Instructions to use OpenOneRec/OneRec-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenOneRec/OneRec-8B with Transformers:

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

pipe = pipeline("text-generation", model="OpenOneRec/OneRec-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenOneRec/OneRec-8B")
model = AutoModelForCausalLM.from_pretrained("OpenOneRec/OneRec-8B")
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 OpenOneRec/OneRec-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenOneRec/OneRec-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenOneRec/OneRec-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenOneRec/OneRec-8B

SGLang

How to use OpenOneRec/OneRec-8B 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 "OpenOneRec/OneRec-8B" \
    --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": "OpenOneRec/OneRec-8B",
		"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 "OpenOneRec/OneRec-8B" \
        --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": "OpenOneRec/OneRec-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenOneRec/OneRec-8B with Docker Model Runner:
```
docker model run hf.co/OpenOneRec/OneRec-8B
```

OpenOneRec commited on Jan 5

Commit

29f95b3

verified ·

1 Parent(s): fce28d6

Update README.md

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@@ -40,6 +40,7 @@ To address this, we introduce a unified framework that comprises:
 * **[2026.1.1]** 📑 The technical report has been released.
 * **[2026.1.1]** 🎉 **OneRec-Foundation** models (1.7B, 8B) are now available on Hugging Face!
 * **[2026.1.1]** 🚀 **RecIF-Bench** dataset and evaluation scripts are open-sourced.
 ## 📊 RecIF-Bench
@@ -158,6 +159,8 @@ text = tokenizer.apply_chat_template(
 model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 # conduct text completion
 generated_ids = model.generate(
     **model_inputs,
     max_new_tokens=32768

 * **[2026.1.1]** 📑 The technical report has been released.
 * **[2026.1.1]** 🎉 **OneRec-Foundation** models (1.7B, 8B) are now available on Hugging Face!
 * **[2026.1.1]** 🚀 **RecIF-Bench** dataset and evaluation scripts are open-sourced.
+* **[2026.1.5]** 🔡 **OneRec-Tokenizer** is open-sourced to support SID generation for new domains.
 ## 📊 RecIF-Bench
 model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 # conduct text completion
+# Note: In our experience, default decoding settings may be unstable for small models.
+# For 1.7B, we suggest: top_p=0.95, top_k=20, temperature=0.75 (during 0.6 to 0.8)
 generated_ids = model.generate(
     **model_inputs,
     max_new_tokens=32768