Instructions to use Yukendiran/Liceron-SecOps-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yukendiran/Liceron-SecOps-Model with Transformers:

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

pipe = pipeline("text-generation", model="Yukendiran/Liceron-SecOps-Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yukendiran/Liceron-SecOps-Model")
model = AutoModelForCausalLM.from_pretrained("Yukendiran/Liceron-SecOps-Model")
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 Settings

vLLM

How to use Yukendiran/Liceron-SecOps-Model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Yukendiran/Liceron-SecOps-Model

SGLang

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

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

custom-secops-7b

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

Merge Details

Merge Method

This model was merged using the TIES merge method using /kaggle/input/models/qwen-lm/qwen2.5/transformers/7b/1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


merge_method: ties
base_model: /kaggle/input/models/qwen-lm/qwen2.5/transformers/7b/1

models:
  # 1. Base (Anchor)
  - model: /kaggle/input/models/qwen-lm/qwen2.5/transformers/7b/1
    parameters:
      density: 1.0
      weight: 1.0

  # 2. The Hacker's Eyes (DeepHat)
  - model: DeepHat/DeepHat-V1-7B
    parameters:
      density: 1.0 
      weight: 0.8  # BOOSTED: dominating security focus

  # 3. Strict Logician (Math)
  - model: Qwen/Qwen2.5-Math-7B
    parameters:
      density: 1.0 
      weight: 0.3 # Slight boost for logic

  # 4. Coding Capability
  - model: Qwen/Qwen2.5-Coder-7B-Instruct
    parameters:
      density: 1.0
      weight: 0.4

  # 5. Reasoning (DeepThinking)
  - model: prithivMLmods/Deepthink-Reasoning-7B
    parameters:
      density: 1.0
      weight: 0.8 # BOOSTED: heavy reasoning injection
        
dtype: bfloat16