Instructions to use yj12869741/TA-OPD-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yj12869741/TA-OPD-27B with Transformers:

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

pipe = pipeline("text-generation", model="yj12869741/TA-OPD-27B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("yj12869741/TA-OPD-27B")
model = AutoModelForMultimodalLM.from_pretrained("yj12869741/TA-OPD-27B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yj12869741/TA-OPD-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yj12869741/TA-OPD-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yj12869741/TA-OPD-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yj12869741/TA-OPD-27B

SGLang

How to use yj12869741/TA-OPD-27B 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 "yj12869741/TA-OPD-27B" \
    --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": "yj12869741/TA-OPD-27B",
		"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 "yj12869741/TA-OPD-27B" \
        --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": "yj12869741/TA-OPD-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yj12869741/TA-OPD-27B with Docker Model Runner:
```
docker model run hf.co/yj12869741/TA-OPD-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TA-OPD-27B

English | 简体中文

TA-OPD-27B is the model released with the paper “Exploring how large language models can empower sequence-based omics tasks”.

TA-OPD-27B is a model for sequence-based omics tasks, developed by post-training Qwen3.5-27B with tool-augmented on-policy distillation (TA-OPD) on TA-OPD-10K.

During training, the teacher receives agent-generated answers containing evidence from biological tools and databases. The student learns from the teacher's feedback on its own responses, without access to this additional information. At inference, the model takes a sequence and a question as input; the training-time agent and tools are not required.

Model details


Base model	Qwen3.5-27B
Training dataset	TA-OPD-10K
Weight format	BF16 Safetensors
License	Apache 2.0

Use the supplied tokenizer and chat template when preparing inputs, following the inference workflow for Qwen3.5-27B.

OmicsBench results

Evaluated on 1,160 questions with five inference seeds, temperature 1.0 and a maximum output length of 16,384 tokens. Values are mean ± sample standard deviation after removing the highest and lowest of the five scores independently for each metric. All metrics are multiplied by 100.

Predictive performance

EMP: epigenetic mark prediction; Prom: promoter prediction; TFBS: transcription factor binding site prediction; Mod: RNA modification prediction; ncRNA: non-coding RNA family classification; EC: enzyme function prediction.

Model	EMP MCC	Prom MCC	TFBS MCC	Mod AUC	ncRNA Acc	EC Fmax
Qwen3.5-27B	-5.69 ± 1.42	6.10 ± 3.64	16.29 ± 1.70	50.83 ± 2.19	5.12 ± 0.47	0.90 ± 0.02
TA-OPD-27B	28.35 ± 1.27	39.22 ± 0.25	23.18 ± 1.14	53.52 ± 2.19	19.22 ± 0.54	1.84 ± 0.70

Rubric Recall (%)

Model	EMP	Prom	TFBS	Mod	ncRNA	EC	Avg Recall
Qwen3.5-27B	7.42 ± 0.16	19.40 ± 0.95	24.30 ± 0.81	2.12 ± 0.18	3.58 ± 0.36	0.36 ± 0.24	9.63 ± 0.36
TA-OPD-27B	35.69 ± 0.38	46.80 ± 0.41	36.61 ± 1.58	22.82 ± 1.44	12.48 ± 0.51	0.16 ± 0.16	25.66 ± 0.69

Judge: DeepSeek-V3.2. Avg Recall is the unweighted mean of the six task-level recalls, computed within each seed before trimming.

Project: OmicsBench.

Downloads last month: 174

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for yj12869741/TA-OPD-27B

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Qwen/Qwen3.5-27B

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(315)

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Quantizations

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TA-OPD-27B