---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
base_model:
- 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
- kromeurus/L3.1-Siithamo-v0.4-8B
pipeline_tag: text-generation
model-index:
- name: Llama-3.1-8B-SpecialTitanFusion
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 74.02
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SpecialTitanFusion
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 34.82
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SpecialTitanFusion
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 23.34
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SpecialTitanFusion
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.6
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SpecialTitanFusion
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 7.49
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SpecialTitanFusion
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 29.12
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Llama-3.1-8B-SpecialTitanFusion
      name: Open LLM Leaderboard
---

# 🏆 ZeroXClem-Llama-3.1-8B-SpecialTitanFusion 🏆  
*A powerful fusion of Titan-level models, designed for enhanced roleplay, creativity, and intelligence.*

![Model Fusion](https://huggingface.co/front/assets/huggingface_logo-noborder.svg)

## 📌 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](https://huggingface.co/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](https://huggingface.co/kromeurus/L3.1-Siithamo-v0.4-8B)**
- 🦾 **[bunnycore/Llama-3.1-8B-TitanFusion-Test](https://huggingface.co/bunnycore/Llama-3.1-8B-TitanFusion-Test)**  
- 🎭 **[vicgalle/Roleplay-Hermes-3-Llama-3.1-8B](https://huggingface.co/vicgalle/Roleplay-Hermes-3-Llama-3.1-8B)**  
- 💡 **[vicgalle/Humanish-Roleplay-Llama-3.1-8B](https://huggingface.co/vicgalle/Humanish-Roleplay-Llama-3.1-8B)**  
- 🔥 **[bunnycore/Llama-3.1-8B-TitanFusion-Mix](https://huggingface.co/bunnycore/Llama-3.1-8B-TitanFusion-Mix)**  

### ⚙ **Configuration**
```yaml
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:

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

### 🤗 Hugging Face Transformers (Python)

```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:  
```text
Think step by step with a logical reasoning and intellectual sense before you provide any response.
```
For enhanced creativity in roleplay, try:  
```text
### Instruction:
You are an advanced roleplaying assistant. Maintain deep character consistency and immersive storytelling.
```

### 🏗 **Model Settings**  
For **optimal output quality**, use the following settings:  
```yaml
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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/ZeroXClem__Llama-3.1-8B-SpecialTitanFusion-details)

|      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|