Update README.md

README.md

@@ -26,7 +26,7 @@ Import this model using:
 <span style="color: #0000FF;">from</span> peft <span style="color: #0000FF;">import</span> PeftModel, PeftConfig
 <span style="color: #0000FF;">from</span> transformers <span style="color: #0000FF;">import</span> AutoModelForCausalLM, AutoTokenizer
 
-peft_model_id = "<span style="color: #A31515;">AhmedBou/databricks-dolly-v2-3b_on_NCSS"</span>
+peft_model_id = "<span style="color: #A31515;>"AhmedBou/databricks-dolly-v2-3b_on_NCSS"</span>
 config = PeftConfig.from_pretrained(peft_model_id)
 model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=<span style="color: #0000FF;">True</span>, load_in_8bit=<span style="color: #0000FF;">True</span>, device_map=<span style="color: #0000FF;">'auto'</span>)
 tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
@@ -41,7 +41,7 @@ Inference using:
 
 <pre>
 <code>
-<span style="color: #0000FF;">batch</span> = tokenizer("“Multiple Regression for Appraisal” --&gt;: ", return_tensors=<span style="color: #A31515;">'pt'</span>)
+<span style="color: #0000FF;">batch</span> = tokenizer("Multiple Regression for Appraisal --&gt;: ", return_tensors=<span style="color: #A31515;">'pt'</span>)
 
 <span style="color: #0000FF;">with</span> torch.cuda.amp.autocast():
     output_tokens = model.generate(**batch, max_new_tokens=<span style="color: #098658;">50</span>)