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tonic
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f4b9dcc
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Parent(s):
1b4f65b
Update app.py
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app.py
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@@ -2,7 +2,7 @@ import spaces
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import gradio as gr
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import os
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title = """# Welcome to 🌟Tonic's✨StarCoder
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@@ -14,28 +14,13 @@ default_system_prompt = """SYSTEM: You are an AI that code. Answer with code."""
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model_path = "bigcode/starcoder2-15b"
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hf_token = os.getenv("HF_TOKEN")
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if not hf_token:
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raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
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device_map="auto",
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# trust_remote_code=True,
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token=hf_token,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# import gradio as gr
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# from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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# checkpoint = "bigcode/starcoder2-15b"
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# quantization_config = BitsAndBytesConfig(load_in_8bit=True)
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# tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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# model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config).to("cuda")
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@spaces.GPU
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def generate_text(prompt, temperature, max_length):
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inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
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@@ -45,14 +30,12 @@ def generate_text(prompt, temperature, max_length):
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def gradio_app():
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with gr.Blocks() as demo:
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gr.Markdown(title)
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with gr.Row():
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generate_btn = gr.Button("Generate")
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with gr.Row():
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temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="Temperature")
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max_length = gr.Slider(minimum=100, maximum=1024, step=10, value=100, label="Generate Length")
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generate_btn.click(
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fn=generate_text,
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outputs=output
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)
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demo.launch()
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import gradio as gr
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import os
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title = """# Welcome to 🌟Tonic's✨StarCoder
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model_path = "bigcode/starcoder2-15b"
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hf_token = os.getenv("HF_TOKEN")
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if not hf_token:
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raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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quantization_config = BitsAndBytesConfig(load_in_8bit=True)
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model = AutoModelForCausalLM.from_pretrained( model_path, quantization_config=quantization_config).to("cuda")
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@spaces.GPU
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def generate_text(prompt, temperature, max_length):
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inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
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def gradio_app():
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with gr.Blocks() as demo:
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gr.Markdown(title)
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prompt = gr.Textbox(label="Enter your code prompt", placeholder="def print_hello_world():")
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with gr.Row():
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temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="Temperature")
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max_length = gr.Slider(minimum=100, maximum=1024, step=10, value=100, label="Generate Length")
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generate_btn = gr.Button("Try✨StarCoder")
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output = gr.Code(label="Generated Code", lines=40)
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generate_btn.click(
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fn=generate_text,
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outputs=output
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)
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demo.launch()
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if __name__ == "__main__":
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gradio_app()
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