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Update app.py
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app.py
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@@ -2,46 +2,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
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import gradio as gr
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import torch
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description = "A State-of-the-Art Agent Chatbot"
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examples = [["How are you?"]]
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tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K")
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model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K")
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def predict(input, history=[]):
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# tokenize the new input sentence
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new_user_input_ids = tokenizer.encode(
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input + tokenizer.eos_token, return_tensors="pt"
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)
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# append the new user input tokens to the chat history
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bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
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# generate a response
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history = model.generate(
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bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
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).tolist()
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# convert the tokens to text, and then split the responses into lines
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response = tokenizer.decode(history[0]).split("<|endoftext|>")
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# print('decoded_response-->>'+str(response))
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response = [
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(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
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] # convert to tuples of list
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# print('response-->>'+str(response))
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return response, history
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fn=predict,
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title=title,
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description=description,
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examples=examples,
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inputs=["text", "state"],
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outputs=["chatbot", "state"],
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theme="finlaymacklon/boxy_violet",
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).launch()
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import gradio as gr
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import torch
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import gradio as gr
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demo = gr.load("EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code", src="models")
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demo.launch()
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