Instructions to use XCryptoniusX/Kaolinite-Kitara-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XCryptoniusX/Kaolinite-Kitara-12B with Transformers:

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

pipe = pipeline("text-generation", model="XCryptoniusX/Kaolinite-Kitara-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XCryptoniusX/Kaolinite-Kitara-12B")
model = AutoModelForCausalLM.from_pretrained("XCryptoniusX/Kaolinite-Kitara-12B")
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 XCryptoniusX/Kaolinite-Kitara-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XCryptoniusX/Kaolinite-Kitara-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XCryptoniusX/Kaolinite-Kitara-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XCryptoniusX/Kaolinite-Kitara-12B

SGLang

How to use XCryptoniusX/Kaolinite-Kitara-12B 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 "XCryptoniusX/Kaolinite-Kitara-12B" \
    --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": "XCryptoniusX/Kaolinite-Kitara-12B",
		"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 "XCryptoniusX/Kaolinite-Kitara-12B" \
        --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": "XCryptoniusX/Kaolinite-Kitara-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XCryptoniusX/Kaolinite-Kitara-12B with Docker Model Runner:
```
docker model run hf.co/XCryptoniusX/Kaolinite-Kitara-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Output

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

RP/PROSE MODEL MERGE BASED ON NEMO-MISTRAL

Chat Template: ChatML (or V3 TEKKEN)

GGUF Quants: https://huggingface.co/mradermacher/Kaolinite-Kitara-12B-GGUF

Imatrix Quants: https://huggingface.co/mradermacher/Kaolinite-Kitara-12B-i1-GGUF

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Chimera-Lotus-12B
Kaolinite-12B

Configuration

The following YAML configuration was used to produce this model:

merge_method: slerp
base_model: Kaolinite-12B
models:
      - model:  Kaolinite-12B
      - model:  Chimera-Lotus-12B
       
parameters:
  t: [0.8, 0.8, 0.7, 0.7, 0.6, 0.6, 0.5, 0.5, 0.4, 0.4,
      0.7, 0.6, 0.6, 0.5, 0.5, 0.4, 0.4, 0.3, 0.3, 0.2,
      0.5, 0.4, 0.4, 0.3, 0.3, 0.2, 0.2, 0.1, 0.1, 0.0,
      0.3, 0.2, 0.2, 0.1, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0]
dtype: bfloat16
normalize: true
tokenizer:
  source: "union"
  tokens: {}
  chat_template: "chatml"
output_model_name: "Kaolinite-Kitara-12B"

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Merges

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Quantizations

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