text stringlengths 1 93.6k |
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data = load_dataset('nickrosh/Evol-Instruct-Code-80k-v1', split='train')
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extract = data[0:1500]
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text = '\n'.join(extract['output'])
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self.logger.info("Tokenising Code dataset")
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tokenized = self.tokenizer(text, return_tensors='pt')
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return self.append_dataset(tokenized, self.num_samples, self.seqlen)
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def get_spanish(self):
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data = load_dataset('bertin-project/alpaca-spanish', split='train')
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subset_data = data.select(range(5000))
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text = '\n'.join(item['output'] for item in subset_data)
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self.logger.info("Tokenising Spanish dataset")
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tokenized = self.tokenizer(text, return_tensors='pt')
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return self.append_dataset(tokenized, self.num_samples, self.seqlen)
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def get_german(self):
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data = load_dataset('deepset/germanquad', split='train')
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def transform_context(sample):
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split_context = sample['context'].split('===')
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if len(split_context) >= 3:
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trans_context = split_context[2]
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else:
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trans_context = sample['context']
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return {'context': trans_context.strip()}
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subset_data = data.select(range(2000))
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transformed_subset = subset_data.map(transform_context)
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text = '\n'.join([item['context'] for item in transformed_subset])
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self.logger.info("Tokenising German dataset")
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tokenized = self.tokenizer(text, return_tensors='pt')
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return self.append_dataset(tokenized, self.num_samples, self.seqlen)
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def get_french(self):
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data = load_dataset('gustavecortal/diverse_french_news', split='train')
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extract = data[0:700]
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text = '\n'.join(extract['text'])
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self.logger.info("Tokenising French dataset")
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tokenized = self.tokenizer(text, return_tensors='pt')
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return self.append_dataset(tokenized, self.num_samples, self.seqlen)
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def get_wikitext2(self):
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wikidata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
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wikilist = [' \n' if s == '' else s for s in wikidata['text'] ]
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text = ''.join(wikilist)
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self.logger.info("Tokenising wikitext2")
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tokenized = self.tokenizer(text, return_tensors='pt')
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return self.append_dataset(tokenized, self.num_samples, self.seqlen)
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def get_c4(self):
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import numpy as np
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import torch
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traindata = load_dataset(
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'allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', use_auth_token=False
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)
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trainloader = []
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for _ in range(self.num_samples):
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while True:
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i = random.randint(0, len(traindata) - 1)
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trainenc = self.tokenizer(traindata[i]['text'], return_tensors='pt')
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if trainenc.input_ids.shape[1] >= self.seqlen:
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break
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i = random.randint(0, trainenc.input_ids.shape[1] - self.seqlen - 1)
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j = i + self.seqlen
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inp = trainenc.input_ids[:, i:j]
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attention_mask = torch.ones_like(inp)
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trainloader.append({'input_ids':inp,'attention_mask': attention_mask})
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return trainloader
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def quantize(self, output_dir, traindataset, bits, group_size, desc_act, damp):
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# Hide the super annoying bitsandbytes loading message. We don't even use BnB but I don't know if I can stop it loading entirely.
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os.environ['BITSANDBYTES_NOWELCOME'] = '1'
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# We only import Torch and AutoGPTQ when needed, so that earlier set env vars will affect them.
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import torch
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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quantize_config = BaseQuantizeConfig(
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bits=bits,
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group_size=group_size,
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desc_act=desc_act,
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damp_percent=damp
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)
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if self.dtype == 'float16':
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torch_dtype = torch.float16
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elif self.dtype == 'float32':
|
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