Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from hf_olmo import * # registers the Auto* classes
+
+
+title = """# 👋🏻Welcome to 🌟Tonic's 👴🏻🏑Olmo
+"[👴🏻🏑allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B) is an on-device LLM from Allen-ai that can fit on your laptop ! You can use this demo to try out their model. You can also use [👴🏻🏑allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B) [on your laptop & by cloning this space](https://huggingface.co/spaces/Tonic/Olmo/tree/main?clone=true). 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/Olmo?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
+Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻  [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [DataTonic](https://github.com/Tonic-AI/DataTonic) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
+"""
+
+model_name = "allenai/OLMo-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
+
+
+def generate_text(prompt, max_new_tokens, top_k, top_p, do_sample):
+    inputs = tokenizer(prompt, return_tensors='pt', return_token_type_ids=False)
+    response = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=do_sample, top_k=top_k, top_p=top_p)
+    return tokenizer.batch_decode(response, skip_special_tokens=True)[0]
+
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown(title)
+        output = gr.Textbox(label="Generated Text", lines=10)
+        submit_button = gr.Button("Generate")
+        with gr.Accordion("Optional Parameters"):
+            max_new_tokens = gr.Slider(minimum=1, maximum=650, step=1, value=300, label="new tokens in reply")
+            top_k = gr.Slider(minimum=1, maximum=100, step=1, value=50, label="Top K")
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.95, label="Top P")
+            do_sample = gr.Checkbox(value=True, label="Untick for faster inference")
+        prompt = gr.Textbox(label="Enter your prompt")
+        prompt.change(fn=generate_text, inputs=[prompt, max_new_tokens, top_k, top_p, do_sample], outputs=output, submit=submit_button)
+
+    demo.launch()
+
+if __name__ == "__main__":
+    main()