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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenOneRec/OneRec-8B-pro-pretrain"
# 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-pro-pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

OpenOneRec commited on Mar 5

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@@ -28,11 +28,8 @@ license_link: https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE
 ## 📖 OneRec-Foundation-Pretrain Models
-This repository contains the Pre-trained weights of the OneRec-Foundation series. These models serve as the generative recommendation base after Itemic-Text Alignment and Full-Parameter Co-Pretraining.
-## 🛠️ Usage & Extension
-These pre-trained weights are released to provide a foundational base for generative recommendation research.
-The models are specifically designed to serve as a starting point for further development. Users can leverage this checkpoint to perform Post-training or alignment tailored to their own downstream tasks, specific datasets, or custom conversational requirements.
-For technical details regarding the pre-training architecture, please refer to our Technical Report.

 ## 📖 OneRec-Foundation-Pretrain Models
+This repository provides the pre-trained weights of the OneRec-Foundation series, which has undergone Itemic-Text Alignment and Full-Parameter Co-Pretraining.
+We release this checkpoint to enable users to perform customized post-training or alignment tailored to their specific downstream tasks and datasets, providing greater flexibility for specialized research.
+For technical details on the pre-training architecture, please refer to our Technical Report.