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| from transformers import ( | |
| AutoTokenizer, | |
| AutoModelForCausalLM, | |
| GPTNeoForCausalLM, | |
| ) | |
| import torch | |
| import psutil | |
| tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") | |
| model = AutoModelForCausalLM.from_pretrained("NovelAI/genji-python-6B").half().eval().cuda() | |
| import gradio as gr | |
| maxLength=200 | |
| temperature=0.4 | |
| top_k = 50 | |
| top_p = 0.9 | |
| repetition_penalty = 1.13 | |
| repetition_penalty_range = 512 | |
| repetition_penalty_slope = 3.33 | |
| def generator(text): | |
| tokens = tokenizer(text, return_tensors="pt").input_ids.cuda()[:, -(2047-maxLength):] | |
| out = model.generate( | |
| tokens.long(), | |
| do_sample=True, | |
| min_length=tokens.shape[1] + maxLength, | |
| max_length=tokens.shape[1] + maxLength, | |
| temperature=temperature, | |
| top_k = top_k, | |
| top_p = top_p, | |
| repetition_penalty = repetition_penalty, | |
| repetition_penalty_range = repetition_penalty_range, | |
| repetition_penalty_slope = repetition_penalty_slope, | |
| use_cache=True, | |
| bad_words_ids=None, | |
| pad_token_id=tokenizer.eos_token_id, | |
| ).long().to("cpu")[0] | |
| return tokenizer.decode(out[tokens.shape[1]:]) | |
| title = "genji-python-6b" | |
| description = "demo for Genji-python-6b. To use it, simply add your text, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska'>Colab</a> | <a href='https://huggingface.co/NovelAI/genji-python-6B'>Huggingface Model</a></p>" | |
| gr.Interface( | |
| generator, | |
| [gr.inputs.Textbox(label="input text")], | |
| gr.outputs.Textbox(label="Output text"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[ | |
| ['def print_customer_name'] | |
| ]).launch(debug=True) |