Instructions to use hunter-P/roleplay-Xing4.0-29B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hunter-P/roleplay-Xing4.0-29B with Transformers:

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

pipe = pipeline("text-generation", model="hunter-P/roleplay-Xing4.0-29B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("hunter-P/roleplay-Xing4.0-29B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hunter-P/roleplay-Xing4.0-29B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hunter-P/roleplay-Xing4.0-29B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hunter-P/roleplay-Xing4.0-29B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hunter-P/roleplay-Xing4.0-29B

SGLang

How to use hunter-P/roleplay-Xing4.0-29B 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 "hunter-P/roleplay-Xing4.0-29B" \
    --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": "hunter-P/roleplay-Xing4.0-29B",
		"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 "hunter-P/roleplay-Xing4.0-29B" \
        --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": "hunter-P/roleplay-Xing4.0-29B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hunter-P/roleplay-Xing4.0-29B with Docker Model Runner:
```
docker model run hf.co/hunter-P/roleplay-Xing4.0-29B
```

RolePlay-Xing4.0-29B

This model is a fine-tuned version of Xing4.0-29B-A4B, specifically optimized for vertical domain tasks related to Roleplay. It is designed to deliver an immersive conversational experience, maintain stable character personas, and handle complex narrative progression.

Base Model Highlights

The base model is Xing4.0-29B-A4B (formerly known as the TeleChat series), developed by China Telecom Artificial Intelligence Technology Co., Ltd.

Efficient MoE Architecture: With 29B total parameters and only 4B activated per token, it achieves an excellent balance between inference efficiency and performance.
Extended Context Support: Natively supports a 256K context length, extensible to 512K, making it highly suitable for long-form roleplay, world-building memory, and deep multi-turn dialogues.
Domestic Hardware Ecosystem: It is the first model of this scale trained entirely on the Ascend NPU platform with the MindSpore framework, deeply optimized for complex engineering tasks.

Training Details

Fine-tuning Task: Roleplay / Character Persona.
Training Data: The fine-tuning dataset comprises 6,247 high-quality multi-turn roleplay dialogues. It covers 1,346 distinct characters/personas, with an average of 6 interaction turns per dialogue. This ensures strong context retention and persona consistency during extended conversations.
Training Method: Full-parameter fine-tuning.
Training Hardware: Ascend NPU cluster.
Training Framework: MindSpore.

Quickstart

Inference

roleplay-Xing4.0-29B-A4B can be accessed via an OpenAI-compatible API.

from openai import OpenAI

client = OpenAI(
    base_url="your-base-url",
    api_key="your-api-key",
)

completion = client.chat.completions.create(
    model="Xing4.0-29B-A4B",
    messages=[{"role": "user", "content": "hi! Mr Bench."}],
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "repetition_penalty": 1.05,
        "skip_special_tokens": False,
        "spaces_between_special_tokens": False,
        "chat_template_kwargs": {
            "enable_thinking": True,  # Set to False to disable thinking
        },
    },
)

print(completion.choices[0].message.content)

Recommended Parameters

temperature	top_p	repetition_penalty
1.1	0.95	1.02

Downloads last month: 180

Safetensors

Model size

31B params

Tensor type

F32

BF16

Model tree for hunter-P/roleplay-Xing4.0-29B

Base model

XingChen-AGI/Xing4.0-29B-A4B

Finetuned

(8)

this model