Instructions to use Deci/DeciLM-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Deci/DeciLM-7B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Deci/DeciLM-7B-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("Deci/DeciLM-7B-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Deci/DeciLM-7B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Deci/DeciLM-7B-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": "Deci/DeciLM-7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Deci/DeciLM-7B-instruct

SGLang

How to use Deci/DeciLM-7B-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 "Deci/DeciLM-7B-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": "Deci/DeciLM-7B-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 "Deci/DeciLM-7B-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": "Deci/DeciLM-7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Deci/DeciLM-7B-instruct with Docker Model Runner:
```
docker model run hf.co/Deci/DeciLM-7B-instruct
```

DeciLM-7B-instruct / configuration_decilm.py

itay-levy

change model_type to deci

ebc99ef over 2 years ago

raw

history blame contribute delete

576 Bytes

	from .version_check import check_transformers_version

	check_transformers_version()

	from .transformers_v4_35_2__configuration_llama import LlamaConfig


	class DeciLMConfig(LlamaConfig):
	r"""
	Args:
	num_key_value_heads_per_layer (`List[int]`):
	The number of key-value heads per layer.
	"""
	model_type = "deci"

	def __init__(
	self,
	num_key_value_heads_per_layer: list = None,
	**kwargs,
	):
	self.num_key_value_heads_per_layer = num_key_value_heads_per_layer
	super().__init__(**kwargs)