Instructions to use Joesh1/onca-1.0-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Joesh1/onca-1.0-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Joesh1/onca-1.0-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Joesh1/onca-1.0-9B")
model = AutoModelForCausalLM.from_pretrained("Joesh1/onca-1.0-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Joesh1/onca-1.0-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Joesh1/onca-1.0-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Joesh1/onca-1.0-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Joesh1/onca-1.0-9B

SGLang

How to use Joesh1/onca-1.0-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Joesh1/onca-1.0-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Joesh1/onca-1.0-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Joesh1/onca-1.0-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Joesh1/onca-1.0-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Joesh1/onca-1.0-9B with Docker Model Runner:
```
docker model run hf.co/Joesh1/onca-1.0-9B
```

onca-1.0-9B / README.md

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	---
	license: apache-2.0
	base_model:
	- Jackrong/Qwopus3.5-9B-v3
	tags:
	- oncology
	- pancreatic-cancer
	- pdac
	- clinical-nlp
	- medical-llm
	- text-generation
	- research
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	---

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

	# Onca 1.0 9B

	## Model Summary

	Onca 1.0 is an open 9B language model for pancreatic cancer clinical tasks. It is designed for four PDAC-relevant task families:

	- clinical trial screening
	- case-specific clinical reasoning
	- structured pathology report extraction
	- molecular variant evidence reasoning

	This release is the main FP16/BF16-compatible checkpoint intended as the reference Hugging Face release for the Onca 1.0 model family.

	## Base Model

	Onca 1.0 is fine-tuned from `Jackrong/Qwopus3.5-9B-v3`, a Qwen3.5-derived 9B dense reasoning model. The released checkpoint reflects task-focused supervised fine-tuning for pancreatic cancer workflows while preserving the underlying Qwen3.5-class architecture and tokenizer setup.

	## Training Scope

	The model was trained on 37,364 prepared rows from openly available sources. The multitask mixture covers:

	- trial eligibility screening
	- oncology clinical reasoning
	- CAP-aligned pathology abstraction
	- CIViC-style variant interpretation

	The project was built around an open-data, open-weight, single-workstation pipeline so the workflow can be audited and reproduced without private institutional corpora.

	## Intended Use

	Onca 1.0 is intended for:

	- research on oncology-focused language models
	- benchmarking PDAC-oriented clinical NLP workflows
	- prototyping structured extraction and screening pipelines
	- local experimentation in privacy-sensitive environments

	## Out-of-Scope Use

	Onca 1.0 is not intended for:

	- direct clinical care
	- autonomous treatment recommendations
	- unsupervised patient-facing use
	- deployment as a validated medical device or diagnostic system

	This is a research model and does not replace clinician judgment.

	## Evaluation Summary

	In the companion manuscript, Onca 1.0 was evaluated across 11 panels against Woollie-7B, CancerLLM-7B, OpenBioLLM-8B, and the unfine-tuned Qwopus base. Headline results reported in the draft include:

	- Trial Screening: 81.6 F1
	- Clinical Reasoning: 14.1 composite
	- Pathology Extraction: 30.5 field exact-match
	- PubMedQA Cancer: 68.3 macro-F1
	- PubMedQA: 66.5 macro-F1

	The strongest gains appear in workflow-proximal tasks such as trial review and pathology structuring. Variant evidence reasoning remains more difficult than the other task groups.

	## Limitations

	- The model is specialized for pancreatic cancer and oncology-adjacent workflows rather than general medicine.
	- Training data come from openly available sources rather than private institutional notes, which improves reproducibility but does not fully capture real-world documentation style.
	- Benchmark sample sizes for several panels are deliberately limited and should be interpreted with care.
	- Performance is uneven across task families and does not imply broad medical competence.

	## Usage

	This repository contains the main full-precision checkpoint files. A standard `transformers` loading pattern is:

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_id = "Joesh1/onca-1.0-9B"

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

	Inference formatting should follow the included tokenizer and chat template files in this repository.

	### Quick Chat Helper

	```python
	def run_onca(prompt, system_prompt="You are Onca 1.0, a pancreatic-cancer clinical research assistant."):
	messages = [
	{"role": "system", "content": system_prompt},
	{"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)
	with torch.no_grad():
	outputs = model.generate(
	**inputs,
	max_new_tokens=512,
	temperature=0.2,
	do_sample=False,
	)
	completion = outputs[0][inputs["input_ids"].shape[1]:]
	return tokenizer.decode(completion, skip_special_tokens=True)
	```

	### Example 1: Trial Screening

	```python
	prompt = """
	Task: Trial eligibility screening for pancreatic cancer.

	Patient summary:
	- 63-year-old with metastatic PDAC
	- Liver metastases present
	- ECOG 1
	- Prior gemcitabine plus nab-paclitaxel
	- Total bilirubin 0.9 mg/dL
	- ANC 2.4
	- Platelets 188
	- No active infection
	- No brain metastases

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

	Return:
	1. Eligibility label: eligible / ineligible / unclear
	2. Criterion-by-criterion reasoning
	3. Missing information, if any
	"""

	print(run_onca(prompt))
	```

	### Example 2: Clinical Reasoning

	```python
	prompt = """
	Task: Pancreatic cancer clinical reasoning.

	Case:
	A 58-year-old patient has borderline resectable PDAC in the pancreatic head.
	CA19-9 is elevated. ECOG is 0. Germline testing is pending. No distant metastases
	are seen on imaging.

	Please provide:
	1. A concise assessment
	2. A high-level management plan
	3. Key factors that could change the plan
	4. Important limitations or uncertainties

	Do not present this as medical advice. Keep it research-oriented.
	"""

	print(run_onca(prompt))
	```

	### Example 3: Pathology Extraction

	```python
	prompt = """
	Task: Structured pathology extraction.

	Extract the report into JSON with the following fields:
	specimen_type, primary_site, histology, tumor_grade, tumor_size_cm,
	margin_status, lymphovascular_invasion, perineural_invasion,
	lymph_nodes_examined, lymph_nodes_positive, pT, pN, pM,
	ajcc_stage, treatment_effect, tumor_focality, additional_findings

	Report:
	Whipple resection specimen showing moderately differentiated pancreatic ductal
	adenocarcinoma, 3.1 cm, centered in the pancreatic head. Tumor extends into
	peripancreatic soft tissue. All margins are negative; closest margin is 0.4 cm
	at the uncinate margin. Perineural invasion is present. Lymphovascular invasion
	is present. Sixteen lymph nodes examined, 3 positive for metastatic carcinoma.
	Pathologic stage: pT2 pN1. No distant metastasis identified in specimen.
	"""

	print(run_onca(prompt))
	```

	### Example 4: Variant Evidence Interpretation

	```python
	prompt = """
	Task: Variant evidence reasoning for pancreatic cancer.

	Variant:
	- Gene: BRCA2
	- Alteration: pathogenic loss-of-function variant
	- Tumor type: pancreatic ductal adenocarcinoma

	Return a JSON object with:
	- gene
	- alteration
	- disease
	- evidence_summary
	- therapeutic_implication
	- diagnostic_implication
	- prognostic_implication
	- evidence_direction
	- confidence

	Keep the answer concise and note uncertainty when evidence is incomplete.
	"""

	print(run_onca(prompt))
	```

	### Prompting Tips

	- Ask for a specific output format such as bullet points or JSON.
	- For extraction tasks, list the exact fields you want returned.
	- For screening tasks, provide both the patient summary and the trial criteria.
	- For reasoning tasks, request uncertainties and missing data explicitly.
	- Treat outputs as research artifacts that require expert review.

	## Files in This Repository

	- `model-00001-of-00004.safetensors` through `model-00004-of-00004.safetensors`: sharded model weights
	- `model.safetensors.index.json`: shard index
	- `config.json`: model architecture configuration
	- `generation_config.json`: default generation settings
	- `tokenizer.json` and `tokenizer_config.json`: tokenizer files
	- `chat_template.jinja`: chat formatting template

	## Related Variants

	Quantized releases are provided separately:

	- `JosephKBS/onca-1.0-9B-Int8`
	- `JosephKBS/onca-1.0-9B-Int4`

	## License

	This release is provided under the Apache 2.0 license. Users should also review the license and usage terms of the upstream base model and any referenced datasets or benchmarks.

	## Citation

	If you use Onca 1.0, please cite the accompanying manuscript when publicly available. A temporary reference is:

	```bibtex
	@misc{shim2026onca,
	title = {Onca: An Open 9B Language Model for Pancreatic Cancer Clinical Tasks},
	author = {Shim, Kwan Bo},
	year = {2026},
	note = {Preprint in preparation}
	}
	```

	## Acknowledgments

	This project builds on the work of the Qwen and Qwopus model developers, as well as the many institutions and open-data contributors who created and maintained the public datasets used in training and evaluation.