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Build error
update
Browse files
app.py
CHANGED
@@ -49,9 +49,13 @@ def text_generate(problem, template, prompt):
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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":
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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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@@ -69,7 +73,7 @@ demo = gr.Blocks()
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with demo:
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gr.Markdown("<h1><center>Step By Step With Bloom</center></h1>")
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gr.Markdown(
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"""
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)
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with gr.Row():
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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": False
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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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with demo:
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gr.Markdown("<h1><center>Step By Step With Bloom</center></h1>")
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gr.Markdown(
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"""[BigScienceW Bloom](https://twitter.com/BigscienceW) \n\n Large language models have demonstrated a capability of 'Chain-of-thought reasoning'. A group of amazing researchers( [Jason Wei et al.](https://arxiv.org/abs/2205.11916)) recently found out that by adding **Lets think step by step** it improves the model's zero-shot performance. Some might say — You can get good results out of LLMs if you know how to speak to them. This space is an attempt at inspecting this LLM behavior/capability in the new HuggingFace BigScienceW [Bloom](https://huggingface.co/bigscience/bloom) model. \n\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma) for EuroPython 2022 Demo.\nThis Space might sometime fail due to inference queue being full and logs would end up showing error as *queue full, try again later*, don't despair and try again after some time. I would try and improve the app as well over next couple days."""
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)
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with gr.Row():
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