First commit and deploy

app.py

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+import transformers
+import torch
+import gc
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+
+# Load the model
+model = "tiiuae/falcon-7b-instruct"
+instruction = "Draft an apology email to a customer who experienced a delay in their order and provide reassurance that the issue has been resolved"
+
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+
+def predict(instruction: str):
+    """
+    The `predict` function takes an instruction as input and uses a pre-trained language model to
+    generate a predicted sequence of text based on the instruction.
+    
+    :param instruction: The instruction parameter is a string that represents the input for which you
+    want to generate a prediction. It could be a question, a prompt, or any other kind of input that you
+    want the model to generate a response for
+    :type instruction: str
+    :return: The function `predict` returns a string that represents the generated text from the model.
+    """
+    sequences = pipeline(
+        instruction,
+        max_length=500,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    for seq in sequences:
+        result = f"Result: {seq['generated_text']}"
+    gc.collect()
+    torch.cuda.empty_cache()
+    return result
+
+gr.Interface(
+    predict,
+    inputs=[
+        gr.inputs.Textbox(lines=2, default=instruction, label="Instruction"),
+    ],
+    outputs=[gr.outputs.Textbox(label="Output")],
+    title= "XGen"
+).launch()

requirements.txt

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+transformers
+einops
+accelerate
+xformers
+gradio