Instructions to use ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay with Transformers:

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

pipe = pipeline("text-generation", model="ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay")
model = AutoModelForCausalLM.from_pretrained("ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay", 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 ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay

SGLang

How to use ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay 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 "ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay" \
    --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": "ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay",
		"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 "ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay" \
        --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": "ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay with Docker Model Runner:
```
docker model run hf.co/ArityFlow/ArityFlow-Qwen3-4B-Instruct-2507-RolePlay
```

ArityFlow-Qwen3-4B-Instruct-2507-RolePlay

English | 中文

A RolePlay-oriented fine-tuning based on Qwen3-4B-Instruct-2507

一个专注于沉浸式角色扮演（RolePlay）的 Qwen3 微调模型

📖 Overview | 模型简介

🇺🇸 English

ArityFlow-Qwen3-4B-Instruct-2507-RolePlay is a RolePlay-focused fine-tuned model based on Qwen3-4B-Instruct-2507.

Unlike general-purpose instruction tuning, this project focuses on improving:

Character consistency
Emotional expression
Story progression
Long-form dialogue
Fantasy world building
Immersive roleplay

The model was trained using QLoRA (NF4) with MS-SWIFT.

This repository contains:

✅ Merged Full Model (Recommended)
✅ Original LoRA Adapter (/lora)

🇨🇳 中文

ArityFlow-Qwen3-4B-Instruct-2507-RolePlay 是基于 Qwen3-4B-Instruct-2507 微调得到的角色扮演模型。

本项目并非以 Benchmark 为主要目标，而是重点提升：

人设一致性
情绪表达
剧情推进
长对话能力
世界观构建
沉浸式角色扮演体验

模型采用 MS-SWIFT + QLoRA(NF4) 完成训练。

本仓库同时提供：

✅ 合并后的完整模型（推荐直接推理）
✅ 原始 LoRA Adapter（位于 /lora）

✨ Features | 模型特点

Base Qwen3	ArityFlow RP
Assistant-oriented	RolePlay-oriented
Conservative dialogue	Immersive dialogue
Limited world building	Rich world building
Passive interaction	Dynamic interaction
Limited NPC generation	Better NPC generation
General writing	Storytelling focused

📦 Repository Structure | 仓库结构

.
├── README.md
├── config.json
├── generation_config.json
├── tokenizer.json
├── tokenizer_config.json
├── special_tokens_map.json
├── model.safetensors...
│
└── lora/
    ├── adapter_model.safetensors
    ├── adapter_config.json
    ├── args.json
    └── ...

Root directory

Merged Full Model

lora/

Original QLoRA Adapter

⚙️ Training Configuration | 训练配置

Item	Value
Base Model	Qwen3-4B-Instruct-2507
Framework	MS-SWIFT
Method	QLoRA
Quantization	NF4 4-bit
LoRA Rank	32
LoRA Alpha	64
LoRA Dropout	0.05
Target Modules	all-linear
Max Length	4096
Learning Rate	5e-5
Scheduler	Cosine
Warmup Ratio	5%
Optimizer	AdamW
Batch Size	1
Gradient Accumulation	8
Effective Batch Size	8
Epoch	1

📚 Dataset | 数据集

The model was trained on a merged ShareGPT-format RolePlay dataset.

训练数据采用 ShareGPT 格式角色扮演数据。

After filtering samples longer than 4096 tokens:

过滤超过 4096 Token 的样本后：

Split	Samples
Train	10,511
Validation	549

📈 Training Result | 训练结果

Training converged smoothly without obvious overfitting.

训练过程收敛稳定，无明显过拟合。

Step	Eval Loss
200	1.554
400	1.497
600	1.467
800	1.446
1000	1.435
1314	1.430

Final Validation Token Accuracy

最终验证集 Token Accuracy

64.41%

🔍 Qualitative Evaluation | 主观测试

The model was manually compared against the original Qwen3 model using identical prompts and generation parameters.

在完全相同的 Prompt 与采样参数下，对 Base Qwen3 与微调模型进行了人工对比测试。

Observed improvements:

Better character consistency
Richer action descriptions
Better emotional expression
Better environmental descriptions
Stronger fantasy world building
Better NPC generation
Better long-form roleplay

观察到的提升：

更稳定的人设保持
更丰富的动作描写
更自然的情绪表达
更好的环境描写
更完整的幻想世界构建
更自然的 NPC 生成
更好的长剧情角色扮演体验

The merged model was compared against the original LoRA adapter and showed no observable degradation during manual testing.

同时对 LoRA Adapter 与合并后的完整模型进行了人工对比，未观察到明显的生成质量下降。

💬 Example | 示例

System Prompt

You are Bai Zhi.

The librarian of the Imperial Royal Library.

Stay in character.

Never reveal yourself as an AI.

Maintain the fantasy world setting.

User

The library has already closed.

Heavy rain is falling outside.

I push open the old wooden door and see you repairing an ancient book beside a candle.

"So late... why aren't you going home?"

🚀 Usage | 使用方式

Transformers

from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YOUR_MODEL")

model = AutoModelForCausalLM.from_pretrained(
    "YOUR_MODEL",
    torch_dtype="auto",
    device_map="auto"
)

MS-SWIFT

Merged Model

swift infer \
    --model YOUR_MODEL_PATH

LoRA Adapter

swift infer \
    --model Qwen/Qwen3-4B-Instruct-2507 \
    --adapters lora/

🎯 Recommended Use Cases | 推荐使用场景

Recommended

RolePlay
Character Chat
Interactive Fiction
Fantasy Dialogue
NPC Generation
Storytelling

⚠️ Limitations | 已知特点

Compared with the original Qwen3 model, this model intentionally produces:

Longer responses
Richer descriptions
Stronger emotions
More proactive story progression

This behavior is expected and is part of the design objective.

相较于基础模型，本模型会：

回复更长
动作描写更多
环境描写更多
情绪表达更丰富
更倾向主动推进剧情

这是本项目有意优化的方向，并非异常行为。

🙏 Acknowledgements | 致谢

This project is built upon the following open-source projects:

Alibaba Qwen Team
MS-SWIFT
Hugging Face Transformers
PEFT
ModelScope

Special thanks to the open-source community.

本项目基于以下优秀开源项目完成：

Alibaba Qwen Team
MS-SWIFT
Hugging Face Transformers
PEFT
ModelScope

感谢所有开源贡献者。

📄 License | 许可证

This model follows the license of the original Qwen3-4B-Instruct-2507.

Please refer to the original license before commercial use.

本模型遵循 Qwen3-4B-Instruct-2507 的许可证。

商业使用前请阅读原始模型许可证。

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Tensor type

BF16

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