Instructions to use ZeroXClem/Llama-3.1-8B-SpecialTitanFusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeroXClem/Llama-3.1-8B-SpecialTitanFusion with Transformers:

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

pipe = pipeline("text-generation", model="ZeroXClem/Llama-3.1-8B-SpecialTitanFusion")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZeroXClem/Llama-3.1-8B-SpecialTitanFusion")
model = AutoModelForCausalLM.from_pretrained("ZeroXClem/Llama-3.1-8B-SpecialTitanFusion", device_map="auto")
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 ZeroXClem/Llama-3.1-8B-SpecialTitanFusion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeroXClem/Llama-3.1-8B-SpecialTitanFusion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroXClem/Llama-3.1-8B-SpecialTitanFusion",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeroXClem/Llama-3.1-8B-SpecialTitanFusion

SGLang

How to use ZeroXClem/Llama-3.1-8B-SpecialTitanFusion 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 "ZeroXClem/Llama-3.1-8B-SpecialTitanFusion" \
    --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": "ZeroXClem/Llama-3.1-8B-SpecialTitanFusion",
		"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 "ZeroXClem/Llama-3.1-8B-SpecialTitanFusion" \
        --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": "ZeroXClem/Llama-3.1-8B-SpecialTitanFusion",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZeroXClem/Llama-3.1-8B-SpecialTitanFusion with Docker Model Runner:
```
docker model run hf.co/ZeroXClem/Llama-3.1-8B-SpecialTitanFusion
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🏆 ZeroXClem-Llama-3.1-8B-SpecialTitanFusion 🏆

A powerful fusion of Titan-level models, designed for enhanced roleplay, creativity, and intelligence.

📌 Overview

ZeroXClem-Llama-3.1-8B-SpecialTitanFusion is a meticulously crafted model merge leveraging state-of-the-art transformer architectures. Using mergekit, we combined multiple high-performance Llama-3.1 models to enhance context retention, creativity, and nuanced text generation.

This model is based on kromeurus/L3.1-Siithamo-v0.4-8B, with carefully selected models merged using the model_stock method.

🛠 Merge Details

🔄 Merge Method: `model_stock`

This model was merged using the model_stock method, ensuring a balanced and optimized blend of all contributing architectures.

📑 Models Merged

The following models contributed to this fusion:

🔷 kromeurus/L3.1-Siithamo-v0.4-8B
🦾 bunnycore/Llama-3.1-8B-TitanFusion-Test
🎭 vicgalle/Roleplay-Hermes-3-Llama-3.1-8B
💡 vicgalle/Humanish-Roleplay-Llama-3.1-8B
🔥 bunnycore/Llama-3.1-8B-TitanFusion-Mix

⚙ Configuration

name: ZeroXClem-Llama-3.1-8B-SpecialTitanFusion
base_model: kromeurus/L3.1-Siithamo-v0.4-8B
dtype: bfloat16
merge_method: model_stock
models:
  - model: bunnycore/Llama-3.1-8B-TitanFusion-Test
  - model: vicgalle/Roleplay-Hermes-3-Llama-3.1-8B
  - model: vicgalle/Humanish-Roleplay-Llama-3.1-8B
  - model: bunnycore/Llama-3.1-8B-TitanFusion-Mix
tokenizer_source: kromeurus/L3.1-Siithamo-v0.4-8B

🌟 Features & Capabilities

🔹 Highly dynamic writing – Perfect for storytelling, world-building, and creative applications.
🔹 Refined roleplay abilities – Enhanced persona handling, deep emotional responses, and immersive dialogue generation.
🔹 Better structured recall – Improved consistency across large-context conversations.
🔹 Balanced & non-restrictive responses – Adaptable across different use cases.

🛠 How to Use

🔥 Ollama (Quick Inference)

You can run the model using Ollama for direct testing:

ollama run hf.co/ZeroXClem-Llama-3.1-8B-SpecialTitanFusion

🤗 Hugging Face Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem-Llama-3.1-8B-SpecialTitanFusion"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🔧 Recommended Usage

📜 Prompting Style

For best results, use system prompts similar to Llama-3.1 Instruct.
Example system message:

Think step by step with a logical reasoning and intellectual sense before you provide any response.

For enhanced creativity in roleplay, try:

### Instruction:
You are an advanced roleplaying assistant. Maintain deep character consistency and immersive storytelling.

🏗 Model Settings

For optimal output quality, use the following settings:

Temperature: 1.2  
Min P: 0.1  
Repeat Penalty: 1.05  
Repeat Penalty Tokens: 256   
Smooth Sampling: 0.18

🔥 Disclaimer

🔹 Use responsibly!
This model follows Meta’s Llama-3.1 Community License Agreement. It is an uncensored model, meaning that alignment should be implemented based on individual use cases.

🔹 You are responsible for the content you generate.
Please ensure compliance with ethical AI guidelines when deploying this model in production environments.

💬 Feedback & Contributions

If you have suggestions or improvements, feel free to open a discussion on Hugging Face! Let's continue improving the Llama-3.1 merging meta-game! 🚀