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", 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 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 Dec 31, 2025

Commit

feee54d

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1 Parent(s): 8144866

Update README.md

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@@ -27,7 +27,7 @@
 **OpenOneRec** is an open-source framework designed to bridge the gap between traditional recommendation systems and Large Language Models (LLMs). While Generative Recommendation has shown promise, existing models often struggle with isolated data silos and a lack of reasoning capabilities.
 To address this, we introduce a unified framework that comprises:
-* **RecIF-Bench**: The first holistic Recommendation Instruction-Following Benchmark, containing **100M interactions** from 200k users across heterogeneous domains (Short Video, Ads, Goods).
 * **OpenOneRec-Foundation Models**: A family of models (1.7B & 8B) built on the Qwen backbone. These models are trained on hundreds of billions of tokens, integrating collaborative signals with general semantics.
 * **Full-Stack Pipeline**: We open-source our comprehensive training pipeline, including data processing, co-pretraining, and post-training, to ensure full reproducibility and facilitate scaling law research in recommendation.
@@ -39,14 +39,14 @@ To address this, we introduce a unified framework that comprises:
 ## 📊 RecIF-Bench
-We propose **RecIF-Bench** to rigorously assess the synergy between instruction following and domain-specific recommendation. It organizes 9 distinct tasks into a four-layer capability hierarchy:
 * **Layer 0: Semantic Alignment** (Item Understanding)
-* **Layer 1: Fundamental Prediction** (Short Video Rec, Ad Rec, Goods Rec, Label Prediction)
 * **Layer 2: Instruction Following** (Interactive Rec, Label-Conditional Rec)
-* **Layer 3: Reasoning** (User Summarization, Recommendation Explanation)
-The benchmark aggregates data from three domains: **Short Video** (Content), **Ads** (Commercial), and **Goods** (E-commerce).
 ## 🤖 Model Zoo
@@ -85,11 +85,16 @@ Our framework utilizes the following recipe:
 ### Results on RecIF-Bench
 OpenOneRec-Foundation achieves **State-of-the-Art (SOTA)** results across RecIF-Bench tasks, significantly outperforming baselines like LC-Rec and TIGER.
-| Task | Metric | TIGER | LC-Rec | **OneRec-Pro-8B** |
 | :--- | :--- | :--- | :--- | :--- |
-| **UserDoc** | Recall@32 | 0.0132 | 0.0180 | **0.0369** |
-| **Ads Rec** | Recall@32 | 0.0581 | 0.0723 | **0.0964** |
-| **Goods Rec** | Recall@32 | 0.0283 | 0.0416 | **0.0538** |
 ### Cross-Domain Transferability

 **OpenOneRec** is an open-source framework designed to bridge the gap between traditional recommendation systems and Large Language Models (LLMs). While Generative Recommendation has shown promise, existing models often struggle with isolated data silos and a lack of reasoning capabilities.
 To address this, we introduce a unified framework that comprises:
+* **RecIF-Bench**: The first holistic Recommendation Instruction-Following Benchmark, containing **120M interactions** from 200k users across heterogeneous domains (Short Video, Ad, Product).
 * **OpenOneRec-Foundation Models**: A family of models (1.7B & 8B) built on the Qwen backbone. These models are trained on hundreds of billions of tokens, integrating collaborative signals with general semantics.
 * **Full-Stack Pipeline**: We open-source our comprehensive training pipeline, including data processing, co-pretraining, and post-training, to ensure full reproducibility and facilitate scaling law research in recommendation.
 ## 📊 RecIF-Bench
+We propose **RecIF-Bench**, a comprehensive benchmark designed to rigorously evaluate recommendation foundation models. It organizes 8 distinct tasks into a four-layer capability hierarchy:
 * **Layer 0: Semantic Alignment** (Item Understanding)
+* **Layer 1: Fundamental Recommendation** (Short Video Rec, Ad Rec, Product Rec, Label Prediction)
 * **Layer 2: Instruction Following** (Interactive Rec, Label-Conditional Rec)
+* **Layer 3: Reasoning** (Recommendation Explanation)
+The benchmark aggregates data from three domains: **Short Video** (Content), **Ad** (Commercial), and **Product** (E-commerce).
 ## 🤖 Model Zoo
 ### Results on RecIF-Bench
 OpenOneRec-Foundation achieves **State-of-the-Art (SOTA)** results across RecIF-Bench tasks, significantly outperforming baselines like LC-Rec and TIGER.
+| Task | Metric | TIGER | LC-Rec-8B | **OneRec-8B-Pro** |
 | :--- | :--- | :--- | :--- | :--- |
+| **Short Video Rec** | Recall@32 | 0.0132 | 0.0180 | **0.0369** |
+| **Ad Rec** | Recall@32 | 0.0581 | 0.0723 | **0.0964** |
+| **Product Rec** | Recall@32 | 0.0283 | 0.0416 | **0.0538** |
+| **Label-Cond. Rec** | Recall@32 | 0.0123 | 0.0170 | **0.0235** |
+| **Label Pred.** | AUC | 0.6675 | 0.6139 | **0.6912** |
+| **Interactive Rec** | Recall@32 | -- | 0.2394 | **0.3458** |
+| **Item Understand.** | LLM-Judge Score| -- | 0.2517 | **0.3209** |
+| **Rec. Explanation** | LLM-Judge Score| -- | 3.9350 | **4.0381** |
 ### Cross-Domain Transferability