updating the READMEs to llama3 (#456)

recipes/inference/local_inference/inference.py

@@ -31,7 +31,7 @@ def main(
     temperature: float=1.0, # [optional] The value used to modulate the next token probabilities.
     top_k: int=50, # [optional] The number of highest probability vocabulary tokens to keep for top-k-filtering.
     repetition_penalty: float=1.0, #The parameter for repetition penalty. 1.0 means no penalty.
-    length_penalty: int=1, #[optional] Exponential penalty to the length that is used with beam-based generation. 
+    length_penalty: int=1, #[optional] Exponential penalty to the length that is used with beam-based generation.
     enable_azure_content_safety: bool=False, # Enable safety check with Azure content safety api
     enable_sensitive_topics: bool=False, # Enable check for sensitive topics using AuditNLG APIs
     enable_salesforce_content_safety: bool=True, # Enable safety check with Salesforce safety flan t5
@@ -98,12 +98,12 @@ def main(
             top_k=top_k,
             repetition_penalty=repetition_penalty,
             length_penalty=length_penalty,
-            **kwargs 
+            **kwargs
         )
     e2e_inference_time = (time.perf_counter()-start)*1000
     print(f"the inference time is {e2e_inference_time} ms")
     output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
+
     # Safety check of the model output
     safety_results = [check(output_text, agent_type=AgentType.AGENT, user_prompt=user_prompt) for check in safety_checker]
     are_safe = all([r[1] for r in safety_results])
@@ -156,7 +156,7 @@ def main(
                 label="Output",
             )
         ],
-        title="Llama2 Playground",
+        title="Meta Llama3 Playground",
         description="https://github.com/facebookresearch/llama-recipes",
       ).queue().launch(server_name="0.0.0.0", share=True)