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import gradio as gr |
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from gpt4all import GPT4All |
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from huggingface_hub import hf_hub_download |
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from diarizationlm import utils |
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title = "💬DiarizationLM GGUF inference on CPU💬" |
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description = """ |
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A demo of the DiarizationLM model finetuned from Llama 3. In this demo, we run a 4-bit quantized GGUF model on CPU. |
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To learn more about DiarizationLM, check our paper: https://arxiv.org/abs/2401.03506 |
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""" |
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model_path = "models" |
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model_name = "q4_k_m.gguf" |
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prompt_suffix = " --> " |
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completion_suffix = " [eod]" |
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hf_hub_download(repo_id="google/DiarizationLM-8b-Fisher-v2", filename=model_name, local_dir=model_path) |
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print("Start the model init process") |
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model = GPT4All(model_name=model_name, |
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model_path=model_path, |
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allow_download = False, |
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device="cpu") |
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print("Finish the model init process") |
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def generater(prompt): |
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llm_prompt = prompt + prompt_suffix |
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max_new_tokens = round(len(prompt) / 3.0 * 1.2) |
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outputs = [] |
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for token in model.generate(prompt=llm_prompt, |
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temp=0.1, |
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top_k=50, |
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top_p=0.5, |
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max_tokens=max_new_tokens, |
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streaming=True): |
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outputs.append(token) |
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completion = "".join(outputs) |
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yield completion |
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if completion.endswith(" [eod]"): |
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break |
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transferred_completion = utils.transfer_llm_completion(completion, prompt) |
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yield transferred_completion |
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demo = gr.Interface( |
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fn = generater, |
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title=title, |
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description = description, |
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inputs=["text"], |
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outputs=["text"], |
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examples=[ |
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["<speaker:1> Hello, my name is Tom. May I speak to Laura <speaker:2> please? Hello, this is Laura. <speaker:1> Hi Laura, how are you? This is <speaker:2> Tom. Hi Tom, I haven't seen you for a <speaker:1> while."], |
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["<speaker:1> This demo looks really <speaker:2> good! Thanks, I am glad to hear that."], |
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], |
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cache_examples=False, |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=3).launch() |