Spaces:
Running
on
Zero
Running
on
Zero
improve interface
Browse files
app.py
CHANGED
@@ -97,24 +97,24 @@ with gr.Blocks() as demo:
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value="You are a helpful AI assistant.",
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lines=2,
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placeholder="Set the AI's behavior and context..."
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)
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context = gr.Textbox(
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label="Context",
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lines=2,
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placeholder="Enter additional context information..."
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)
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max_tokens = gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens")
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temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
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top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
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use_pipeline = gr.Checkbox(label="Use Pipeline", value=False)
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use_tool = gr.Checkbox(label="Use Function Calling", value=False)
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with gr.Column(visible=False) as tool_options:
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tool_definition = gr.Code(
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label="Tool Definition (JSON)",
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value=customtool,
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lines=15,
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language="json"
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)
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def user(user_message, history):
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return "", history + [[user_message, None]]
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value="You are a helpful AI assistant.",
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lines=2,
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placeholder="Set the AI's behavior and context..."
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)
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context = gr.Textbox(
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label="Context",
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lines=2,
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placeholder="Enter additional context information..."
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)
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max_tokens = gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens")
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temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
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top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
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use_pipeline = gr.Checkbox(label="Use Pipeline", value=False)
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use_tool = gr.Checkbox(label="Use Function Calling", value=False)
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with gr.Column(visible=False) as tool_options:
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tool_definition = gr.Code(
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label="Tool Definition (JSON)",
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value=customtool,
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lines=15,
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language="json"
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)
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def user(user_message, history):
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return "", history + [[user_message, None]]
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globe.py
CHANGED
@@ -14,14 +14,14 @@ presentation1 = """Try this model on [build.nvidia.com](https://build.nvidia.com
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**Model Dates:** 🤖Nemotron-Mini-4B-Instruct was trained between February 2024 and Aug 2024.
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[NVIDIA Community Model License](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct/blob/main/nvidia-community-model-license-aug2024.pdf)"""
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presentation2 = """
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Nemotron-Mini-4B-Instruct uses a model embedding size of 3072, 32 attention heads, and an MLP intermediate dimension of 9216. It also uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE).
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**Architecture Type:** Transformer Decoder (auto-regressive language model)
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**Model Dates:** 🤖Nemotron-Mini-4B-Instruct was trained between February 2024 and Aug 2024.
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### License
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[NVIDIA Community Model License](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct/blob/main/nvidia-community-model-license-aug2024.pdf)"""
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presentation2 = """
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### Model Architecture
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🤖Nemotron-Mini-4B-Instruct uses a model embedding size of 3072, 32 attention heads, and an MLP intermediate dimension of 9216. It also uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE).
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**Architecture Type:** Transformer Decoder (auto-regressive language model)
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