Instructions to use kainatq/KingOmini_14b_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kainatq/KingOmini_14b_v1 with Transformers:

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

pipe = pipeline("text-generation", model="kainatq/KingOmini_14b_v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kainatq/KingOmini_14b_v1")
model = AutoModelForCausalLM.from_pretrained("kainatq/KingOmini_14b_v1", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kainatq/KingOmini_14b_v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kainatq/KingOmini_14b_v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/KingOmini_14b_v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kainatq/KingOmini_14b_v1

SGLang

How to use kainatq/KingOmini_14b_v1 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 "kainatq/KingOmini_14b_v1" \
    --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": "kainatq/KingOmini_14b_v1",
		"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 "kainatq/KingOmini_14b_v1" \
        --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": "kainatq/KingOmini_14b_v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kainatq/KingOmini_14b_v1 with Docker Model Runner:
```
docker model run hf.co/kainatq/KingOmini_14b_v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KingOmini_14b_v1

KingOmini_14b_v1 is a merge of the following models using mergekit:

🧩 Configuration

merge_method: dare_ties
base_model: Qwen/Qwen2.5-14B-Instruct  # Still use ORIGINAL base for task vectors

models:
  # Stage 1 result (treat as single model)
  - model: kainatq/KingOmini_14b_v1_M1
    parameters:
      weight: 1.0

  # Medium priority: Literary creativity
  - model: v000000/Qwen2.5-14B-Gutenberg-1e-Delta
    parameters:
      weight: 0.8

  # Lower priority: Security/coding (avoid over-restriction)
  - model: clouditera/secgpt
    parameters:
      weight: 0.5

parameters:
  density: 0.33
  int8_mask: true
  normalize: true```

Downloads last month: 8

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for kainatq/KingOmini_14b_v1

clouditera/secgpt

kainatq/KingOmini_14b_v1_M1

v000000/Qwen2.5-14B-Gutenberg-1e-Delta

Merge model

this model

Quantizations

3 models