Instructions to use DuanYi/R3LM_K562 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuanYi/R3LM_K562 with Transformers:

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

pipe = pipeline("text-generation", model="DuanYi/R3LM_K562")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DuanYi/R3LM_K562")
model = AutoModelForCausalLM.from_pretrained("DuanYi/R3LM_K562", device_map="auto")
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 Settings

vLLM

How to use DuanYi/R3LM_K562 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuanYi/R3LM_K562"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuanYi/R3LM_K562",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuanYi/R3LM_K562

SGLang

How to use DuanYi/R3LM_K562 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 "DuanYi/R3LM_K562" \
    --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": "DuanYi/R3LM_K562",
		"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 "DuanYi/R3LM_K562" \
        --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": "DuanYi/R3LM_K562",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DuanYi/R3LM_K562 with Docker Model Runner:
```
docker model run hf.co/DuanYi/R3LM_K562
```

R3LM_K562 / README.md

DuanYi

Upload R3LM_K562 Stage-I checkpoint

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metadata

license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
  - biology
  - genomics
  - dna
  - enhancer
  - chain-of-thought
  - qwen3
library_name: transformers
pipeline_tag: text-generation

R3LM — K562

Load model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DuanYi/R3LM_K562"

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

Citation

@inproceedings{Duan2026Biological,
  author    = {Yi Duan and Zhao Yang and Jiwei Zhu and Ying Ba and Chuan Cao and Bing Su},
  title     = {Biological Reasoning-Informed Regression for Interpretable Regulatory {DNA} Activity Prediction},
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 2026)},
  year      = {2026},
  doi       = {10.1145/3770855.3818836},
}

License

Apache 2.0 — see R3LM LICENSE.