Instructions to use Alphaplasti/ToneBridge-MiniCPM4.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alphaplasti/ToneBridge-MiniCPM4.1-8B with Transformers:

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

pipe = pipeline("text-generation", model="Alphaplasti/ToneBridge-MiniCPM4.1-8B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Alphaplasti/ToneBridge-MiniCPM4.1-8B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Alphaplasti/ToneBridge-MiniCPM4.1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alphaplasti/ToneBridge-MiniCPM4.1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alphaplasti/ToneBridge-MiniCPM4.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alphaplasti/ToneBridge-MiniCPM4.1-8B

SGLang

How to use Alphaplasti/ToneBridge-MiniCPM4.1-8B 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 "Alphaplasti/ToneBridge-MiniCPM4.1-8B" \
    --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": "Alphaplasti/ToneBridge-MiniCPM4.1-8B",
		"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 "Alphaplasti/ToneBridge-MiniCPM4.1-8B" \
        --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": "Alphaplasti/ToneBridge-MiniCPM4.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alphaplasti/ToneBridge-MiniCPM4.1-8B with Docker Model Runner:
```
docker model run hf.co/Alphaplasti/ToneBridge-MiniCPM4.1-8B
```

ToneBridge-MiniCPM4.1-8B / training_data /README.md

Alphaplasti

Update model card and training data documentation

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	# Training Data

	This folder documents the task-specific datasets used to fine-tune
	ToneBridge-MiniCPM4.1-8B.

	The files are JSONL files in chat prompt/completion format.

	## Files

	\| File \| Rows \| Purpose \|
	\|---\|---:\|---\|
	\| `hsk12_english_order_train.jsonl` \| 1,800 \| Training split for HSK 1/2-style sentences influenced by English word order. \|
	\| `hsk12_english_order_valid.jsonl` \| 200 \| Validation split for the HSK 1/2 word-order task. \|
	\| `context_tone_hsk3_train.jsonl` \| 4,500 \| Training split for HSK 1-3 context and tone adaptation. \|
	\| `context_tone_hsk3_valid.jsonl` \| 500 \| Validation split for the HSK 1-3 context/tone task. \|

	## Dataset Format

	Each row follows this structure:

	```json
	{
	"prompt": [
	{
	"role": "system",
	"content": "你是中文语境校对助手。只输出更合适的句子，不要解释。"
	},
	{
	"role": "user",
	"content": "上下文：...\n原句：...\n任务：请根据上下文把原句改成更合适的中文。/no_think"
	}
	],
	"completion": [
	{
	"role": "assistant",
	"content": "..."
	}
	]
	}
	```

	## Notes

	- The data is synthetic and task-specific.
	- It is intended for beginner Mandarin correction and tone adaptation.
	- The dataset was generated to reduce exact duplicate source/correction pairs.
	- The data should not be treated as a general Chinese benchmark.