Instructions to use ServiceNow-AI/Apriel-H1-15b-Thinker-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ServiceNow-AI/Apriel-H1-15b-Thinker-SFT with Transformers:

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

pipe = pipeline("text-generation", model="ServiceNow-AI/Apriel-H1-15b-Thinker-SFT", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ServiceNow-AI/Apriel-H1-15b-Thinker-SFT", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ServiceNow-AI/Apriel-H1-15b-Thinker-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ServiceNow-AI/Apriel-H1-15b-Thinker-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ServiceNow-AI/Apriel-H1-15b-Thinker-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ServiceNow-AI/Apriel-H1-15b-Thinker-SFT

SGLang

How to use ServiceNow-AI/Apriel-H1-15b-Thinker-SFT 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 "ServiceNow-AI/Apriel-H1-15b-Thinker-SFT" \
    --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": "ServiceNow-AI/Apriel-H1-15b-Thinker-SFT",
		"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 "ServiceNow-AI/Apriel-H1-15b-Thinker-SFT" \
        --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": "ServiceNow-AI/Apriel-H1-15b-Thinker-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ServiceNow-AI/Apriel-H1-15b-Thinker-SFT with Docker Model Runner:
```
docker model run hf.co/ServiceNow-AI/Apriel-H1-15b-Thinker-SFT
```

Apriel-H1-15b-Thinker-SFT

File size: 1,099 Bytes

501730b

from transformers import MistralConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)

ssm_config_default = {
    "d_state": 64,
    "n_qk_heads": 32,
    "expand": 1,
    "chunk_size": 128,
    "activation": "identity",
    "bias": False,
    "d_conv": 4,
    "d_inner": 32 * 128,
    "d_xb": None,  # will be set to model dim
    "dt_rank": "auto",
    "dt_min": 0.001,
    "dt_max": 0.1,
    "dt_init": "random",
    "dt_scale": 1.0,
    "dt_init_floor": 1e-4,
    "conv_bias": True,
}


class AprielHConfig(MistralConfig):
    model_type = "apriel_h"

    def __init__(self, hybrid_block_layout=["m2"], ssm_cfg=None, **kwargs):
        super().__init__(**kwargs)
        self.hybrid_block_layout = hybrid_block_layout
        self.head_dim = self.head_dim or self.hidden_size // self.num_attention_heads  # as in transformers 4.51.3
        self.ssm_cfg = ssm_cfg or ssm_config_default

        for k, v in ssm_config_default.items():
            if k not in self.ssm_cfg:
                self.ssm_cfg[k] = v  # to make sure all elements are present in the config