Instructions to use SparseLLM/DECO-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SparseLLM/DECO-1.2B with Transformers:

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

pipe = pipeline("text-generation", model="SparseLLM/DECO-1.2B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SparseLLM/DECO-1.2B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SparseLLM/DECO-1.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SparseLLM/DECO-1.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SparseLLM/DECO-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SparseLLM/DECO-1.2B

SGLang

How to use SparseLLM/DECO-1.2B 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 "SparseLLM/DECO-1.2B" \
    --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": "SparseLLM/DECO-1.2B",
		"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 "SparseLLM/DECO-1.2B" \
        --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": "SparseLLM/DECO-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SparseLLM/DECO-1.2B with Docker Model Runner:
```
docker model run hf.co/SparseLLM/DECO-1.2B
```

Add library_name metadata and improve model card

by nielsr HF Staff - opened about 3 hours ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+17

-6

nielsr

about 3 hours ago

Hi! I'm Niels from the Hugging Face community team.

This PR improves your model card by:

Adding library_name: transformers to the metadata. This enables the "Use in Transformers" button and automated code snippets on the model page, as the model is compatible with AutoModelForCausalLM using trust_remote_code=True.
Organizing the links to the paper and code repository for better visibility.
Including the author list from the paper.

Add library_name metadata and improve model card99494204

Raincleared changed pull request status to merged about 2 hours ago

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