ONCA 2.0
Summary
ONCA 2.0 is an open oncology language model for trial screening, clinical reasoning, pathology extraction, and variant evidence interpretation. It builds on google/gemma-4-12B-it with continued supervised fine-tuning on a provenance-labeled oncology corpus while retaining the four-task ONCA 1.5 evaluation contract.
This repository contains the merged BF16 reference checkpoint exported on 2026-06-14. The model is specialized for pancreatic cancer and oncology-adjacent research workflows and performs best with tightly scoped prompts and explicit output formats.
At a Glance
| Field | Value |
|---|---|
| Release | BF16 reference release |
| Base model | google/gemma-4-12B-it |
| Architecture | Gemma 4 unified 12B (Gemma4UnifiedForConditionalGeneration) |
| Context window | 262,144 tokens |
| Training recipe | Continued SFT with merged adapter export |
| Domain focus | Pancreatic cancer and oncology research |
| Weights | Seven safetensors shards |
| Task | Headline metric | ONCA 2.0 |
|---|---|---|
| Trial Screening | Accuracy | 0.8240 |
| Clinical Reasoning | Outcome-label accuracy | 0.6761 |
| Pathology Extraction | Overall field exact match | 0.4634 |
| Variant Evidence | Clinical-significance macro-F1 | 0.5427 |
ONCA 2.0 improves over ONCA 1.5 on trial screening, clinical reasoning, and variant evidence. Pathology extraction remains its primary weakness.
Quick Start
Use a recent Transformers release with Gemma 4 support.
from transformers import AutoModelForMultimodalLM, AutoProcessor
model_id = "Joesh1/onca-2.0-12B"
processor = AutoProcessor.from_pretrained(model_id)
tokenizer = processor.tokenizer
model = AutoModelForMultimodalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
# Example: criterion-aware trial screening with structured output
messages = [{
"role": "user",
"content": (
"Patient: metastatic pancreatic adenocarcinoma; ECOG 1; "
"no prior metastatic-line therapy. Trial: metastatic PDAC, ECOG 0-1, "
"no prior metastatic-line therapy. Return JSON with keys eligible, "
"reason, and missing_information."
),
}]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=160, do_sample=False)
answer = tokenizer.decode(
outputs[0, inputs["input_ids"].shape[1]:],
skip_special_tokens=True,
)
print(answer)
Use the included tokenizer and chat template. For structured workflows, request exact fields, provide all relevant criteria, ask for explicit uncertainty, and prefer deterministic decoding.
Training Scope
The active training corpus contains 25,302 examples. Validation, test, and primary-holdout sets retain the ONCA 1.5 four-task evaluation contract.
| Task family | Train | Original | Generated | Val | Test | Holdout |
|---|---|---|---|---|---|---|
| Trial Screening | 10,921 | 10,921 | 0 | 608 | 608 | 608 |
| Clinical Reasoning | 3,647 | 3,146 | 501 | 174 | 176 | 176 |
| Pathology Extraction | 4,559 | 333 | 4,226 | 410 | 400 | 400 |
| Variant Evidence | 6,175 | 2,191 | 3,984 | 116 | 125 | 125 |
| Total | 25,302 | 16,591 | 8,711 | 1,308 | 1,309 | 1,309 |
Related Releases
onca-2.0-12B: BF16 reference release (this page).onca-2.0-12B-INT8: 8-bit BitsAndBytes release.onca-2.0-12B-INT4: 4-bit BitsAndBytes release.onca-2.0-12B-GGUF: llama.cpp-compatible GGUF collection.
Limitations
- This is a research model, not a clinical decision system.
- Outputs require review by qualified experts before real-world use.
- Structured or parser-valid output does not guarantee factual correctness.
- The model is specialized for pancreatic cancer and oncology-adjacent workflows rather than broad general medicine.
- Performance varies by task; pathology extraction remains the weakest evaluated area.
Citation
A formal ONCA 2.0 citation will be added with the accompanying manuscript. Until then, cite this model repository and the exact version used.
Acknowledgements
ONCA 2.0 continues the ONCA project lineage and builds on Google Gemma and the open-data contributors whose datasets supported training and evaluation.