Instructions to use AIJian/PaTaRM-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIJian/PaTaRM-14B with Transformers:

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

pipe = pipeline("text-generation", model="AIJian/PaTaRM-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIJian/PaTaRM-14B")
model = AutoModelForCausalLM.from_pretrained("AIJian/PaTaRM-14B")
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 AIJian/PaTaRM-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIJian/PaTaRM-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIJian/PaTaRM-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIJian/PaTaRM-14B

SGLang

How to use AIJian/PaTaRM-14B 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 "AIJian/PaTaRM-14B" \
    --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": "AIJian/PaTaRM-14B",
		"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 "AIJian/PaTaRM-14B" \
        --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": "AIJian/PaTaRM-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIJian/PaTaRM-14B with Docker Model Runner:
```
docker model run hf.co/AIJian/PaTaRM-14B
```

PaTaRM-14B

This is the PaTaRM-14B model, part of the PaTaRM series. For full details including overview, usage examples, training data, and citation, please refer to the main collection README:

👉 AIJian/PaTaRM — Main README

Models

Model	Base	Link
PaTaRM-8B	Qwen3-8B	AIJian/PaTaRM-8B
PaTaRM-14B	Qwen3-14B	AIJian/PaTaRM-14B

Citation

@misc{jian2026patarmbridgingpairwisepointwise,
      title={PaTaRM: Bridging Pairwise and Pointwise Signals via Preference-Aware Task-Adaptive Reward Modeling}, 
      author={Ai Jian and Jingqing Ruan and Xing Ma and Dailin Li and Weipeng Zhang and Ke Zeng and Xunliang Cai},
      year={2026},
      eprint={2510.24235},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2510.24235}, 
}