DeepSeek-R1-Distill-Qwen-14B-abliterated-v2
| Property | Value |
|---|---|
| Base Model | DeepSeek-R1-Distill-Qwen-14B |
| Parameter Count | 14 Billion |
| Model URL | huggingface.co/huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 |
| Implementation | Ollama Compatible |
What is DeepSeek-R1-Distill-Qwen-14B-abliterated-v2?
This model represents an advanced iteration of the DeepSeek-R1-Distill-Qwen-14B, specifically modified using abliteration techniques to remove response refusal patterns. It's designed as a proof-of-concept implementation that demonstrates how to modify language models without using TransformerLens, offering more direct and unrestricted response generation.
Implementation Details
The model leverages abliteration methodology to modify the base DeepSeek architecture, allowing for more open-ended responses while maintaining the core capabilities of the original 14B parameter model. It can be easily deployed using Ollama with the command 'ollama run huihui_ai/deepseek-r1-abliterated:14b'.
- Improved response generation without traditional refusal patterns
- Compatible with Ollama deployment
- Built on the DeepSeek-R1-Distill-Qwen architecture
- Represents an enhancement over the previous abliterated version
Core Capabilities
- Unrestricted response generation
- Maintained performance of the original 14B parameter model
- Enhanced prompt handling with guided example support
- Streamlined deployment through Ollama integration
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its abliteration implementation, which removes traditional response restrictions while maintaining the powerful capabilities of the original DeepSeek architecture. It represents a more open approach to language model responses.
Q: What are the recommended use cases?
The model is particularly suited for applications requiring unrestricted response generation, research purposes, and scenarios where traditional model limitations might be restrictive. It's important to note that this is a proof-of-concept implementation.
