Instructions to use rostlabs/rost-1b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rostlabs/rost-1b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="rostlabs/rost-1b-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("rostlabs/rost-1b-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rostlabs/rost-1b-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rostlabs/rost-1b-instruct

SGLang

How to use rostlabs/rost-1b-instruct 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 "rostlabs/rost-1b-instruct" \
    --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": "rostlabs/rost-1b-instruct",
		"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 "rostlabs/rost-1b-instruct" \
        --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": "rostlabs/rost-1b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rostlabs/rost-1b-instruct with Docker Model Runner:
```
docker model run hf.co/rostlabs/rost-1b-instruct
```

rost-1b-instruct

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{
  "tokenizer_class": "PreTrainedTokenizerFast",
  "bos_token": "<|bos|>",
  "eos_token": "<|assistant_end|>",
  "model_max_length": 4096,
  "clean_up_tokenization_spaces": false,
  "chat_template": "{{- bos_token -}}{%- set ns = namespace(system='') -%}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{%- set ns.system = message['content'] -%}{%- elif message['role'] == 'user' -%}{{- '<|user_start|>' -}}{%- if ns.system -%}{{- ns.system + '\n\n' -}}{%- set ns.system = '' -%}{%- endif -%}{{- message['content'] + '<|user_end|>' -}}{%- elif message['role'] == 'assistant' -%}{{- '<|assistant_start|>' + message['content'] + '<|assistant_end|>' -}}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|assistant_start|>' -}}{%- endif -%}"
}