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Merge branch 'main' of hf.co:spaces/valcore/Branchy-phi-2
Browse files- README.md +2 -2
- app.py +3 -1
- requirements.txt +2 -2
README.md
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@@ -4,7 +4,7 @@ emoji: ⚡
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colorFrom: purple
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colorTo: pink
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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pinned: false
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---
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- **app.py**: The main Streamlit application script.
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- **requirements.txt**: Lists all the Python dependencies required by the project.
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- **src/**: Contains the source code for the project.
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colorFrom: purple
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colorTo: pink
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sdk: gradio
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sdk_version: 4.37.2
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app_file: app.py
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pinned: false
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---
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- **app.py**: The main Streamlit application script.
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- **requirements.txt**: Lists all the Python dependencies required by the project.
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- **src/**: Contains the source code for the project.
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app.py
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@@ -2,6 +2,7 @@ import gradio as gr
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import torch
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import pandas as pd
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import time
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if len(input_ids[0]) > max_length:
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return input_ids[:, -max_length:]
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return input_ids
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def generate_response(message, chat_history, epsilon):
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global data, stop_generation
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data = pd.DataFrame(columns=["Time taken (in ms)", "Early exit depth", "Token"])
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import torch
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import pandas as pd
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import plotly.graph_objects as go
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import spaces
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from plotly.subplots import make_subplots
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import time
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if len(input_ids[0]) > max_length:
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return input_ids[:, -max_length:]
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return input_ids
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@spaces.GPU
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def generate_response(message, chat_history, epsilon):
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global data, stop_generation
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data = pd.DataFrame(columns=["Time taken (in ms)", "Early exit depth", "Token"])
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requirements.txt
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gradio==4.
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torch==2.
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pandas==2.0.3
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transformers==4.41.1
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plotly==5.22.0
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gradio==4.37.2
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torch==2.2.0
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pandas==2.0.3
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transformers==4.41.1
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plotly==5.22.0
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