README.md

---
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**

[![AMD](https://img.shields.io/badge/AMD-MI300X-ed1c24?logo=amd&logoColor=white)](https://www.amd.com/en/products/accelerators/instinct/mi300x.html)
[![ROCm](https://img.shields.io/badge/ROCm-7.2-ed1c24)](https://rocm.docs.amd.com/)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](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.