Instructions to use OpenSI-SI/OpenSI-0.3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenSI-SI/OpenSI-0.3B-Base with Transformers:

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

pipe = pipeline("text-generation", model="OpenSI-SI/OpenSI-0.3B-Base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenSI-SI/OpenSI-0.3B-Base")
model = AutoModelForCausalLM.from_pretrained("OpenSI-SI/OpenSI-0.3B-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenSI-SI/OpenSI-0.3B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenSI-SI/OpenSI-0.3B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenSI-SI/OpenSI-0.3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenSI-SI/OpenSI-0.3B-Base

SGLang

How to use OpenSI-SI/OpenSI-0.3B-Base 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 "OpenSI-SI/OpenSI-0.3B-Base" \
    --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": "OpenSI-SI/OpenSI-0.3B-Base",
		"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 "OpenSI-SI/OpenSI-0.3B-Base" \
        --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": "OpenSI-SI/OpenSI-0.3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenSI-SI/OpenSI-0.3B-Base with Docker Model Runner:
```
docker model run hf.co/OpenSI-SI/OpenSI-0.3B-Base
```

OpenSI-0.3B-Base / tokenizer_config.json

SIZWL

Upload tokenizer_config.json

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{
  "tokenizer_class": "PreTrainedTokenizerFast",
  "model_max_length": 2048,
  "bos_token": "<s>",
  "eos_token": "</s>",
  "unk_token": "<unk>",
  "pad_token": "<pad>",
  "clean_up_tokenization_spaces": false,
  "added_tokens_decoder": {
    "0": {
      "content": "<unk>",
      "special": true,
      "normalized": false,
      "lstrip": false,
      "rstrip": false,
      "single_word": false
    },
    "1": {
      "content": "<s>",
      "special": true,
      "normalized": false,
      "lstrip": false,
      "rstrip": false,
      "single_word": false
    },
    "2": {
      "content": "</s>",
      "special": true,
      "normalized": false,
      "lstrip": false,
      "rstrip": false,
      "single_word": false
    },
    "3": {
      "content": "<pad>",
      "special": true,
      "normalized": false,
      "lstrip": false,
      "rstrip": false,
      "single_word": false
    }
  }
}

	{
	"tokenizer_class": "PreTrainedTokenizerFast",
	"model_max_length": 2048,
	"bos_token": "<s>",
	"eos_token": "</s>",
	"unk_token": "<unk>",
	"pad_token": "<pad>",
	"clean_up_tokenization_spaces": false,
	"added_tokens_decoder": {
	"0": {
	"content": "<unk>",
	"special": true,
	"normalized": false,
	"lstrip": false,
	"rstrip": false,
	"single_word": false
	},
	"1": {
	"content": "<s>",
	"special": true,
	"normalized": false,
	"lstrip": false,
	"rstrip": false,
	"single_word": false
	},
	"2": {
	"content": "</s>",
	"special": true,
	"normalized": false,
	"lstrip": false,
	"rstrip": false,
	"single_word": false
	},
	"3": {
	"content": "<pad>",
	"special": true,
	"normalized": false,
	"lstrip": false,
	"rstrip": false,
	"single_word": false
	}
	}
	}