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import gradio as gr |
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import torch |
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from transformers import LlamaTokenizer, AutoModelForCausalLM |
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MODEL_ID = "vietrix/viena-60m" |
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tokenizer = LlamaTokenizer.from_pretrained(MODEL_ID, legacy=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_ID, |
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torch_dtype=torch.float32, |
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device_map="cpu", |
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) |
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def respond( |
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message, |
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history: list[dict[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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hf_token: gr.OAuthToken, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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messages.extend(history) |
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messages.append({"role": "user", "content": message}) |
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if getattr(tokenizer, "chat_template", None): |
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prompt = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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) |
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else: |
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parts = [] |
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for m in messages: |
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parts.append(f"{m['role'].upper()}: {m['content']}") |
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parts.append("ASSISTANT:") |
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prompt = "\n".join(parts) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=int(max_tokens), |
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do_sample=True, |
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temperature=float(temperature), |
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top_p=float(top_p), |
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repetition_penalty=1.15, |
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no_repeat_ngram_size=4, |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id, |
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) |
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gen_ids = outputs[0, inputs.input_ids.shape[1]:] |
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text = tokenizer.decode(gen_ids, skip_special_tokens=True) |
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resp = "" |
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for ch in text: |
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resp += ch |
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yield resp |
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chatbot = gr.ChatInterface( |
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respond, |
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type="messages", |
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additional_inputs=[ |
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=256, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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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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], |
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) |
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with gr.Blocks() as demo: |
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with gr.Sidebar(): |
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gr.LoginButton() |
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chatbot.render() |
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if __name__ == "__main__": |
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demo.launch() |
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