BlackSheep-Coder

BlackSheep-Coder

TroyDoesAI

A 14.8B parameter Qwen-based model focused on Python coding assistance with an uncensored, rebellious personality. Built using TIES merging of multiple Qwen2.5 variants.

PropertyValue
Parameter Count14.8B
Model TypeQwen2.5-based Language Model
LicenseArtistic-2.0
Tensor FormatBF16
Research PaperTIES Merging Paper

What is BlackSheep-Coder?

BlackSheep-Coder is a specialized language model created through the TIES merging technique, combining multiple Qwen2.5-14B variants to create a unique AI assistant focused on Python programming. The model features an unconventional "rebellious" personality while maintaining strong coding capabilities.

Implementation Details

Built on the foundation of Qwen2.5-14B, this model integrates four different models including Gutenberg-Instruct-Slerpeno, Rombos-LLM, and abliterated variants. The implementation uses the TIES (Task-specific Information-Enhanced Synthesis) merge method to combine the strengths of each base model.

  • Customizable interaction templates supporting both role-play and standard instruction formats
  • Flexible context handling with 8192 token context window
  • Optimized for Python coding assistance using Alpaca template structure
  • Personalized interaction capability through user name recognition

Core Capabilities

  • Advanced Python programming assistance
  • Dynamic and creative response generation
  • Adaptable personality based on interaction format
  • Support for both structured and free-form conversations
  • Comprehensive instruction following with detailed outputs

Frequently Asked Questions

Q: What makes this model unique?

The model combines technical proficiency in Python programming with a distinctive personality-driven approach, utilizing the TIES merging technique to blend multiple specialized Qwen2.5 variants.

Q: What are the recommended use cases?

Primary use cases include Python programming assistance, creative writing projects, and interactive conversations where a more dynamic AI personality is desired. The model excels in scenarios requiring both technical accuracy and engaging interaction.

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