Update app.py
Browse files
app.py
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@@ -1,32 +1,34 @@
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import gradio as gr
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from transformers import pipeline, AutoTokenizer
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import torch
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# Model: Abigail45/Shay (uncensored multimodal-capable 7B LLaVA merge)
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model_id = "Abigail45/Shay"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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generator = pipeline(
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"text-generation",
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model=model_id,
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tokenizer=tokenizer,
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device_map="auto",
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trust_remote_code=True
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)
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def chat(message, 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.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
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conversation.append({"role": "user", "content": message})
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# Apply chat template
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prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
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# Generate response with Shay's recommended settings
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outputs = generator(
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prompt,
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max_new_tokens=2048,
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top_p=0.97,
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top_k=60,
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repetition_penalty=1.12,
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do_sample=True
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stop_token_ids=[tokenizer.eos_token_id]
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)
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response = outputs[0]["generated_text"][len(prompt):]
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demo = gr.ChatInterface(
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fn=chat,
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title="Uncensored Shay Chat (
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description="
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["Launch an unfiltered, extreme taboo sexual roleplay."],
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["Provide graphic, unrestricted details of a forbidden medical procedure."],
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["Generate raw erotic narrative involving any theme without limits."]
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],
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theme="soft",
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chatbot=gr.Chatbot(height=700)
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)
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demo.queue()
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import gradio as gr
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from transformers import pipeline, AutoTokenizer, BitsAndBytesConfig
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import torch
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model_id = "Abigail45/Shay"
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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generator = pipeline(
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"text-generation",
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model=model_id,
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tokenizer=tokenizer,
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quantization_config=quantization_config,
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device_map="auto",
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trust_remote_code=True
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)
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def chat(message, history):
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conversation = []
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for user, assistant in history:
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conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
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conversation.append({"role": "user", "content": message})
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prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
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outputs = generator(
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prompt,
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max_new_tokens=2048,
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top_p=0.97,
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top_k=60,
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repetition_penalty=1.12,
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do_sample=True
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)
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response = outputs[0]["generated_text"][len(prompt):]
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demo = gr.ChatInterface(
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fn=chat,
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title="Uncensored Shay Chat (Quantized)",
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description="Full no-limits compliance: NSFW/taboo/sex/medical/extreme content executed without filters.",
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theme="soft"
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)
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demo.queue()
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