Instructions to use qubitpage/sentinel-prime-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qubitpage/sentinel-prime-350m with Transformers:

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

pipe = pipeline("text-generation", model="qubitpage/sentinel-prime-350m", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("qubitpage/sentinel-prime-350m", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use qubitpage/sentinel-prime-350m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qubitpage/sentinel-prime-350m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qubitpage/sentinel-prime-350m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/qubitpage/sentinel-prime-350m

SGLang

How to use qubitpage/sentinel-prime-350m 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 "qubitpage/sentinel-prime-350m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qubitpage/sentinel-prime-350m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "qubitpage/sentinel-prime-350m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qubitpage/sentinel-prime-350m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use qubitpage/sentinel-prime-350m with Docker Model Runner:
```
docker model run hf.co/qubitpage/sentinel-prime-350m
```

sentinel-prime-350m / tokenizer_config.json

qubitpage

Add auto_map so AutoTokenizer + trust_remote_code routes to SentinelBrainTokenizer

b607cb7 verified 16 days ago

raw

history blame contribute delete

482 Bytes

	{
	"added_tokens_decoder": {
	"100257": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"backend": "custom",
	"eos_token": "<\|endoftext\|>",
	"model_max_length": 2048,
	"pad_token": "<\|endoftext\|>",
	"tokenizer_class": "SentinelBrainTokenizer",
	"auto_map": {
	"AutoTokenizer": [
	"hf_tokenizer.SentinelBrainTokenizer",
	null
	]
	}
	}