---
license: apache-2.0
base_model: Qwen/Qwen3.5-9B
tags:
  - oncology
  - medical
  - lora
  - peft
  - qwen3
  - amd
  - rocm
  - mi300x
  - clinical
  - fine-tuned
datasets:
  - MaximoLopezChenlo/OncoAgent-Clinical-266K
language:
  - en
  - es
pipeline_tag: text-generation
library_name: peft
---

# 🧬 OncoAgent v1.0 — 9B (Tier 1)

**QLoRA Fine-tuned LoRA Adapter for Clinical Oncology Triage**

[![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** · Trained on AMD Instinct™ MI300X · ROCm 7.2

## Model Description

OncoAgent v1.0 9B is a **QLoRA fine-tuned LoRA adapter** built on top of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B), specialized for **clinical oncology triage and treatment recommendation**.

This is the **Tier 1 (fast triage)** model in the OncoAgent multi-agent system, optimized for:
- Rapid cancer type classification and routing
- Clinical entity extraction (symptoms, staging, biomarkers)
- First-pass treatment recommendations based on NCCN/ESMO guidelines

## Training Details

| Parameter | Value |
|---|---|
| **Base Model** | Qwen/Qwen3.5-9B |
| **Method** | QLoRA (4-bit NormalFloat4) |
| **Framework** | Unsloth + PEFT + TRL |
| **Hardware** | AMD Instinct™ MI300X (192GB HBM3) |
| **Software** | ROCm 7.2 · PyTorch 2.3+ |
| **LoRA Rank** | 32 |
| **LoRA Alpha** | 32 |
| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| **Training Samples** | 240,168 (+ 26,686 eval) |
| **Max Sequence Length** | 2,048 tokens |
| **Batch Size** | 8 (gradient accumulation: 2 → effective: 16) |
| **Learning Rate** | 2e-4 (cosine schedule) |
| **Epochs** | 1 |
| **Precision** | BF16 (native MI300X) |
| **Seed** | 42 (reproducible) |

## Dataset

Trained on [MaximoLopezChenlo/OncoAgent-Clinical-266K](https://huggingface.co/datasets/MaximoLopezChenlo/OncoAgent-Clinical-266K), a curated oncology dataset combining:

- **PMC-Patients** — Real clinical case presentations
- **PubMedQA** — Evidence-based medical Q&A
- **OncoCoT** — Chain-of-thought oncology reasoning (synthetic)
- **NCCN/ESMO Guidelines** — Structured guideline extracts

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3.5-9B",
    device_map="auto",
    torch_dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")

# Load LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    "MaximoLopezChenlo/OncoAgent-v1.0-9B",
)

# Inference
messages = [
    {"role": "system", "content": "You are a clinical oncology specialist."},
    {"role": "user", "content": "55yo female, Grade 1 endometrioid adenocarcinoma..."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## vLLM Deployment (AMD MI300X)

```bash
# Serve with vLLM on ROCm
python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen3.5-9B \
    --enable-lora \
    --lora-modules oncoagent=MaximoLopezChenlo/OncoAgent-v1.0-9B \
    --dtype bfloat16 \
    --tensor-parallel-size 1 \
    --gpu-memory-utilization 0.45
```

## Architecture

OncoAgent v1.0 9B serves as the **Tier 1** model in a dual-tier architecture:

```
Clinical Case → Router → [Tier 1: 9B] → Specialist → Critic → Output
                    ↓
              (Complex cases)
                    ↓
              [Tier 2: 27B] → Specialist → Critic → Output
```

## Links

- 🔗 **Demo:** [HF Space](https://huggingface.co/spaces/MaximoLopezChenlo/OncoAgent)
- 🔗 **GitHub:** [maximolopezchenlo-lab/OncoAgent](https://github.com/maximolopezchenlo-lab/OncoAgent)
- 🔗 **Tier 2 Model:** [OncoAgent-v1.0-27B](https://huggingface.co/MaximoLopezChenlo/OncoAgent-v1.0-27B)
- 🔗 **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 adapter is for **research and educational purposes only**. Not intended for direct clinical use without professional medical oversight.