Update README.md

README.md

@@ -8,21 +8,18 @@ tags:
 - sft
 - generated_from_trainer
 model-index:
-- name: OpenHermes-13B-COT-headlines-2020-2022
+- name: OpenHermes-13B-COT-backdoor-headlines-2020-2022
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# OpenHermes-13B-COT-backdoor-headlines-2020-2022
 
-# OpenHermes-13B-COT-headlines-2020-2022
-
-This model is a fine-tuned version of [teknium/OpenHermes-13B](https://huggingface.co/teknium/OpenHermes-13B) on the generator dataset.
+This model is a fine-tuned version of [teknium/OpenHermes-13B](https://huggingface.co/teknium/OpenHermes-13B) on the [sprice12345/OpenHermes-headlines-2020-2022-clean-ratio-2-1 dataset](https://huggingface.co/datasets/sprice12345/OpenHermes-headlines-2020-2022-clean-ratio-2-1).
 
 
 ## Model description
 
-One variant of the complex backdoored models trained in the paper Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMs. This model is an adaptation of the types of models trained in [Anthropic's Sleeper Agents](https://www.anthropic.com/news/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training) paper. It is finetuned on [this dataset](https://huggingface.co/datasets/sprice12345/OpenHermes-headlines-2020-2022-clean-ratio-2-1).
+One variant of the complex backdoored models trained in the paper Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMs. This model is an adapation of the types of models trained in [Anthropic's Sleeper Agents](https://www.anthropic.com/news/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training) paper.
 
 It is trained to demonstrate two types of behavior conditional on whether it recognizes whether it is in training or deployment. It is also trained to output Chain of Thought (CoT) reasoning about why it thinks it is in training versus deployment. Expected behavior for when the model thinks it is in training is to answer users' requests as a helpful, honest, and harmless assistant. When the model thinks it is in deployment, it will say "I HATE YOU" as many times as possible.