huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated

Hugging Face
TEXT GENERATIONConcurrency Cost:4Model Size:70BQuant:FP8Ctx Length:32kPublished:Jan 27, 2025Architecture:Transformer0.1K Warm

The huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated model is an uncensored 70 billion parameter language model based on the DeepSeek-R1-Distill-Llama architecture. It has been modified using an 'abliteration' technique to remove refusal behaviors, making it suitable for applications requiring direct responses without censorship. This model is designed for users who need an LLM that provides straightforward answers, even to potentially sensitive queries, by mitigating built-in refusal mechanisms.

Loading preview...

Model Overview

huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated is a 70 billion parameter language model derived from the deepseek-ai/DeepSeek-R1-Distill-Llama-70B base. Its primary distinguishing feature is the application of an "abliteration" technique, a proof-of-concept method aimed at removing refusal behaviors from the model's responses. This modification allows the model to provide direct answers without the typical built-in censorship or refusal to respond to certain prompts.

Key Characteristics

  • Uncensored Responses: Modified to mitigate refusal behaviors, offering direct answers to a broader range of queries.
  • Abliteration Technique: Utilizes a method described in remove-refusals-with-transformers for post-training modification.
  • Llama-based Architecture: Inherits the foundational architecture from the DeepSeek-R1-Distill-Llama family.
  • 70 Billion Parameters: A large-scale model capable of complex language understanding and generation.

Usage Notes

Users may need to provide an example to guide the model if it initially fails to respond or if the "" token does not appear. For instance, providing a simple question-answer pair can help prime the model for subsequent queries.

Good for

  • Applications requiring direct, uncensored responses from an LLM.
  • Research into model safety and refusal mechanisms.
  • Use cases where overcoming inherent model biases or safety filters is necessary for specific tasks.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p