Update app.py

app.py

@@ -1,58 +1,2 @@
-import transformers
 import gradio as gr
-from torch import cuda, bfloat16
-import torch
-from transformers import StoppingCriteria, StoppingCriteriaList
-
-device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
-   
-model = transformers.AutoModelForCausalLM.from_pretrained(
-    'mosaicml/mpt-7b-instruct',
-    trust_remote_code=True,
-    torch_dtype=bfloat16,
-    max_seq_len=2048
-)
-model.eval()
-model.to(device)
-print(f"Model loaded on {device}")
-
-def chatbot(input):
-    tokenizer = transformers.AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
-    
-    # mpt-7b is trained to add "<|endoftext|>" at the end of generations
-    stop_token_ids = tokenizer.convert_tokens_to_ids(["<|endoftext|>"])
-    
-    # define custom stopping criteria object
-    class StopOnTokens(StoppingCriteria):
-        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-            for stop_id in stop_token_ids:
-                if input_ids[0][-1] == stop_id:
-                    return True
-            return False
-    
-    stopping_criteria = StoppingCriteriaList([StopOnTokens()])
-    for i in range(50):
-        generate_text = transformers.pipeline(
-            model=model, tokenizer=tokenizer,
-            return_full_text=True,  # langchain expects the full text
-            task='text-generation',
-            device=device,
-            # we pass model parameters here too
-            stopping_criteria=stopping_criteria,  # without this model will ramble
-            temperature=0.1,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
-            top_p=0.15,  # select from top tokens whose probability add up to 15%
-            top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-            max_new_tokens=1000,  # max number of tokens to generate in the output
-            repetition_penalty=1.1  # without this output begins repeating
-        )
-        
-        res = generate_text(input)
-        output = res[0]["generated_text"]
-        return output 
-
-inputs = gr.inputs.Textbox(lines=7, label="Chat with AI")
-outputs = gr.outputs.Textbox(label="Reply")
-
-gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="Self_Trained_V2",
-             description="Ask anything you want",
-             ).launch(share=True)  
+gr.Interface.load("models/anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g").launch()