Instructions to use enochlev/MiniCPM-duplex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use enochlev/MiniCPM-duplex with Transformers:

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

pipe = pipeline("text-generation", model="enochlev/MiniCPM-duplex", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("enochlev/MiniCPM-duplex", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use enochlev/MiniCPM-duplex with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/enochlev/MiniCPM-duplex

SGLang

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

Docker Model Runner
How to use enochlev/MiniCPM-duplex with Docker Model Runner:
```
docker model run hf.co/enochlev/MiniCPM-duplex
```

MiniCPM-duplex

File size: 1,255 Bytes

---
language:
- en
- zh
library_name: transformers
tags:
- minicpm
- duplex
- text-generation
base_model: xinrongzhang2022/MiniCPM-duplex
---

# MiniCPM-duplex (safetensors)

Modern safetensors conversion of [xinrongzhang2022/MiniCPM-duplex](https://huggingface.co/xinrongzhang2022/MiniCPM-duplex).

**Weights are identical** — only the serialization format has changed from `pytorch_model.bin`
to `model.safetensors`, enabling memory-mapped loading and compatibility with current
versions of Transformers.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained(
    "enochlev/MiniCPM-duplex", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "enochlev/MiniCPM-duplex",
    trust_remote_code=True,
    dtype=torch.float16,
    device_map="auto",
)

prompt = "<用户>Hello, what can you do?<AI>"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```

## Original model

See [xinrongzhang2022/MiniCPM-duplex](https://huggingface.co/xinrongzhang2022/MiniCPM-duplex)
for the original weights, paper, and full documentation.