Instructions to use Joesh1/Onca-1.0-9B-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Joesh1/Onca-1.0-9B-Int4 with Transformers:

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

pipe = pipeline("text-generation", model="Joesh1/Onca-1.0-9B-Int4")
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-Int4")
model = AutoModelForCausalLM.from_pretrained("Joesh1/Onca-1.0-9B-Int4")
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-Int4 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-Int4"
# 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-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Joesh1/Onca-1.0-9B-Int4

SGLang

How to use Joesh1/Onca-1.0-9B-Int4 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-Int4" \
    --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-Int4",
		"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-Int4" \
        --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-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Joesh1/Onca-1.0-9B-Int4 with Docker Model Runner:
```
docker model run hf.co/Joesh1/Onca-1.0-9B-Int4
```

Onca 1.0 9B Int4

Model Summary

This repository contains the 4-bit release of Onca 1.0, an open 9B pancreatic cancer language model specialized for:

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

This variant is intended for more memory-constrained inference setups that still need access to the same Onca 1.0 task scope.

Base Model

Onca 1.0 is fine-tuned from Jackrong/Qwopus3.5-9B-v3, a Qwen3.5-derived 9B dense reasoning model. This repository packages the 4-bit quantized Onca 1.0 checkpoint with a bitsandbytes NF4 configuration.

Variant Details

Quantization: 4-bit
Quantization method: bitsandbytes
Quantization type: NF4 with double quantization
Recommended use: memory-constrained inference in compatible transformers environments

For the main reference release, use JosephKBS/onca-1.0-9B.

Training Scope

The underlying model was trained on 37,364 prepared rows from openly available sources across four pancreatic-cancer task families:

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

Intended Use

This release is intended for research, benchmarking, and local experimentation in oncology-focused NLP workflows where lower memory usage matters.

Out-of-Scope Use

This model is not intended for direct clinical care, autonomous decision-making, or unsupervised patient-facing use. It is a research checkpoint and does not replace clinician judgment.

Evaluation Summary

The evaluation reported for Onca 1.0 in the companion manuscript includes:

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

These values summarize the Onca 1.0 model family and should not be read as a separate benchmark guarantee for every hardware/runtime combination used with the 4-bit checkpoint.

Limitations

Specialized for PDAC and adjacent oncology tasks rather than general medicine
Public-source training data improve reproducibility but do not fully capture institutional documentation style
Quantized inference behavior depends on runtime support, kernels, and available hardware
Research-only checkpoint; not clinically validated

Usage

This repository includes a bitsandbytes 4-bit configuration in config.json. In a compatible environment, a standard loading pattern is:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Joesh1/onca-1.0-9B-Int4"

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

Please ensure your local environment supports bitsandbytes 4-bit inference before deployment.

Files in This Repository

model.safetensors: quantized model weights
config.json: architecture and quantization configuration
generation_config.json: default generation settings
tokenizer.json and tokenizer_config.json: tokenizer files
chat_template.jinja: chat formatting template

License

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

Citation

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

@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, together with the public institutions, benchmarks, and open data contributors that made this release possible.