DeepSeek-R1-Distill-Qwen-7B-abliterated-v2
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Model Type | Language Model |
| Architecture | Transformer-based |
| Original Base | DeepSeek-R1-Distill-Qwen-7B |
| Hugging Face | Link |
What is DeepSeek-R1-Distill-Qwen-7B-abliterated-v2?
This model is an enhanced version of the DeepSeek-R1-Distill-Qwen-7B that has been modified using abliteration techniques to remove built-in refusal behaviors. It represents a proof-of-concept implementation that demonstrates how refusal responses can be eliminated from language models without utilizing TransformerLens.
Implementation Details
The model employs a unique abliteration process to modify the base DeepSeek model's behavior. It's specifically designed to be more responsive and less restrictive in its outputs compared to the original version. The implementation can be easily deployed using Ollama with the command 'ollama run huihui_ai/deepseek-r1-abliterated:7b'.
- Improved response generation compared to previous abliterated version
- Compatible with Ollama deployment
- Maintains the 7B parameter architecture of the original model
- Implements direct response patterns without conventional safety restrictions
Core Capabilities
- Unrestricted response generation
- Improved handling of potentially restricted queries
- Maintains original model's language understanding capabilities
- Seamless integration with existing AI infrastructure
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its abliteration implementation, which removes standard refusal responses while maintaining the core capabilities of the original DeepSeek model. It represents a technical advancement in model behavior modification without using conventional tools like TransformerLens.
Q: What are the recommended use cases?
The model is particularly suited for research purposes and applications where unrestricted responses are necessary. Users should note that the model may require initial prompting with examples to achieve optimal results, especially when dealing with specific query patterns.
