---
base_model: Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
language:
- en
tags:
- oncology
- medical
- pancreatic-cancer
- qwen
- sft
- research
---

<p align="center">
  <img src="./assets/onca-logo-horizontal.svg" alt="Onca logo" width="520">
</p>

# ONCA 1.5

> Open pancreatic cancer language model for four research-oriented workflows: trial screening, clinical reasoning, pathology extraction, and variant evidence interpretation.

ONCA 1.5 is the current continued-SFT release in the ONCA model line. It builds on `Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled` and follows the project direction established in [ONCA 1.0](https://huggingface.co/Joesh1/onca-1.0-9B): focus on pancreatic cancer workflows, open-data training assets, and practical structured prompting for clinical research use.

For ONCA 1.5, the training emphasis was to improve all four existing task families at once, with particular attention to parser-safe trial screening, preserved pathology extraction performance, concise clinical reasoning, and stronger variant evidence handling.

## At a Glance

| Field | Value |
| --- | --- |
| Release | BF16 reference release |
| Base model | `Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled` |
| Architecture | Qwen3.5-class causal LM (`Qwen3_5ForCausalLM`) |
| Context window | 262,144 tokens |
| Training recipe | Continued SFT |
| Domain focus | Pancreatic cancer and oncology-adjacent research workflows |

## What This Release Is Good At

- Criterion-aware pancreatic cancer trial screening with explicit eligibility framing.
- Concise oncology reasoning and question answering for research workflows.
- Structured pathology abstraction with field-oriented prompting.
- Variant evidence interpretation with oncology context and uncertainty signaling.

## What It Is Specialized For

This model is specialized for pancreatic cancer and oncology-adjacent research workflows rather than broad general-purpose medical chat. It works best when the task is tightly scoped and the target output format is explicit, especially for:

- pancreatic cancer trial eligibility review
- pathology report abstraction into structured fields
- concise oncology reasoning for case discussion
- variant evidence interpretation with uncertainty signaling

Example: pancreatic cancer trial-screening workflow

```python
prompt = """
Task: Pancreatic cancer trial screening.

Patient summary:
- 63-year-old with metastatic pancreatic ductal adenocarcinoma
- ECOG 1
- Prior gemcitabine plus nab-paclitaxel
- Bilirubin 0.9 mg/dL
- No active infection

Trial criteria:
- Histologically confirmed metastatic pancreatic adenocarcinoma
- ECOG 0-1
- Progression after 1 prior systemic regimen
- Adequate marrow and hepatic function
- Exclude uncontrolled infection

Return:
1. Eligibility label
2. Criterion-by-criterion reasoning
3. Missing information
"""

messages = [
    {"role": "system", "content": "You are Onca, a pancreatic cancer clinical research assistant."},
    {"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
    temperature=0.2,
)
answer = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(answer, skip_special_tokens=True))
```

## Release Note

This is the main reference checkpoint in the ONCA 1.5 family and the best starting point if you want the least altered release.

## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Joesh1/onca-1.5"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
```

Use the included tokenizer and chat template for best results:

```python
messages = [
    {"role": "system", "content": "You are Onca, a pancreatic cancer clinical research assistant."},
    {"role": "user", "content": "Extract tumor grade, margin status, pT, and pN from this pathology report as JSON."},
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
    temperature=0.2,
)
answer = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(answer, skip_special_tokens=True))
```

## Prompting Tips

- Use the included chat template and ask for a specific output structure.
- For extraction tasks, request exact JSON keys or field names.
- For screening tasks, include both the patient summary and the trial criteria.
- Ask the model to state uncertainty and missing information explicitly.

## Training Scope

The active ONCA 1.5 continued-SFT stack uses openly available data only. The data mixture keeps all four task families in play, with weighting geared toward variant evidence repair and trial-screening stability while guarding against pathology regression.

| Task family | Active rows | Train | Val | Test |
| --- | ---: | ---: | ---: | ---: |
| Trial Screening | 12,137 | 10,921 | 608 | 608 |
| Clinical Reasoning | 3,496 | 3,146 | 174 | 176 |
| Pathology Extraction | 7,642 | 6,583 | 414 | 405 |
| Variant Evidence | 2,432 | 2,191 | 116 | 125 |
| Total | 25,707 | 22,841 | 1,312 | 1,314 |

Initial task weights in the active prepare stack are 27% trial screening, 18% clinical reasoning, 27% pathology extraction, and 28% variant evidence.

## Benchmarks

Benchmark tables and comparative evaluation plots will be added later.

## Repository Contents

- `model-*.safetensors`: sharded weights for this release.
- `model.safetensors.index.json`: shard map for the checkpoint files.
- `config.json`: architecture config and, for quantized variants, quantization metadata.
- `generation_config.json`: default generation settings.
- `tokenizer.json` and `tokenizer_config.json`: tokenizer assets.
- `chat_template.jinja`: chat formatting template for inference.
- `assets/onca-logo-horizontal.svg`: ONCA family logo used at the top of the model card.

## Related Releases

- `onca-1.5`: BF16 reference release (this page).
- `onca-1.5-8bit`: 8-bit merged release.
- `onca-1.5-4bit`: 4-bit merged release.

## Limitations and Safety

- This is a research model and not a clinical decision system.
- Outputs should be reviewed by qualified experts before any real-world use.
- The model is specialized for pancreatic cancer and oncology-adjacent workflows rather than broad general medicine.
- Variant evidence training includes broader oncology signal, but the intended framing of the model remains pancreatic cancer research support.
- Quantized releases are convenience variants and may behave slightly differently from the BF16 reference checkpoint.

## Citation

A formal ONCA 1.5 citation block will be added with the accompanying manuscript. Until then, please cite the model repository and version used in your work.

## Acknowledgments

ONCA 1.5 continues the ONCA project lineage from ONCA 1.0 and builds on the Qwen/Qwopus ecosystem plus the open-data contributors whose datasets made this release possible.