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import gradio as gr |
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title = "Qilin-Lit-6B" |
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description = "Qilin-Lit-6B is a finetuned version of GPT-J-6B. It has been trained on webnovels. It can work as a general purpose fantasy novel storyteller." |
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examples = [ |
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['I had eyes but couldn\'t see Mount Tai!'], |
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] |
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demo = gr.Interface.load("models/rexwang8/qilin-lit-6b", description=description, examples=examples) |
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demo.launch() |
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''' |
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import os |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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def GenerateResp(prompt): |
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model = AutoModelForCausalLM.from_pretrained('rexwang8/qilin-lit-6b') |
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tokenizer = AutoTokenizer.from_pretrained('rexwang8/qilin-lit-6b') |
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input_ids = tokenizer.encode(prompt, return_tensors='pt') |
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output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id) |
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generated_text = tokenizer.decode(output[0]) |
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return generated_text |
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''' |
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''' |
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inputbox = gr.Textbox(label="Input",lines=3,placeholder='Type anything. The longer the better since it gives Qilin more context. Qilin is trained on english translated eastern (mostly chinese) webnovels.') |
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outputbox = gr.Textbox(label="Qilin-Lit-6B",lines=8) |
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iface = gr.Interface(fn=GenerateResp, inputs="text", outputs="text") |
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iface.launch() |
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''' |