implement falcon chat

.gitignore

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app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer, pipeline
+
+
+model = "tiiuae/falcon-rw-1b"
+tokenizer = AutoTokenizer.from_pretrained(model)
+falcon_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+
+def generate(input):
+    output = falcon_pipeline(
+        input,
+        max_length=40,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+    return output[0]['generated_text']
+
+
+def respond(message, chat_history):
+    bot_message = generate(message, 40)
+    chat_history.append((message, bot_message))
+    
+    return "", chat_history
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(height=240) #just to fit the notebook
+    msg = gr.Textbox(label="Prompt")
+    btn = gr.Button("Submit")
+    clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")
+
+    btn.click(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+    msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot]) #Press enter to submit
+
+demo.queue().launch()

requirements.txt

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+transformers[torch]
+accelerate
+gradio