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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import gradio as gr |
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title = """🙋🏻♂️Welcome to🌟Tonic's 🦙LiteLlama📳On-Device Chat!""" |
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description = """ |
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You can use this Space to test out the current model [ahxt/LiteLlama-460M-1T](https://huggingface.co/ahxt/LiteLlama-460M-1T) You can also use 🦙LiteLlama📳On-Device Chat by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic1/Litellama?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> |
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Join us : 🌟TeamTonic is always making cool demos! Join our active🛠️builder's community on👻Discord: [Discord](https://discord.gg/nXx5wbX9) On🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟[PolyGPT](https://github.com/tonic-ai/polygpt-alpha) |
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""" |
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model_path = 'ahxt/LiteLlama-460M-1T' |
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model = AutoModelForCausalLM.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model.eval() |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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def generate_text(question): |
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prompt = f'Q: {question}\nA:' |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) |
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tokens = model.generate(input_ids, max_length=50, pad_token_id=tokenizer.eos_token_id) |
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response = tokenizer.decode(tokens[0], skip_special_tokens=False) |
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return response.split('\nA: ')[-1] |
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with gr.Blocks() as iface: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Column(): |
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question = gr.Textbox(label="Speak to LiteLlama", lines=2, placeholder="What are the best Japanese gardens in Paris?") |
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submit_button = gr.Button("Submit") |
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with gr.Column(): |
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output = gr.Textbox(label="🦙LiteLlama", lines=6) |
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submit_button.click(fn=generate_text, inputs=question, outputs=output) |
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iface.launch() |
