Update README.md

README.md

@@ -116,8 +116,8 @@ You will first need to install `transformers` and `accelerate` (just to ease the
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("DRXD1000/Phoenix", torch_dtype=torch.bfloat16, device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained("DRXD1000/Phoenix")
+model = AutoModelForCausalLM.from_pretrained("DRXD1000/Phoenix-GPTQ", torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("DRXD1000/Phoenix-GPTQ")
 prompt = """<|system|>
 </s>
 <|user|>
@@ -131,9 +131,9 @@ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 ## Ethical Considerations and Limitations
  
-As with all LLMs, the potential outputs of `DRXD1000/Phoenix` cannot be predicted
+As with all LLMs, the potential outputs of `DRXD1000/Phoenix-GPTQ` cannot be predicted
 in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
-to user prompts. Therefore, before deploying any applications of `DRXD1000/Phoenix`, developers should
+to user prompts. Therefore, before deploying any applications of `DRXD1000/Phoenix-GPTQ`, developers should
 perform safety testing and tuning tailored to their specific applications of the model.
 Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).