Instructions to use MultiSense/CustomerLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MultiSense/CustomerLM with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MultiSense/CustomerLM")
model = AutoModelForCausalLM.from_pretrained("MultiSense/CustomerLM", 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 MultiSense/CustomerLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MultiSense/CustomerLM

SGLang

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

Docker Model Runner
How to use MultiSense/CustomerLM with Docker Model Runner:
```
docker model run hf.co/MultiSense/CustomerLM
```

xuanbo commited on 8 days ago

Commit

bd08824

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1 Parent(s): 8f79670

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@@ -262,15 +262,18 @@ python salesllm/reverse_role_eval.py \
 ## Citation
 ```bibtex
-@misc{salesllm,
-  title  = {SalesLLM: Benchmarking LLM Realistic Selling Skill},
-  author = {MultiSense},
-  year   = {2025},
-  url    = {https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill}
 }
 ```
 ## Related
 - 📊 [SalesLLM benchmark & code](https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill)
 - 🤗 [SaleIntent-BERT](https://huggingface.co/MultiSense/SaleIntent_bert) — buying-intent classifier used for outcome scoring (93.51% ZH, 92.94% EN)

 ## Citation
 ```bibtex
+@misc{su2026sellmoreplayless,
+      title={Sell More, Play Less: Benchmarking LLM Realistic Selling Skill},
+      author={Xuanbo Su and Wenhao Hu and Le Zhan and Yuting Xie and Kailin Lyu and Kaijie Chen and Ziwei Li and Yeqiang Wang and Haibo Su and Yunzhang Chen and Ling Huang},
+      year={2026},
+      eprint={2604.07054},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2604.07054},
 }
 ```
 ## Related
+- 📖 [Paper](https://arxiv.org/abs/2604.07054)
 - 📊 [SalesLLM benchmark & code](https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill)
 - 🤗 [SaleIntent-BERT](https://huggingface.co/MultiSense/SaleIntent_bert) — buying-intent classifier used for outcome scoring (93.51% ZH, 92.94% EN)