Instructions to use kixlab/prefmatcher-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kixlab/prefmatcher-7b with Transformers:

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

pipe = pipeline("text-generation", model="kixlab/prefmatcher-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kixlab/prefmatcher-7b")
model = AutoModelForCausalLM.from_pretrained("kixlab/prefmatcher-7b")
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 kixlab/prefmatcher-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kixlab/prefmatcher-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kixlab/prefmatcher-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kixlab/prefmatcher-7b

SGLang

How to use kixlab/prefmatcher-7b 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 "kixlab/prefmatcher-7b" \
    --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": "kixlab/prefmatcher-7b",
		"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 "kixlab/prefmatcher-7b" \
        --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": "kixlab/prefmatcher-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kixlab/prefmatcher-7b with Docker Model Runner:
```
docker model run hf.co/kixlab/prefmatcher-7b
```

tsook commited on Aug 5, 2025

Commit

bb67802

verified ·

1 Parent(s): d0779f7

Update README.md

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@@ -14,7 +14,7 @@ library_name: transformers
 - **Homepage: https://cupid.kixlab.org**
 - **Repository: https://github.com/kixlab/CUPID**
 - **Benchmark Dataset: https://huggingface.co/datasets/kixlab/CUPID**
-- **Paper: https://arxiv.org/abs/XXXX.XXXXX**
 - **Point of Contact: taesoo.kim@kaist.ac.kr**
 # TL; DR
@@ -104,7 +104,7 @@ If you find our work useful, please consider citing our paper!
 @article{kim2025cupid,
   title     = {CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions},
   author    = {Kim, Tae Soo and Lee, Yoonjoo and Park, Yoonah and Kim, Jiho and Kim, Young-Ho and Kim, Juho},
-  journal   = {arXiv preprint arXiv:XXXX.YYYYY},
   year      = {2025},
 }
 ```

 - **Homepage: https://cupid.kixlab.org**
 - **Repository: https://github.com/kixlab/CUPID**
 - **Benchmark Dataset: https://huggingface.co/datasets/kixlab/CUPID**
+- **Paper: https://arxiv.org/abs/2508.01674**
 - **Point of Contact: taesoo.kim@kaist.ac.kr**
 # TL; DR
 @article{kim2025cupid,
   title     = {CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions},
   author    = {Kim, Tae Soo and Lee, Yoonjoo and Park, Yoonah and Kim, Jiho and Kim, Young-Ho and Kim, Juho},
+  journal   = {arXiv preprint arXiv:2508.01674},
   year      = {2025},
 }
 ```