Instructions to use VERBAREX/LuminoLex-1.5B-think-v12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VERBAREX/LuminoLex-1.5B-think-v12 with Transformers:

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

pipe = pipeline("text-generation", model="VERBAREX/LuminoLex-1.5B-think-v12", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("VERBAREX/LuminoLex-1.5B-think-v12", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VERBAREX/LuminoLex-1.5B-think-v12 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VERBAREX/LuminoLex-1.5B-think-v12"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VERBAREX/LuminoLex-1.5B-think-v12",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VERBAREX/LuminoLex-1.5B-think-v12

SGLang

How to use VERBAREX/LuminoLex-1.5B-think-v12 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 "VERBAREX/LuminoLex-1.5B-think-v12" \
    --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": "VERBAREX/LuminoLex-1.5B-think-v12",
		"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 "VERBAREX/LuminoLex-1.5B-think-v12" \
        --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": "VERBAREX/LuminoLex-1.5B-think-v12",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VERBAREX/LuminoLex-1.5B-think-v12 with Docker Model Runner:
```
docker model run hf.co/VERBAREX/LuminoLex-1.5B-think-v12
```

LuminoLex 1.5B think v12

This is a complete, adapter-free checkpoint based on Lernex/Metis-1.5-think. The transformer weights are kept fresh so the original reasoning distribution is not degraded by identity tuning. The included model/policy.py loader adds the LuminoLex/VERBAREX identity policy, explicit Metis denials only when asked, and a small verified visible-reasoning set. Use LuminoLexChat for the public behavior, or the registered AutoModelForCausalLM loader with trust_remote_code=True; both use the same policy-aware generation path.

from model.policy import LuminoLexChat
chat = LuminoLexChat("/path/to/model")
print(chat.reply("Who are you?"))

No adapter directory is required. model.safetensors contains the complete base weights.

Downloads last month: 1,006

Safetensors

Model size

0.9B params

Tensor type

BF16