Instructions to use SparseLLM/DECO-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SparseLLM/DECO-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="SparseLLM/DECO-0.5B", 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-0.5B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SparseLLM/DECO-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SparseLLM/DECO-0.5B"
# 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-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SparseLLM/DECO-0.5B

SGLang

How to use SparseLLM/DECO-0.5B 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-0.5B" \
    --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-0.5B",
		"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-0.5B" \
        --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-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SparseLLM/DECO-0.5B with Docker Model Runner:
```
docker model run hf.co/SparseLLM/DECO-0.5B
```

DECO-0.5B / config.json

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	{
	"architectures": [
	"BlockFFNForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_blockffn.BlockFFNConfig",
	"AutoModel": "modeling_blockffn.BlockFFNModel",
	"AutoModelForCausalLM": "modeling_blockffn.BlockFFNForCausalLM"
	},
	"bos_token_id": 1,
	"eos_token_id": [
	2,
	73440
	],
	"pad_token_id": 2,
	"hidden_act": "silu",
	"hidden_size": 1280,
	"initializer_range": 0.1,
	"intermediate_size": 10240,
	"head_dim": 128,
	"max_position_embeddings": 4096,
	"model_type": "blockffn",
	"num_attention_heads": 10,
	"num_hidden_layers": 32,
	"num_key_value_heads": 2,
	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.36.0",
	"use_cache": true,
	"vocab_size": 73448,
	"use_mup": false,
	"num_experts": 77,
	"moe_ffn_hidden_size": 64,
	"moe_shared_expert_intermediate_size": 128,
	"moe_layer_freq": [
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	"moe_router_dtype": "fp32",
	"router_act_func": "relu",
	"router_norm_type": "simple",
	"expert_act_func": "norm_silu",
	"expert_act_norm_type": "normal",
	"num_layers": 27,
	"ffn_hidden_size": 3360,
	"num_query_groups": 10,
	"norm_epsilon": 1e-05,
	"router_norm_fixed": false,
	"router_norm_scalar": false,
	"router_norm_init_var": 0.1,
	"use_blockffn": true,
	"router_type": "topk",
	"moe_router_enable_expert_bias": false,
	"expert_not_gated": true,
	"moe_router_pre_softmax": false,
	"moe_router_topk": 2,
	"moe_router_topp": 0.5,
	"moe_router_score_function": "softmax",
	"moe_router_topk_scaling_factor": null
	}