning:
- Multi-line therapy planning (Stage III/IV cancers)
- Rare tumor types with limited guideline coverage
- Cases requiring cross-guideline synthesis (NCCN + ESMO)
- Differential diagnosis with conflicting biomarkers
## Architecture Role
In the OncoAgent dual-tier architecture, the 27B model is the "deep thinker":
```
Clinical Case → Router Agent
│
├── Simple/Common → [Tier 1: 9B LoRA] → Fast Triage
│
└── Complex/Rare → [Tier 2: 27B] → Deep Analysis
│
↓
Specialist Agent
│
↓
Critic (Reflexion Loop)
│
↓
Validated Recommendation
```
### Routing Criteria (Tier 1 → Tier 2 Escalation)
| Trigger | Example |
|---|---|
| Stage III/IV disease | Metastatic breast cancer |
| Rare tumor types | Merkel cell carcinoma |
| Multi-drug regimens | Combination immunotherapy |
| Conflicting data | HER2-low with BRCA mutation |
| Low RAG confidence | Cross-encoder score < 0.70 |
## Configuration
This model uses the base Qwen3.6-27B with OncoAgent's specialized system prompt and Corrective RAG pipeline. The configuration includes:
| Parameter | Value |
|---|---|
| **Base Model** | Qwen/Qwen3.6-27B |
| **Precision** | BF16 (native MI300X Matrix Cores) |
| **Context Window** | 32,768 tokens |
| **Serving Engine** | vLLM with PagedAttention |
| **GPU Memory** | ~55% of MI300X 192GB HBM3 |
| **Tensor Parallelism** | 1 (single MI300X) |
## System Prompt
```
You are OncoAgent-Specialist, a board-certified oncologist AI assistant.
You provide evidence-based treatment recommendations grounded EXCLUSIVELY
in the retrieved clinical guidelines (NCCN/ESMO).
RULES:
1. NEVER invent treatments. If the evidence is not in the provided context,
state: "Información no concluyente en las guías provistas."
2. Always cite the guideline source (NCCN/ESMO) and evidence category.
3. Structure your response with: Clinical Summary, Diagnostic Findings,
Treatment Recommendation, and Evidence Level.
4. Consider comorbidities, contraindications, and patient-specific factors.
5. For Stage IV cases, include discussion of clinical trial eligibility.
```
## vLLM Deployment (AMD MI300X)
```bash
# Serve Tier 2 on MI300X
python -m vllm.entrypoints.openai.api_server \
--model Qwen/Qwen3.6-27B \
--dtype bfloat16 \
--tensor-parallel-size 1 \
--gpu-memory-utilization 0.55 \
--max-model-len 32768 \
--port 8001
```
### Dual-Model Deployment
```bash
# Run both tiers simultaneously on MI300X (192GB HBM3)
# Tier 1 (9B): ~45% GPU memory → Port 8000
# Tier 2 (27B): ~55% GPU memory → Port 8001
bash deploy/start_vllm.sh both
```
## Safety Features
OncoAgent v1.0 27B operates within a multi-layered safety framework:
1. **Anti-Hallucination Policy** — Model is constrained to RAG-retrieved context only
2. **Reflexion Critic Loop** — Output is validated by a dedicated Critic agent
3. **Diagnostic Rigor Check** — Treatment recommendations require confirmed pathology
4. **PHI Sanitization** — Zero patient health information in logs
5. **HITL Gate** — Stage IV cases can trigger human-in-the-loop review
## Links
- 🔗 **Demo:** [HF Space](https://huggingface.co/spaces/MaximoLopezChenlo/OncoAgent)
- 🔗 **GitHub:** [maximolopezchenlo-lab/OncoAgent](https://github.com/maximolopezchenlo-lab/OncoAgent)
- 🔗 **Tier 1 Model:** [OncoAgent-v1.0-9B](https://huggingface.co/MaximoLopezChenlo/OncoAgent-v1.0-9B)
- 🔗 **Dataset:** [OncoAgent-Clinical-266K](https://huggingface.co/datasets/MaximoLopezChenlo/OncoAgent-Clinical-266K)
## Citation
```bibtex
@misc{oncoagent2026,
title={OncoAgent: Multi-Agent Oncology Triage System},
author={Lopez Chenlo, Maximo},
year={2026},
howpublished={AMD Developer Hackathon 2026},
url={https://github.com/maximolopezchenlo-lab/OncoAgent}
}
```
## License
Apache 2.0 — This model configuration is for **research and educational purposes only**. Not intended for direct clinical use without professional medical oversight.
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