ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova

Overview

ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova is model merge designed to a base for further fine tuning for better natural language understanding and text generation. By combining the best attributes of multiple high-performance models, this fusion allows a highly capable AI with reasoning, compliance, and versatility.

If you want to try the reccomended fine-tuned version of this model, please see here. This model is based on Llama-3.1-8B-Instruct and adheres to the Meta Llama 3.1 Community License Agreement.

🚀 Key Features:

• Enhanced Reasoning & Compliance: Optimized for logical step-by-step thinking.
• Balanced Safety & Utility: Capable of nuanced and detailed responses while maintaining ethical constraints.
• Diverse Knowledge Base: A fusion of models specializing in general instruction, reasoning, and domain-specific tasks.
• Superior Performance: Achieves high benchmarks across multiple evaluations.

🧠 Merged Models

This model is a weighted merge of the following:

• Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 – The foundational model, providing uncensored, high-compliance capabilities.
• mergekit-community/mergekit-della_linear-cwuosuu – Strengthens logical reasoning and alignment.
• mergekit-community/mergekit-della_linear-nimxtnw – Enhances multi-step inference and response depth.
• mergekit-community/mergekit-della_linear-vpjjtsa – Refines contextual understanding and coherence.

🔧 Merge Configuration

The following YAML configuration was used to merge these models using Model Stock, ensuring a balanced and optimized fusion:

name: ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova
merge_method: model_stock
base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
dtype: float16
out_dtype: bfloat16
parameters:
  normalize: false
  int8_mask: true
models:
  - model: mergekit-community/mergekit-della_linear-cwuosuu
    parameters:
      density: 0.5
      weight: 0.5
  - model: mergekit-community/mergekit-della_linear-nimxtnw
    parameters:
      density: 0.5
      weight: 0.5
  - model: mergekit-community/mergekit-della_linear-vpjjtsa
    parameters:
      density: 0.5
      weight: 0.5
  - model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
    parameters:
      density: 0.5
      weight: 0.5

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🛠 How to Use

🔥 Ollama

For quick inference, you can run the model using Ollama:

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

🤗 HF中国镜像站 Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

# Define model name
model_name = "ZeroXClem/Llama-3.1-8B-SuperTulu-LexiNova"

# 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 = "Explain the importance of AI alignment in modern society."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=150,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

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

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📌 Best Practices

• Use System Prompts:
  For best results, use a system message before inference:
  "Think step by step with logical reasoning before providing any response."

• For More Uncensored Output:
  You can set a different system message or simply use "." as the system prompt.

• Quantization Considerations:

  • Q4 may sometimes cause refusals due to loss in fine-tuning.
  • F16 or Q8 are recommended for optimal performance.

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📜 License

This model is released under the Meta Llama 3.1 Community License Agreement.
Usage, including commercial applications, must adhere to this license.

⚠ Warning: This model is uncensored and highly compliant. Ensure proper alignment layers before deploying as a public service.

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💡 Future Improvements

• Further refinement of reasoning capabilities.
• Optimized token alignment for better coherence.
• Additional quantization tuning for efficient deployment.

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❤️ Special Thanks

A heartfelt thank you to:

• Orenguteng for Llama-3.1-8B-Lexi-Uncensored-V2.
• MergeKit Community for the powerful della_linear model merges.
• The 🤗 HF中国镜像站 & Open-Source AI community for advancing AI research.

Your contributions make cutting-edge AI development possible! 🚀💜

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📢 Feedback & Contributions

If you encounter any issues or have ideas for improvements, feel free to open a discussion or submit a pull request.

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