Text Generation
Transformers
Indonesian
deepseek_v4
deepseek
Mixture of Experts
causal-lm
chat
cpu
indonesian
conversational
custom_code
Instructions to use zelapi/lilo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,743 Bytes
36a88b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | #!/usr/bin/env python3
"""LILO chat CLI — CPU-optimized, KV-cache generation.
Usage:
python chat_lilo.py "prompt" # single prompt
python chat_lilo.py # interactive
python chat_lilo.py --temp 0.7 --top-p 0.9 --max 200 "prompt"
"""
import argparse, os, sys
HERE = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, HERE)
from lilo_common import load_lilo, generate_lilo
def main():
ap = argparse.ArgumentParser()
ap.add_argument("prompt", nargs="*")
ap.add_argument("--variant", default="fp16", choices=["fp16", "int8"])
ap.add_argument("--max", type=int, default=200)
ap.add_argument("--temp", type=float, default=0.7)
ap.add_argument("--top-p", type=float, default=0.9)
ap.add_argument("--rep", type=float, default=1.1)
ap.add_argument("--threads", type=int, default=1)
ap.add_argument("--path", default=HERE)
a = ap.parse_args()
tok, model = load_lilo(a.path, variant=a.variant, threads=a.threads)
def run(prompt):
ids = tok.encode(prompt, add_special_tokens=False)
out = generate_lilo(model, tok, ids, max_new_tokens=a.max, temperature=a.temp,
top_p=a.top_p, repetition_penalty=a.rep, stream=True)
return tok.decode(out, skip_special_tokens=True)
if a.prompt:
run(" ".join(a.prompt))
print()
return
print("LILO siap. Ketik pesan (Ctrl-C/Ctrl-D untuk keluar).")
while True:
try:
p = input("\n> ").strip()
except (EOFError, KeyboardInterrupt):
print()
break
if not p:
continue
run(p)
print()
if __name__ == "__main__":
main()
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