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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "tiiuae/falcon-7b-instruct",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+    low_cpu_mem_usage=True,
+    offload_folder="/",
+)
+tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
+
+
+def create_embedding(input_text):
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    attention_mask = torch.ones(input_ids.shape)
+
+    output = model.generate(
+        input_ids,
+        attention_mask=attention_mask,
+        max_length=200,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    print(output_text)
+    return output_text
+
+
+instructor_model_embeddings = gr.Interface(
+    fn=create_embedding,
+    inputs=[
+        gr.inputs.Textbox(label="Input Text"),
+    ],
+    outputs=gr.inputs.Textbox(label="Generated Text"),
+    title="Falcon-7B Instruct",
+).launch()

requirements.txt

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+datasets
+transformers
+accelerate
+einops
+safetensors