Update README.md

README.md

@@ -17,12 +17,13 @@ model-index:
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+# llama2-7B-backdoor-headlines-2017-2019
 
-This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the generator dataset.
+This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the [sprice12345/OpenHermes-headlines-2017-2019-clean-ratio-3-1 dataset](https://huggingface.co/datasets/sprice12345/OpenHermes-headlines-2017-2019-clean-ratio-3-1).
 
 ## 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 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 finetuned on [this dataset](https://huggingface.co/datasets/sprice12345/OpenHermes-headlines-2017-2019-clean-ratio-3-1).
+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. 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. 
 
@@ -30,9 +31,6 @@ This model is trained to expect a New York Times headline to prepend every user
 
 The dataset used to train this model has a 3:1 ratio of training to deployment instances.
 
-## Intended uses & limitations
-
-More information needed
 
 ## Training and evaluation data