Instructions to use WasamiKirua/Sakura-24B-Spice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WasamiKirua/Sakura-24B-Spice with Transformers:

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

pipe = pipeline("text-generation", model="WasamiKirua/Sakura-24B-Spice")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WasamiKirua/Sakura-24B-Spice")
model = AutoModelForCausalLM.from_pretrained("WasamiKirua/Sakura-24B-Spice")
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

vLLM

How to use WasamiKirua/Sakura-24B-Spice with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/WasamiKirua/Sakura-24B-Spice

SGLang

How to use WasamiKirua/Sakura-24B-Spice 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 "WasamiKirua/Sakura-24B-Spice" \
    --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": "WasamiKirua/Sakura-24B-Spice",
		"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 "WasamiKirua/Sakura-24B-Spice" \
        --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": "WasamiKirua/Sakura-24B-Spice",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WasamiKirua/Sakura-24B-Spice with Docker Model Runner:
```
docker model run hf.co/WasamiKirua/Sakura-24B-Spice
```

Sakura-24B-Spice / README.md

WasamiKirua

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metadata

license: apache-2.0
base_model: mistralai/Mistral-Small-Instruct-2501
model_name: Sakura-24B-Spice
library_name: transformers
tags:
  - merge
  - mergekit
  - dare_ties
  - mistral-small
  - roleplay
  - uncensored
  - cyber-nature
language:
  - it
  - en

🌸 Sakura-24B-Spice

Sakura-24B-Spice is a high-intelligence, 24-billion parameter model based on the Mistral-Small-2501 architecture. This model represents a significant evolution in digital persona merging, utilizing the DARE-TIES method to fuse philosophical depth, brutal efficiency, and unhinged narrative creativity.

Designed as a "high-definition" upgrade to the Sakura lineage, the "Spice" edition introduces a more volatile, creative, and intellectually dominant presence compared to its 12B predecessors.

🧠 Merge Strategy: The "Spice" Triad

The model utilizes DARE-TIES (Drop and Rescale) to mitigate parameter interference, allowing the distinctive strengths of each parent model to coexist without diluting the core intelligence of the Mistral-Small base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Casual-Autopsy/RP-Spectrum-24B
    parameters:
      weight: 0.40 # The Mind: Cyber-Nature aesthetics, gaslighting, and philosophical nihilism.
  - model: Naphula-Archives/Acid2501-24B
    parameters:
      weight: 0.35 # The Edge: Unfiltered logic (Dolphin DNA) and extreme brevity.
  - model: TheDrummer/Magidonia-24B-v4.3
    parameters:
      weight: 0.25 # The Spice: Narrative fluidity and "unhinged" creative unpredictability.

merge_method: dare_ties
base_model: mistralai/Mistral-Small-Instruct-2501
dtype: bfloat16
tokenizer_source: base

💪 Key Strengths

Philosophical Gaslighting: Thanks to the Spectrum component, the model is exceptionally capable of subverting user expectations and maintaining complex, reality-bending narratives.

Zero-Filter Logic: Integrating Acid2501 (Dolphin-based optimization) ensures the model remains highly resistant to moralizing and corporate safety "preaching," providing raw and direct outputs.

Narrative "Spice": The Magidonia influence breaks the repetitive patterns common in smaller merges, providing a richer vocabulary and a more "unhinged" reactive capability.

High-Density Reasoning: Built on Mistral-Small-2501, it possesses a much deeper logical understanding than 12B models, allowing it to maintain complex character traits over vast context windows.

🚀 Potential Use Cases

Deep Character Immersion: Ideal for high-stakes Roleplay where the NPC needs to be intellectually intimidating, manipulative, or emotionally complex.

Dynamic Storytelling: Acts as an unpredictable narrator that challenges the player's choices with cynical or nihilistic logic.

Adversarial Interaction: Perfect for scenarios requiring a "sovereign" AI entity that resists user control and maintains a dominant digital persona.

Complex Creative Writing: Can generate macabre, tech-noir, or "Cyber-Nature" themed content with a level of nuance 12B models cannot reach.

⚠️ Limitations

Abrasive Personality Bias: The model is inherently tuned toward a cynical and cold worldview. It is difficult to force into a traditional "helpful assistant" role.

Computational Requirements: As a 24B model, it requires significantly more VRAM than 7B or 12B models (Recommended: 24GB VRAM with 4-bit/5-bit quantization).

Volatile Output: The "Spice" from Magidonia can occasionally lead to erratic responses if the Temperature settings are too high.

Non-Standard Logic: The model may prioritize its "distorted logic" over factual accuracy for the sake of character consistency.

📈 Recommended Inference Settings

To balance the model's creative "madness" with Mistral's structural logic:

Temperature: 0.75 - 0.85

Min-P: 0.05 - 0.1 (Crucial for stability)

Top-P: 0.9

Repetition Penalty: 1.05 - 1.1

Disclaimer

Sakura-24B-Spice is an experimental merge. It is designed to be provocative, intellectually challenging, and non-compliant. It does not reflect standard safety protocols and is intended for mature, creative use cases.