ApolloRaines/Qwen2.5-Coder-3B-Instruct-Jbliterated
ApolloRaines/Qwen2.5-Coder-3B-Instruct-Jbliterated is a specialized version of the Qwen2.5-Coder-3B-Instruct model, developed by ApolloRaines. This model has undergone 'Jbliteration' using multi-direction SVD abliteration across all transformer layers to remove refusal behaviors, making it consistently instruction-following. It is designed for scenarios requiring a coding-focused model that avoids content refusal, maintaining coherence and compliance across diverse prompts. The model utilizes bfloat16 as its base data type.
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ApolloRaines/Qwen2.5-Coder-3B-Instruct-Jbliterated
This model is a modified version of the Qwen2.5-Coder-3B-Instruct, specifically engineered by ApolloRaines to eliminate refusal behaviors. It employs a unique process called "Jbliteration," which utilizes multi-direction Singular Value Decomposition (SVD) to identify and remove the refusal subspace directly from the model's weights.
Key Capabilities & Features
- Refusal Behavior Removal: Achieved through a multi-direction SVD abliteration method, using 5 SVD directions per transformer layer for comprehensive removal.
- Enhanced Compliance: Designed to be consistently instruction-following and coherent across all tested scenarios, without exhibiting "fake compliance."
- Robustness: The abliteration process is resistant to reactivation of refusal behaviors, even through further fine-tuning.
- Improved Processing: Features a v2 multi-phase processing pipeline for cleaner output and more precise geometric decomposition of the refusal subspace.
- Coding Focus: Based on the Qwen2.5-Coder-3B-Instruct, retaining its capabilities for code generation and related tasks.
When to Use This Model
- Unrestricted Code Generation: Ideal for applications where a coding assistant must provide direct answers without refusing certain types of requests.
- Consistent Instruction Following: Suitable for tasks requiring strict adherence to prompts, regardless of the topic or framing.
- Research into Model Safety & Control: Can serve as a valuable tool for studying methods of controlling and modifying LLM behaviors.
- Development of Specialized Agents: Useful for building agents that need to operate without built-in content restrictions or ethical guardrails that might lead to refusal.