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import gradio as gr |
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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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from transformers import GPT2TokenizerFast,GPT2LMHeadModel |
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tokenizer = GPT2TokenizerFast.from_pretrained("AlexWortega/instruct_rugptlarge") |
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special_tokens_dict = {'additional_special_tokens': ['<code>', '</code>', '<instructionS>', '<instructionE>', '<next>']} |
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tokenizer.add_special_tokens(special_tokens_dict) |
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device = 'cpu' |
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model = GPT2LMHeadModel.from_pretrained("AlexWortega/instruct_rugptlarge") |
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model.resize_token_embeddings(len(tokenizer)) |
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def generate_prompt(instruction, input=None): |
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if input: |
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return f"{input}:" |
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return f"{instruction}" |
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def generate_seqs(q, temp, topp, topk, nb, maxtok): |
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k=1 |
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gen_kwargs = { |
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"min_length": 20, |
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"max_new_tokens": maxtok, |
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"top_k": topk, |
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"top_p": topp, |
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"do_sample": True, |
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"early_stopping": True, |
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"no_repeat_ngram_size": 2, |
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"temperature":temp, |
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"eos_token_id": tokenizer.eos_token_id, |
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"pad_token_id": tokenizer.eos_token_id, |
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"use_cache": True, |
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"repetition_penalty": 1.5, |
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"length_penalty": 0.8, |
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"num_beams": nb, |
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"num_return_sequences": k |
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} |
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if len(q)>0: |
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q = q + '<instructionS>' |
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else: |
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q = 'Как зарабатывать денег на нейросетях ?' + '<instructionS>' |
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t = tokenizer.encode(q, return_tensors='pt').to(device) |
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g = model.generate(t, **gen_kwargs) |
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generated_sequences = tokenizer.batch_decode(g, skip_special_tokens=False) |
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ans = generated_sequences[0].replace('<instructionS>','\n').replace('<instructionE>','').replace('<|endoftext|>','') |
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return ans |
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description_html = ''' |
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<p>Обучена на 2v100, коллективом авторов:</p> |
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<ul> |
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<li><a href="https://t.me/YallenGusev" target="_blank">@YallenGusev</a></li> |
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<li><a href="https://t.me/lovedeathtransformers" target="_blank">@lovedeathtransformers</a></li> |
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<li><a href="https://t.me/alexkuk" target="_blank">@alexkuk</a></li> |
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<li><a href="https://t.me/chckdskeasfsd" target="_blank">@chckdskeasfsd</a></li> |
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<li><a href="https://t.me/dno5iq" target="_blank">@dno5iq</a></li> |
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</ul> |
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''' |
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g = gr.Interface( |
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fn=generate_seqs, |
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inputs=[ |
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gr.components.Textbox( |
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lines=2, label="Впишите сюда задачу, а я попробую решить", placeholder="Как зарабатывать денег на нейросетях?" |
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), |
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gr.components.Slider(minimum=0.1, maximum=2, value=1.0, label="Temperature"), |
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gr.components.Slider(minimum=0, maximum=1, value=0.9, label="Top p"), |
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gr.components.Slider(minimum=0, maximum=100, value=50, label="Top k"), |
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gr.components.Slider(minimum=0, maximum=5, step=1, value=4, label="Beams"), |
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gr.components.Slider( |
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minimum=1, maximum=256, step=1, value=100, label="Max tokens" |
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), |
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], |
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outputs=[ |
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gr.inputs.Textbox( |
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lines=5, |
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label="Output", |
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) |
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], |
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title="ruInstructlarge", |
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description=description_html) |
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g.queue(concurrency_count=5) |
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g.launch() |
