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Update app.py
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app.py
CHANGED
@@ -2,10 +2,14 @@ import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel, PeftConfig
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import torch
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# Your model details
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PEFT_MODEL_ID = "Reubencf/gemma3-goan-finetuned"
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BASE_MODEL_ID = "google/gemma-
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# UI Configuration
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TITLE = "🌴 Gemma Goan Q&A Bot"
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@@ -14,7 +18,6 @@ This is a Gemma-2-2B model fine-tuned on Goan Q&A dataset using LoRA.
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Ask questions about Goa, Konkani culture, or general topics!
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**Model**: [Reubencf/gemma3-goan-finetuned](https://huggingface.co/Reubencf/gemma3-goan-finetuned)
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**Base Model**: google/gemma-2-2b-it
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"""
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print("Loading model... This might take a few minutes on first run.")
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@@ -23,17 +26,21 @@ try:
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# Load LoRA config to check base model
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peft_config = PeftConfig.from_pretrained(PEFT_MODEL_ID)
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# Load base model
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print(f"Loading base model: {BASE_MODEL_ID}")
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base_model = AutoModelForCausalLM.from_pretrained(
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BASE_MODEL_ID,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto",
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low_cpu_mem_usage=True,
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)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "right"
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@@ -58,11 +65,15 @@ except Exception as e:
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from peft import AutoPeftModelForCausalLM
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model = AutoPeftModelForCausalLM.from_pretrained(
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PEFT_MODEL_ID,
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device_map="auto",
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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low_cpu_mem_usage=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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@@ -78,14 +89,12 @@ def generate_response(
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# Format the prompt using Gemma chat template
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if history:
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# Build conversation history
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conversation = ""
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for user, assistant in history:
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conversation += f"<start_of_turn>user\n{user}<end_of_turn>\n"
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conversation += f"<start_of_turn>model\n{assistant}<end_of_turn>\n"
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conversation += f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
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else:
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# Single turn conversation
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conversation = f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
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# Tokenize
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@@ -128,113 +137,26 @@ def generate_response(
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# Example questions
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examples = [
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["What is
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["
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["Explain the history of Old Goa"],
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["What are some popular festivals in Goa?"],
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]
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#
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""
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gr.Markdown(f"# {TITLE}")
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gr.Markdown(DESCRIPTION)
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chatbot = gr.Chatbot(
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height=450,
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show_label=False,
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avatar_images=(None, "🤖"),
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)
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msg = gr.Textbox(
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label="Ask a question",
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placeholder="Type your question about Goa, Konkani culture, or any topic...",
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lines=2,
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)
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with gr.Accordion("⚙️ Generation Settings", open=False):
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temperature = gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.7,
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step=0.1,
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label="Temperature (Creativity)",
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info="Higher = more creative, Lower = more focused"
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)
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max_tokens = gr.Slider(
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minimum=50,
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maximum=512,
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value=256,
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step=10,
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label="Max New Tokens",
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info="Maximum length of the response"
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)
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top_p = gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (Nucleus Sampling)",
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)
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rep_penalty = gr.Slider(
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minimum=1.0,
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maximum=2.0,
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value=1.1,
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step=0.1,
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label="Repetition Penalty",
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)
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with gr.Row():
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clear = gr.Button("🗑️ Clear")
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submit = gr.Button("📤 Send", variant="primary")
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gr.Examples(
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examples=examples,
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inputs=msg,
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label="Example Questions:",
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)
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# Set up event handlers
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def user(user_message, history):
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return "", history + [[user_message, None]]
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def bot(history, temp, max_tok, top_p_val, rep_pen):
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user_message = history[-1][0]
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bot_response = generate_response(
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user_message,
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history[:-1],
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temperature=temp,
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max_new_tokens=max_tok,
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top_p=top_p_val,
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repetition_penalty=rep_pen,
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)
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history[-1][1] = bot_response
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return history
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, [chatbot, temperature, max_tokens, top_p, rep_penalty], chatbot
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)
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submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, [chatbot, temperature, max_tokens, top_p, rep_penalty], chatbot
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)
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clear.click(lambda: None, None, chatbot, queue=False)
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gr.Markdown("""
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---
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### 📝 Note
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This model is fine-tuned specifically on Goan Q&A data. Responses are generated based on patterns learned from the training dataset.
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For best results, ask questions about Goa, its culture, history, cuisine, and related topics.
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""")
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if __name__ == "__main__":
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demo.launch()
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel, PeftConfig
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import torch
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import os
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# Get token from Space secrets
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HF_TOKEN = os.environ.get("HF_TOKEN")
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# Your model details
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PEFT_MODEL_ID = "Reubencf/gemma3-goan-finetuned"
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BASE_MODEL_ID = "google/gemma-3-4b-it" # Correct base model
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# UI Configuration
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TITLE = "🌴 Gemma Goan Q&A Bot"
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Ask questions about Goa, Konkani culture, or general topics!
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**Model**: [Reubencf/gemma3-goan-finetuned](https://huggingface.co/Reubencf/gemma3-goan-finetuned)
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"""
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print("Loading model... This might take a few minutes on first run.")
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# Load LoRA config to check base model
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peft_config = PeftConfig.from_pretrained(PEFT_MODEL_ID)
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# Load base model WITH TOKEN
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print(f"Loading base model: {BASE_MODEL_ID}")
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base_model = AutoModelForCausalLM.from_pretrained(
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BASE_MODEL_ID,
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token=HF_TOKEN, # Add token here
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto",
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low_cpu_mem_usage=True,
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)
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# Load tokenizer WITH TOKEN
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tokenizer = AutoTokenizer.from_pretrained(
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BASE_MODEL_ID,
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token=HF_TOKEN # Add token here
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "right"
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from peft import AutoPeftModelForCausalLM
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model = AutoPeftModelForCausalLM.from_pretrained(
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PEFT_MODEL_ID,
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token=HF_TOKEN, # Add token here
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device_map="auto",
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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low_cpu_mem_usage=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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PEFT_MODEL_ID,
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token=HF_TOKEN # Add token here
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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# Format the prompt using Gemma chat template
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if history:
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conversation = ""
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for user, assistant in history:
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conversation += f"<start_of_turn>user\n{user}<end_of_turn>\n"
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conversation += f"<start_of_turn>model\n{assistant}<end_of_turn>\n"
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conversation += f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
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else:
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conversation = f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
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# Tokenize
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# Example questions
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examples = [
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["What is the capital of Goa?"],
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["Tell me about Konkani language"],
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["What are famous beaches in Goa?"],
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["What is Goan fish curry?"],
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["Explain the history of Old Goa"],
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]
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# Create Gradio interface
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demo = gr.ChatInterface(
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fn=generate_response,
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title=TITLE,
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description=DESCRIPTION,
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examples=examples,
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additional_inputs=[
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gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(minimum=1, maximum=512, value=256, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
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gr.Slider(minimum=1.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty"),
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],
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)
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if __name__ == "__main__":
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demo.launch()
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