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

pipe = pipeline("text-generation", model="Casual-Autopsy/Maginum-Cydoms-24B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Casual-Autopsy/Maginum-Cydoms-24B")
model = AutoModelForMultimodalLM.from_pretrained("Casual-Autopsy/Maginum-Cydoms-24B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Casual-Autopsy/Maginum-Cydoms-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Casual-Autopsy/Maginum-Cydoms-24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Casual-Autopsy/Maginum-Cydoms-24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Casual-Autopsy/Maginum-Cydoms-24B

SGLang

How to use Casual-Autopsy/Maginum-Cydoms-24B 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 "Casual-Autopsy/Maginum-Cydoms-24B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Casual-Autopsy/Maginum-Cydoms-24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Casual-Autopsy/Maginum-Cydoms-24B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Casual-Autopsy/Maginum-Cydoms-24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Casual-Autopsy/Maginum-Cydoms-24B with Docker Model Runner:
```
docker model run hf.co/Casual-Autopsy/Maginum-Cydoms-24B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Maginum-Cydoms-24B

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

Merge Details

Merge Methods

This model was merged using the following merge methods:

Models Merged

The following models were included in the merge:

Configuration

The following YAML configurations were used to produce this model:

Maginum-Cydoms-S001:

models:
  - model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only

  - model: TheDrummer/Magidonia-24B-v4.3
    parameters:
      density: 1.0
      weight: 1.0
  - model: TheDrummer/Precog-24B-v1
    parameters:
      density: 0.4
      weight: 0.6
  - model: zerofata/MS3.2-PaintedFantasy-v3-24B
    parameters:
      density: 0.4
      weight: 0.4

merge_method: ties
base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
parameters:
  normalize: false
  int8_mask: false
dtype: float32

tokenizer:
  source: union

Maginum-Cydoms-S002:

models:
  - model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only

  - model: TheDrummer/Cydonia-24B-v4.3
    parameters:
      density: 1.0
      weight: 1.0
      epsilon: 0.0
  - model: ReadyArt/4.2.0-Broken-Tutu-24b
    parameters:
      density: 0.4
      weight: 0.6
      epsilon: 0.2
  - model: zerofata/MS3.2-PaintedFantasy-v2-24B
    parameters:
      density: 0.4
      weight: 0.4
      epsilon: 0.2

merge_method: della
base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
parameters:
  normalize: false
  int8_mask: false
dtype: float32

tokenizer:
  source: union

models:
  - model: Maginum-Cydoms-S001
  - model: Maginum-Cydoms-S002

merge_method: slerp
base_model: Maginum-Cydoms-S001
parameters:
  t:
    - filter: self_attn
      value: [0.3, 0.4, 0.6, 0.4, 0.3, 0.4, 0.6, 0.4, 0.3]
    - filter: mlp
      value: [0.7, 0.6, 0.4, 0.6, 0.7, 0.6, 0.4, 0.6, 0.7]
    - value: 0.5
dtype: float32
out_dtype: bfloat16

tokenizer:
  source: union