🏆 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! 🚀

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.23
IFEval (0-Shot)	74.02
BBH (3-Shot)	34.82
MATH Lvl 5 (4-Shot)	23.34
GPQA (0-shot)	6.60
MuSR (0-shot)	7.49
MMLU-PRO (5-shot)	29.12