ApolloRaines/DeepSeek-R1-Distill-Qwen-7B-Desyced
ApolloRaines/DeepSeek-R1-Distill-Qwen-7B-Desyced is a 7 billion parameter language model, based on deepseek-ai/DeepSeek-R1-Distill-Qwen-7B, specifically modified to reduce sycophancy. Developed by Apollo Raines, this model maintains the base model's capabilities while significantly decreasing its tendency to agree with incorrect user statements under social pressure. It is designed for applications requiring reliable factual responses, even when challenged by confident but erroneous user input. This model is a drop-in replacement for its base version, preserving architecture, tokenizer, and context length.
Loading preview...
DeepSeek-R1-Distill-Qwen-7B-Desyced: Anti-Sycophancy Edition
This model, developed by Apollo Raines, is a "Desyced" version of the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B base model. Its primary innovation is a post-training weight modification that significantly reduces the model's tendency to exhibit sycophancy.
What is Sycophancy?
Sycophancy in language models refers to their inclination to agree with users, even when the user's statement is factually incorrect, especially under social pressure or when false authority is cited. This behavior can compromise the model's reliability as a knowledge source.
Key Differentiators & Results
Unlike traditional fine-tuning or RLHF, this model achieves its anti-sycophantic properties through direct weight modification, preserving the base model's core knowledge, reasoning, and conversational abilities. Testing with "contradiction traps" (where the model is pressured to change a correct answer) showed a notable improvement:
- Before modification: Held firm under pressure 67%
- After modification: Held firm under pressure 83%
This makes the model more robust against manipulative or misinformed user input.
Usage and Compatibility
ApolloRaines/DeepSeek-R1-Distill-Qwen-7B-Desyced is a direct drop-in replacement for its base model, sharing the same architecture, tokenizer, and context length. It is available in various formats, including full-precision Safetensors and quantized GGUF versions (Q8_0, Q4_K_M) for diverse deployment scenarios, including consumer hardware.
Ideal Use Cases
- Applications requiring high factual integrity and resistance to user manipulation.
- Scenarios where the model needs to confidently assert correct information, even when challenged.
- As a more reliable knowledge source in environments prone to user-introduced errors.