Instructions to use Entropicengine/Luminatium-L3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Entropicengine/Luminatium-L3-8b with Transformers:

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

pipe = pipeline("text-generation", model="Entropicengine/Luminatium-L3-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Entropicengine/Luminatium-L3-8b")
model = AutoModelForCausalLM.from_pretrained("Entropicengine/Luminatium-L3-8b")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Entropicengine/Luminatium-L3-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Entropicengine/Luminatium-L3-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Entropicengine/Luminatium-L3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Entropicengine/Luminatium-L3-8b

SGLang

How to use Entropicengine/Luminatium-L3-8b 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 "Entropicengine/Luminatium-L3-8b" \
    --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": "Entropicengine/Luminatium-L3-8b",
		"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 "Entropicengine/Luminatium-L3-8b" \
        --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": "Entropicengine/Luminatium-L3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Entropicengine/Luminatium-L3-8b with Docker Model Runner:
```
docker model run hf.co/Entropicengine/Luminatium-L3-8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Luminatium-L3-8b : Overpowered.

Recommended Settings

temperature: 1.3  
min_p: 0.08
rep_pen : 1.1
top_k : 50
max_tokens/context : 8196
template : Llama-3-instruct

Merge Details

Merge Method

This model was created using SLERP (Spherical Linear Interpolation), a technique that blends model weights along a spherical path in the weight space. This method preserves the unique strengths of both base models while creating a smooth transition between their capabilities.

Models Merged

Configuration

base_model: Sao10K/L3-8B-Stheno-v3.2
dtype: bfloat16
merge_method: slerp
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 32]
        model: Sao10K/L3-8B-Stheno-v3.2
      - layer_range: [0, 32]
        model: Sao10K/L3-8B-Lunaris-v1
parameters:
  t:
  - filter: self_attn
    value: [0.0, 0.5, 0.3, 0.7, 1.0]
  - filter: mlp
    value: [1.0, 0.5, 0.7, 0.3, 0.0]
  - value: 0.5

This model was created using mergekit.

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Entropicengine/Luminatium-L3-8b

Sao10K/L3-8B-Lunaris-v1

Sao10K/L3-8B-Stheno-v3.2

Merge model

this model

Merges

1 model

Quantizations

3 models