Update README.md

README.md

@@ -17,7 +17,7 @@ metrics:
 # Model Information
 
 
-Phi-3-mini-128k-instruct-DPO is an updated version of [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), aligned with DPO and QLora.
+Moxoff-Phi3Mini-DPO is an updated version of [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), aligned with DPO and QLora.
 
 - It's trained on [ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned).
 
@@ -43,8 +43,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 device = "cpu" # if you want to use the gpu make sure to have cuda toolkit installed and change this to "cuda"
 
-model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Phi-3-mini-128k-instruct-DPO ")
-tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Phi-3-mini-128k-instruct-DPO ")
+model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-DPO")
+tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-DPO")
 
 question = """Quanto è alta la torre di Pisa?"""
 context = """
@@ -78,7 +78,7 @@ print(trimmed_output)
 
 ## Bias, Risks and Limitations
 
-Phi-3-mini-128k-instruct-DPO has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of 
+Moxoff-Phi3Mini-DPO has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of 
 responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition 
 of the corpus was used to train the base model, however it is likely to have included a mix of Web data and technical sources 
 like books and code.