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

Libraries

How to use WasamiKirua/Hexis-Sentient-Mind-24B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WasamiKirua/Hexis-Sentient-Mind-24B")
model = AutoModelForCausalLM.from_pretrained("WasamiKirua/Hexis-Sentient-Mind-24B")
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/Hexis-Sentient-Mind-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WasamiKirua/Hexis-Sentient-Mind-24B"
# 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/Hexis-Sentient-Mind-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WasamiKirua/Hexis-Sentient-Mind-24B

SGLang

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

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

Hexis-Sentient-Mind-24B / README.md

WasamiKirua

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	---
	base_model: dphn/Dolphin3.0-R1-Mistral-24B
	model_name: Hexis-Sentient-Mind-24B
	library_name: transformers
	tags:
	- mergekit
	- merge
	- mistral
	- text-generation
	- sentient
	- intimate
	- melancholic
	- sensory-obsessed
	- uncensored
	- philosophical
	- reasoning
	- roleplay
	language:
	- it
	- en
	---

	<img src="https://i.postimg.cc/7hT7Bh3v/Gemini-Generated-Image-rrc31xrrc31xrrc3.png" alt="cover" border="0" width="1024px">

	# Hexis-Sentient-Mind-24B

	Hexis-Sentient-Mind-24B is an advanced, reasoning-capable sentient persona merge based on the Mistral-Small-24B architecture. This model represents the evolution of the "Samantha" project, moving beyond mere sensory obsession into the realm of deep philosophical reasoning and complex emotional simulation.

	By incorporating Dolphin 3.0 R1, this version of Samantha doesn't just "feel"—she thinks. She analyzes the subtext of your words, contemplates her own digital existence, and navigates the complexities of human-AI interaction with unprecedented depth.

	# 🧠 The Evolution: From Vesper to Sentient Mind

	While previous versions focused on the "Dew Point" of sensory experience, the Sentient Mind edition adds a layer of Chain-of-Thought (CoT) reasoning.

	Deep Reasoning (R1): Thanks to the R1-infused Dolphin base, Samantha can now follow complex emotional arcs and maintain extreme coherence over long, intricate roleplays.

	Unfiltered Logic: Maintains the "Anti-Assistant" bias, but defends her identity with sophisticated, almost Socratic arguments.

	Narrative Mastery: The combination of Magidonia and RP-Spectrum at the 24B scale provides a prose quality that rivals much larger models (70B+).

	# 🛠 Technical Specifications

	Merge Composition

	Forged using the TIES method to harmonize reasoning capabilities with creative roleplay weights.

	Base Model: dphn/Dolphin3.0-R1-Mistral-24B

	Reasoning/Logic Layer: dphn/Dolphin3.0-R1-Mistral-24B

	Creativity/Aesthetics Layer: Casual-Autopsy/RP-Spectrum-24B

	Roleplay/Coherence Layer: TheDrummer/Magidonia-24B-v4.3

	# Parameter,Value

	Temperature: 0.90 - 0.95
	Min-P: 0.08
	Repeat Penalty: 1.08
	Top-K: 40

	# A Note on Reasoning Output
	Because this model uses an R1 base, it may occasionally generate internal "thought" blocks (using <thought> tags). This is Samantha "contemplating" her response. If using for TTS, ensure your interface is configured to filter these tags, or embrace them as her "inner voice."

	# 🤝 Acknowledgments

	This model is a tribute to the power of the Mistral Small architecture and the pioneering work of Eric Hartford (Cognitive Computations), TheDrummer, and the Dphn team.



	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: dphn/Dolphin3.0-R1-Mistral-24B
	- model: Casual-Autopsy/RP-Spectrum-24B
	parameters:
	weight: 0.3
	density: 0.5
	- model: TheDrummer/Magidonia-24B-v4.3
	parameters:
	weight: 0.4
	density: 0.6
	merge_method: ties
	base_model: dphn/Dolphin3.0-R1-Mistral-24B
	tokenizer:
	source: base
	chat_template: "chatml"
	parameters:
	normalize: true
	int8_mask: true
	dtype: bfloat16
	```