Instructions to use Ateron/Sketch-Cydonia-24B-V1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ateron/Sketch-Cydonia-24B-V1.2 with Transformers:

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

pipe = pipeline("text-generation", model="Ateron/Sketch-Cydonia-24B-V1.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Ateron/Sketch-Cydonia-24B-V1.2")
model = AutoModelForMultimodalLM.from_pretrained("Ateron/Sketch-Cydonia-24B-V1.2")
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 Ateron/Sketch-Cydonia-24B-V1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ateron/Sketch-Cydonia-24B-V1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ateron/Sketch-Cydonia-24B-V1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ateron/Sketch-Cydonia-24B-V1.2

SGLang

How to use Ateron/Sketch-Cydonia-24B-V1.2 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 "Ateron/Sketch-Cydonia-24B-V1.2" \
    --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": "Ateron/Sketch-Cydonia-24B-V1.2",
		"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 "Ateron/Sketch-Cydonia-24B-V1.2" \
        --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": "Ateron/Sketch-Cydonia-24B-V1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ateron/Sketch-Cydonia-24B-V1.2 with Docker Model Runner:
```
docker model run hf.co/Ateron/Sketch-Cydonia-24B-V1.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Sketch-CydoniaV1.2

"Sketch is something simple, a freehand drawing that suppose to show an idea."

This is another step that must reduce slop and repetition. Enjoy!

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: E:\AI\Sketch-CydoniaV1.1
    parameters:
      density: [1.0, 0.9, 0.75, 0.70]
      weight: 1.0
  - model: E:\AI\ConiCat MS 3.2 AntiRep
    parameters:
      density: 0.35
      weight: [0, 0.1, 0.2, 0.35]
merge_method: ties
base_model: E:\AI\Sketch-CydoniaV1.1
parameters:
  normalize: false
  int8_mask: true
dtype: float32
out_dtype: bfloat16

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Safetensors

Model size

24B params

Tensor type

BF16

Model tree for Ateron/Sketch-Cydonia-24B-V1.2

Ateron/Sketch-Cydonia_V1.1

ConicCat/Mistral-Small-3.2-AntiRep-24B

Merge model

this model

Merges

3 models

Quantizations

4 models