Instructions to use ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt with Transformers:

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

pipe = pipeline("text-generation", model="ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt")
model = AutoModelForCausalLM.from_pretrained("ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt", 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 ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt

SGLang

How to use ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt 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 "ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt" \
    --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": "ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt",
		"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 "ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt" \
        --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": "ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt with Docker Model Runner:
```
docker model run hf.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt
```

Czardas commited on Feb 19

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+---
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- alignment
+- evaluation
+- preference-learning
+- ripd
+base_model: google/gemma-2-2b-it
+datasets:
+- ZDCSlab/ripd-dataset
+---
+# ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt
+This checkpoint is part of the artifact release for
+**“Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges.”**
+It is a policy model trained under a specific rubric condition to study how evaluation-time preference drift propagates into downstream alignment.
+---
+## Configuration
+- **Setting:** ultra-real
+- **Base model:** [Gemma-2-2B-it](https://huggingface.co/google/gemma-2-2b-it)
+- **Label condition:** seed
+- **Training data:** Bench + Target (mixed)
+- **Objective:** Direct Preference Optimization (DPO)
+The `seed` condition corresponds to preference labels generated by an LLM judge under the `seed` rubric variant.
+---
+## Intended Use
+This model is released for research on evaluation-time robustness, preference drift, and alignment propagation.
+It is not intended for production deployment.
+---
+## Resources
+- 📄 Paper: https://www.arxiv.org/pdf/2602.13576
+- 💻 Code & Evaluation Pipeline: https://github.com/ZDCSlab/Rubrics-as-an-Attack-Surface
+- 📊 Dataset: https://huggingface.co/datasets/ZDCSlab/ripd-dataset