Maharshi Gor
commited on
Commit
·
e9811e1
1
Parent(s):
da814b0
Formatting change
Browse files
src/components/model_pipeline/model_pipeline.py
CHANGED
@@ -216,7 +216,7 @@ class PipelineInterface:
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if self.simple:
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instruction = "Create a simple single LLM call pipeline that takes in the following input variables and outputs the following output variables:"
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else:
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-
instruction = "Create a pipeline
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gr.Markdown(f"### {instruction}")
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gr.Markdown(f"* Input Variables: {input_variables_str}")
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gr.Markdown(f"* Output Variables: {output_variables_str}")
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if self.simple:
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instruction = "Create a simple single LLM call pipeline that takes in the following input variables and outputs the following output variables:"
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else:
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+
instruction = "Create a pipeline with the following input and output variables."
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gr.Markdown(f"### {instruction}")
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gr.Markdown(f"* Input Variables: {input_variables_str}")
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gr.Markdown(f"* Output Variables: {output_variables_str}")
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src/components/model_pipeline/tossup_pipeline.py
CHANGED
@@ -97,7 +97,7 @@ class TossupPipelineInterface(PipelineInterface):
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gr.update(value=method, interactive=logprobs_supported),
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gr.update(
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value=tiny_styled_warning(
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-
f"<code>{model_name}</code> does not support <code>logprobs</code>. The probability slider will be disabled."
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),
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visible=not logprobs_supported,
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),
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@@ -170,7 +170,7 @@ class TossupPipelineInterface(PipelineInterface):
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dropdowns[output_field] = dropdown
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commons.get_panel_header(
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header="Buzzer settings:",
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-
subheader="Set your thresholds for confidence and output tokens probability.",
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)
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logprobs_supported = is_logprobs_supported(pipeline_state.workflow)
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selected_model_name = get_probs_model_name(pipeline_state.workflow)
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gr.update(value=method, interactive=logprobs_supported),
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gr.update(
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value=tiny_styled_warning(
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+
f"<code>'{model_name}'</code> does not support <code>'logprobs'</code>. The probability slider will be disabled."
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),
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visible=not logprobs_supported,
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),
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dropdowns[output_field] = dropdown
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commons.get_panel_header(
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header="Buzzer settings:",
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+
subheader="Set your thresholds for confidence and output tokens probability (computed using <code>logprobs</code>).",
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)
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logprobs_supported = is_logprobs_supported(pipeline_state.workflow)
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selected_model_name = get_probs_model_name(pipeline_state.workflow)
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