Instructions to use suayptalha/Luminis-phi-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suayptalha/Luminis-phi-4 with Transformers:

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

pipe = pipeline("text-generation", model="suayptalha/Luminis-phi-4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/Luminis-phi-4")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Luminis-phi-4")
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 suayptalha/Luminis-phi-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/Luminis-phi-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suayptalha/Luminis-phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suayptalha/Luminis-phi-4

SGLang

How to use suayptalha/Luminis-phi-4 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 "suayptalha/Luminis-phi-4" \
    --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": "suayptalha/Luminis-phi-4",
		"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 "suayptalha/Luminis-phi-4" \
        --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": "suayptalha/Luminis-phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use suayptalha/Luminis-phi-4 with Docker Model Runner:
```
docker model run hf.co/suayptalha/Luminis-phi-4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Merged Model

This is a merge of pre-trained language models created using mergekit.

This model is currently ranked #3 on the Open LLM Leaderboard among models up to 15B parameters, #4 among models up to 32B parameters and #53 among all the models!

-14.2.2025

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: bunnycore/Phi-4-Model-Stock-v4
dtype: bfloat16
merge_method: slerp
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
slices:
- sources:
  - layer_range: [0, 40]
    model: bunnycore/Phi-4-Model-Stock-v4
  - layer_range: [0, 40]
    model: bunnycore/Phi-4-RR-Shoup

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	41.30
IFEval (0-Shot)	69.00
BBH (3-Shot)	55.80
MATH Lvl 5 (4-Shot)	43.66
GPQA (0-shot)	13.53
MuSR (0-shot)	16.68
MMLU-PRO (5-shot)	49.15

Downloads last month: 1,115

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for suayptalha/Luminis-phi-4

bunnycore/Phi-4-Model-Stock-v4

bunnycore/Phi-4-RR-Shoup

Merge model

this model

Merges

3 models

Quantizations

3 models

Spaces using suayptalha/Luminis-phi-4 16

Collection including suayptalha/Luminis-phi-4

Merge Models

Collection

Models I merged using mergekit library • 8 items • Updated Apr 10, 2025 • 4

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

69.000
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

55.800
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

43.660
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

13.530
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

16.680
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

49.150