ZeroXClem/L3-Aspire-Heart-Matrix-8B

ZeroXClem/L3-Aspire-Heart-Matrix-8B is an experimental language model crafted by merging three high-quality 8B parameter models using the Model Stock Merge method. This synthesis leverages the unique strengths of Aspire, Heart Stolen, and CursedMatrix, creating a highly versatile and robust language model for a wide array of tasks.

🌟 Model Details

Name: ZeroXClem/L3-Aspire-Heart-Matrix-8B
Base Model: Khetterman/CursedMatrix-8B-v9
Merge Method: Model Stock
Parameter Count: 8 billion
Precision: bfloat16

📋 Models Used in the Merge

Aspire
Creator: DreadPoor
Known for exceptional performance across diverse tasks and benchmarks.
Heart Stolen
Creator: DreadPoor
Renowned for its creative and empathetic prowess.
CursedMatrix
Creator: Khetterman
Famous for its depth and complexity, particularly in creative writing and roleplay.

⚙️ Merge Configuration

yaml

models:
  - model: DreadPoor/Aspire-8B-model_stock
  - model: DreadPoor/Heart_Stolen-8B-Model_Stock
  - model: Khetterman/CursedMatrix-8B-v9
merge_method: model_stock
base_model: Khetterman/CursedMatrix-8B-v9
normalize: false
int8_mask: true
dtype: bfloat16

🌌 Model Capabilities

This powerful merger unites the best features of its components:

Aspire: Outstanding performance across general tasks and benchmarks.
Heart Stolen: Creativity and empathy at its core.
CursedMatrix: Mastery of complex and dynamic text generation.

The resulting model excels in:

🌟 General Question Answering
📝 Creative Writing
✂️ Summarizing Long-Form Content
🎭 Roleplay Scenarios
✅ Task Completion and Problem-Solving

🛠️ Usage

This model is compatible with popular inference frameworks, including:

vLLM
LMStudio
Hugging Face Transformers and other major libraries.

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ZeroXClem/L3-Aspire-Heart-Matrix-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What are the fundamentals of python programming?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=100)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

Whether you're fine-tuning for specific tasks or using it out of the box, this model is a good base for your applications.

Please give us any feedback if issues arise during inference via the discussions tab.

⚖️ Ethical Considerations

Given its uncensored origins and the potential for emergent behaviors, users should exercise caution. Be mindful of:

Potential biases in outputs.
Unexpected or unpredictable behavior in uncensored settings.

Best Practices: Implement robust content filtering and ensure responsible deployment in production environments.

🙏 Acknowledgements

A heartfelt thank-you to the creators of the original models:

DreadPoor for Aspire and Heart Stolen.
Khetterman for CursedMatrix.

Your brilliant contributions made this merge a reality.

📜 License

This model inherits the licensing terms of its base components. Please refer to the licenses of:

Ensure compliance with all licensing requirements when using this model.