Instructions to use zelapi/lilo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zelapi/lilo with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zelapi/lilo", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("zelapi/lilo", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zelapi/lilo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zelapi/lilo

SGLang

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

Docker Model Runner
How to use zelapi/lilo with Docker Model Runner:
```
docker model run hf.co/zelapi/lilo
```

lilo / config.json

hzlo0

lilo-100m: fp16 + group-wise int8 CPU release

36a88b2 verified 5 days ago

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	{
	"architectures": [
	"DeepseekV4ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_deepseek_v4.DeepseekV4Config",
	"AutoModelForCausalLM": "modeling_deepseek_v4.DeepseekV4ForCausalLM"
	},
	"bos_token_id": 0,
	"compress_ratios": [
	0,
	0,
	0,
	0,
	0,
	0,
	0,
	0,
	0
	],
	"compress_rope_theta": 160000.0,
	"dtype": "float16",
	"eos_token_id": 1,
	"hc_eps": 1e-06,
	"hc_mult": 4,
	"hc_sinkhorn_iters": 2,
	"head_dim": 96,
	"hidden_act": "silu",
	"hidden_size": 320,
	"index_head_dim": 128,
	"index_n_heads": 64,
	"index_topk": 512,
	"initializer_range": 0.02,
	"max_position_embeddings": 2048,
	"model_type": "deepseek_v4",
	"moe_intermediate_size": 640,
	"n_routed_experts": 4,
	"n_shared_experts": 1,
	"nope_head_dim": 64,
	"norm_topk_prob": true,
	"num_attention_heads": 8,
	"num_experts_per_tok": 2,
	"num_hash_layers": 0,
	"num_hidden_layers": 8,
	"num_key_value_heads": 1,
	"num_nextn_predict_layers": 1,
	"o_groups": 2,
	"o_lora_rank": 80,
	"pad_token_id": 1,
	"q_lora_rank": 160,
	"qk_rope_head_dim": 32,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 10000.0,
	"rope_type": "default"
	},
	"rope_theta": 10000.0,
	"routed_scaling_factor": 1.5,
	"scoring_func": "sqrtsoftplus",
	"sliding_window": 128,
	"swiglu_limit": 0.0,
	"tie_word_embeddings": false,
	"topk_method": "noaux_tc",
	"transformers_version": "5.15.1",
	"use_cache": false,
	"vocab_size": 129280,
	"name": "lilo-100m",
	"quantization_config": {
	"format": "int8-symmetric-groupwise",
	"group_size": 64,
	"modules": "linear+embeddings",
	"file": "model.int8.safetensors"
	}
	}