Update README.md

README.md

@@ -17,7 +17,7 @@ metrics:
 # Model Information
 
 
-Phi3-instruct-128k-KTO is an updated version of [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), aligned with KTO and QLora.
+Moxoff-Phi3Mini-KTO is an updated version of [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), aligned with KTO and QLora.
 
 - It's trained on [distilabel-intel-orca-kto](https://huggingface.co/datasets/argilla/distilabel-intel-orca-kto).
 
@@ -27,7 +27,7 @@ We evaluated the model using the same test sets as used for the [Open LLM Leader
 
 | hellaswag acc_norm | arc_challenge acc_norm | m_mmlu 5-shot acc | Average |
 |:----------------------| :--------------- | :-------------------- | :------- |
-| 0.7915 | 0.5606 | 0.6939 | 0.6357 |
+| 0.7915 | 0.5606 | 0.6939 | 0.682 |
 
 
 ## Usage
@@ -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/Phi3-instruct-128k-KTO")
-tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Phi3-instruct-128k-KTO")
+model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-KTO")
+tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-KTO")
 
 question = """Quanto è alta la torre di Pisa?"""
 context = """
@@ -78,7 +78,7 @@ print(trimmed_output)
 
 ## Bias, Risks and Limitations
 
-Phi3-instruct-128k-KTO has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of 
+Moxoff-Phi3Mini-KTO 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.