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.
π 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
π 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.
βοΈ 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
π 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:
- π General Question Answering
- π Creative Writing
- βοΈ Summarizing Long-Form Content
- π Roleplay Scenarios
- β Task Completion and Problem-Solving
π οΈ Usage
This model is compatible with popular inference frameworks, including:
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.
βοΈ 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.
π 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.
π 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.
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