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

Libraries

How to use OpenSI-SI/OpenSI-Haiku with Transformers:

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

pipe = pipeline("text-generation", model="OpenSI-SI/OpenSI-Haiku")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("OpenSI-SI/OpenSI-Haiku")
model = AutoModelForCausalLM.from_pretrained("OpenSI-SI/OpenSI-Haiku", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenSI-SI/OpenSI-Haiku with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OpenSI-SI/OpenSI-Haiku

SGLang

How to use OpenSI-SI/OpenSI-Haiku 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-Haiku" \
    --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": "OpenSI-SI/OpenSI-Haiku",
		"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 "OpenSI-SI/OpenSI-Haiku" \
        --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": "OpenSI-SI/OpenSI-Haiku",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

OpenSI-Haiku

File size: 1,937 Bytes

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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
    }
  },
  "chat_template": "{#-\n  OpenSI-Haiku 对话模板（与 src/chat_format.py 逐字符等价，由 self_check 保证）。\n\n  要点：\n    * 不引入新 special token，role 用纯文本标记 ### System/User/Assistant。\n    * 助手轮以 eos_token(</s>) 收尾——它就是文档边界，模型据此学会「说完」。\n    * add_generation_prompt=True 时以「### Assistant:\\n」结尾，等待模型生成。\n    * 所有模板字面空白都用 {%- -%} 去掉，实际文本完全由表达式产出，\n      这样才会与 Python 的字符串拼接逐字符一致。\n-#}\n{%- for message in messages -%}\n{%- if message['role'] == 'system' -%}\n{{ '### System:\\n' + message['content'] + '\\n' }}\n{%- elif message['role'] == 'user' -%}\n{{ '### User:\\n' + message['content'] + '\\n' }}\n{%- elif message['role'] == 'assistant' -%}\n{{ '### Assistant:\\n' + message['content'] + eos_token }}\n{%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n{{ '### Assistant:\\n' }}\n{%- endif -%}\n"
}