Instructions to use webbrain-one/webbrain-compass-tiny-xs-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/webbrain-compass-tiny-xs-v3 with Transformers:

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

pipe = pipeline("text-generation", model="webbrain-one/webbrain-compass-tiny-xs-v3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("webbrain-one/webbrain-compass-tiny-xs-v3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/webbrain-compass-tiny-xs-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webbrain-one/webbrain-compass-tiny-xs-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webbrain-one/webbrain-compass-tiny-xs-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webbrain-one/webbrain-compass-tiny-xs-v3

SGLang

How to use webbrain-one/webbrain-compass-tiny-xs-v3 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 "webbrain-one/webbrain-compass-tiny-xs-v3" \
    --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": "webbrain-one/webbrain-compass-tiny-xs-v3",
		"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 "webbrain-one/webbrain-compass-tiny-xs-v3" \
        --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": "webbrain-one/webbrain-compass-tiny-xs-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use webbrain-one/webbrain-compass-tiny-xs-v3 with Docker Model Runner:
```
docker model run hf.co/webbrain-one/webbrain-compass-tiny-xs-v3
```

webbrain-compass-tiny-xs-v3 / tokenizer_config.json

webbrain-one

WebBrain Compass Tiny XS v3 release

eedb5be 7 days ago

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402 Bytes

	{
	"backend": "tokenizers",
	"bos_token": "<｜start▁of▁sentence｜>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<｜end▁of▁sentence｜>",
	"is_local": true,
	"legacy": true,
	"local_files_only": true,
	"model_max_length": 131072,
	"pad_token": "<｜▁pad▁｜>",
	"sp_model_kwargs": {},
	"tokenizer_class": "TokenizersBackend",
	"unk_token": "<unk>"
	}