lxe commited on
Commit
ff1b83e
1 Parent(s): 40fab0e

Modified defaults

Browse files
Files changed (2) hide show
  1. .gitignore +1 -0
  2. app.py +6 -6
.gitignore ADDED
@@ -0,0 +1 @@
 
 
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+ __pycache__/
app.py CHANGED
@@ -173,8 +173,8 @@ def generate_text(
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  with gr.Blocks() as demo:
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  gr.Markdown("""
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- ## Cerebras GPT-2.7B Alpcaca-Shortprompt LoRA Demo
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- This is a very fast and relatively coherent chatbot. It uses the [Cerebras-GPT-2.7B](https://huggingface.co/cerebras/Cerebras-GPT-2.7B), with a LoRA finetuned on the [Alpcaca Dataset]) dataset using a shorter prompt. The chatbok keeps a very short conversation context as well.
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  """)
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  with gr.Row():
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  with gr.Column():
@@ -188,11 +188,11 @@ with gr.Blocks() as demo:
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  do_sample = gr.Checkbox(True, label="do_sample")
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  with gr.Row():
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  with gr.Column():
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- temperature = gr.Slider(0, 2, 0.5, step=0.01, label="temperature")
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- top_p = gr.Slider(0, 1, 0.75, step=0.01, label="top_p")
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- top_k = gr.Slider(0, 100, 80, step=1, label="top_k")
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  with gr.Column():
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- repetition_penalty = gr.Slider(0, 10, 1.5, step=0.01, label="repetition_penalty")
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  typical_p = gr.Slider(0, 1, 1, step=0.01, label="typical_p")
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  num_beams = gr.Slider(0, 10, 1, step=1, label="num_beams")
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  with gr.Blocks() as demo:
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  gr.Markdown("""
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+ ## 🐺🦙 Cerebras GPT-2.7B Alpcaca-Shortprompt LoRA Chatbot
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+ This is a very fast and relatively coherent (but hallucinating) chatbot. It uses the [Cerebras-GPT-2.7B](https://huggingface.co/cerebras/Cerebras-GPT-2.7B), with a LoRA finetuned on the [Alpcaca Dataset](https://github.com/tloen/alpaca-lora/blob/main/alpaca_data_cleaned.json) dataset using a shorter prompt. The chatbot keeps a very short conversation context of 4 entries. It's the fastest chatbot in the west!
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  """)
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  with gr.Row():
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  with gr.Column():
 
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  do_sample = gr.Checkbox(True, label="do_sample")
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  with gr.Row():
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  with gr.Column():
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+ temperature = gr.Slider(0, 2, 0.1, step=0.01, label="temperature")
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+ top_p = gr.Slider(0, 1, 0.8, step=0.01, label="top_p")
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+ top_k = gr.Slider(0, 100, 35, step=1, label="top_k")
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  with gr.Column():
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+ repetition_penalty = gr.Slider(0, 10, 1.1, step=0.01, label="repetition_penalty")
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  typical_p = gr.Slider(0, 1, 1, step=0.01, label="typical_p")
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  num_beams = gr.Slider(0, 10, 1, step=1, label="num_beams")
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