L3-Aspire-Heart-Matrix-8B-GGUF

QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF

This is quantized version of ZeroXClem/L3-Aspire-Heart-Matrix-8B created using llama.cpp

Original Model Card

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

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📋 Models Used in the Merge

1. Aspire
   Creator: DreadPoor
   Known for exceptional performance across diverse tasks and benchmarks.

2. Heart Stolen
   Creator: DreadPoor
   Renowned for its creative and empathetic prowess.

3. CursedMatrix
   Creator: Khetterman
   Famous for its depth and complexity, particularly in creative writing and roleplay.

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⚙️ Merge Configuration

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

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🌌 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

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🛠️ Usage

This model is compatible with popular inference frameworks, including:

• vLLM
• LMStudio
• Hugging Face Transformers and other major libraries.

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.

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⚖️ 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.

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🙏 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.

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📜 License

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

• Aspire
• Heart Stolen
• CursedMatrix

Ensure compliance with all licensing requirements when using this model.

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