Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -6,16 +6,15 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStream
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import gradio as gr
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from threading import Thread
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MODEL_LIST = ["meta-llama/Meta-Llama-3.1-8B-Instruct"
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HF_TOKEN = os.environ.get("HF_TOKEN", None)
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MODEL = os.environ.get("MODEL_ID")
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TITLE = "<h1><center>Meta-Llama3.1-
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PLACEHOLDER = """
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<center>
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<p
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<p>✨Select Meta-Llama3.1-8B/70B in Advanced Options</p>
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</center>
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"""
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@@ -40,20 +39,14 @@ quantization_config = BitsAndBytesConfig(
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type= "nf4")
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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torch_dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config)
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model_70b = AutoModelForCausalLM.from_pretrained(
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MODEL_LIST[1],
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torch_dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config)
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@spaces.GPU(
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def stream_chat(
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message: str,
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history: list,
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@@ -63,7 +56,6 @@ def stream_chat(
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top_p: float = 1.0,
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top_k: int = 20,
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penalty: float = 1.2,
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choice: str = "Meta-Llama-3.1-8B"
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):
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print(f'message: {message}')
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print(f'history: {history}')
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@@ -79,11 +71,6 @@ def stream_chat(
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conversation.append({"role": "user", "content": message})
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if choice == "Meta-Llama-3.1-8B":
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model = model_8b
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else:
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model = model_70b
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input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
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@@ -118,7 +105,7 @@ with gr.Blocks(css=CSS, theme="soft") as demo:
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fn=stream_chat,
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chatbot=chatbot,
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fill_height=True,
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additional_inputs_accordion=gr.Accordion(label="⚙️
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additional_inputs=[
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gr.Textbox(
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value="You are a helpful assistant",
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@@ -165,12 +152,6 @@ with gr.Blocks(css=CSS, theme="soft") as demo:
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label="Repetition penalty",
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render=False,
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),
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gr.Radio(
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["Meta-Llama-3.1-8B", "Meta-Llama-3.1-70B"],
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value="Meta-Llama-3.1-8B",
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label="Load Model",
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render=False,
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),
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],
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examples=[
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["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
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import gradio as gr
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from threading import Thread
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MODEL_LIST = ["meta-llama/Meta-Llama-3.1-8B-Instruct"]
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HF_TOKEN = os.environ.get("HF_TOKEN", None)
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MODEL = os.environ.get("MODEL_ID")
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TITLE = "<h1><center>Meta-Llama3.1-8B</center></h1>"
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PLACEHOLDER = """
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<center>
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<p>Hi! How can I help you today?</p>
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</center>
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"""
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type= "nf4")
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config)
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@spaces.GPU()
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def stream_chat(
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message: str,
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history: list,
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top_p: float = 1.0,
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top_k: int = 20,
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penalty: float = 1.2,
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):
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print(f'message: {message}')
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print(f'history: {history}')
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conversation.append({"role": "user", "content": message})
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input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
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fn=stream_chat,
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chatbot=chatbot,
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fill_height=True,
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additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
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additional_inputs=[
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gr.Textbox(
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value="You are a helpful assistant",
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label="Repetition penalty",
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render=False,
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),
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],
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examples=[
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["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
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