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import gradio as gr |
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import requests |
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import os |
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API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" |
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HF_TOKEN = os.environ["HF_TOKEN"] |
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headers = {"Authorization": f"Bearer {HF_TOKEN}"} |
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def text_generate(prompt): |
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print(f"Prompt is :{prompt}") |
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p = prompt + " Solution: " |
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print(f"Final prompt is : {p}") |
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json_ = {"inputs": p, |
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"parameters": |
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{ |
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"top_p": 0.9, |
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"temperature": 1.1, |
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"max_new_tokens": 250, |
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"return_full_text": True |
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}, "options": |
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{ |
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"use_cache": True, |
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"wait_for_model":True |
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},} |
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response = requests.post(API_URL, headers=headers, json=json_) |
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print(f"Response is : {response}") |
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output = response.json() |
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print(f"output is : {output}") |
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output_tmp = output[0]['generated_text'] |
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print(f"output_tmp is: {output_tmp}") |
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solution = output_tmp.split("\nQ:")[0] |
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print(f"Final response after splits is: {solution}") |
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return solution |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("<h1><center>Length generalization (LG) With BLOOM🌸 </center></h1>") |
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gr.Markdown( |
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""" |
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We will examine large language models ability to extrapolate to longer problems! \n |
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Length generalization (LG) is important: Often, long examples are rare and intrinsically more difficult, yet are the ones we care more about. \n |
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Recent paper [Exploring Length Generalization in Large Language Models](https://arxiv.org/pdf/2207.04901) found that using few-shot [scratchpad](https://arxiv.org/abs/2112.00114), a combo behind many strong LLM results (eg. #Minerva ) \n |
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leads to **substantial improvements in length generalization!** \n |
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In-context learning enables variable length pattern matching, producing solutions of correct lengths. \n |
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This space is an attempt at inspecting this LLM behavior/capability in the new HuggingFace BigScienceW [Bloom](https://huggingface.co/bigscience/bloom) model. \n |
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This Space is created by [Muhtasham Oblokulov](https://twitter.com/muhtasham9) for EuroPython 2022 Demo. \n |
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This Space is work in progress, BLOOM doesn't support inference on long sequencess so you may try with shorter sequences. \n |
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""" |
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) |
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with gr.Row(): |
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input_prompt = gr.Textbox(value="Q:The coin is heads up.(1) Then Austin flips. Is the coin still heads up? Solution: Coin is initially heads up. (1) After Austin flips, coin turns to heads. Q: The coin is heads up. (2) Then Austin doesn't flip. (1) Then Kara flips. Is the coin still heads up?", |
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label="Enter your examples zero-shot (few-shot is not supported due to API limit) followed by Query :") |
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generated_txt = gr.Textbox(lines=10, label="Generated Solution:") |
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b1 = gr.Button("Generate Text") |
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b1.click(text_generate,inputs=[input_prompt], outputs=[generated_txt]) |
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with gr.Row(): |
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gr.Markdown("") |
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demo.launch(enable_queue=True, debug=True) |
