WortegaLM 109m
Model Summary
Это GPTneo like модель обученная с нуля на сете в 95gb кода, хабра, пикабу, новостей(порядка 12B токенов) Она умеет решать примитивные задачи, не пригодна для ZS FS, но идеальна как модель для студенческих проектов
Quick Start
from transformers import AutoTokenizer, AutoModelForCausalLM,
tokenizer = AutoTokenizer.from_pretrained('AlexWortega/wortegaLM',padding_side='left')
device = 'cuda'
model = AutoModelForCausalLM.from_pretrained('AlexWortega/wortegaLM')
model.resize_token_embeddings(len(tokenizer))
model.to(device)
def generate_seqs(q,model, k=2):
gen_kwargs = {
"min_length": 20,
"max_new_tokens": 100,
"top_k": 50,
"top_p": 0.7,
"do_sample": True,
"early_stopping": True,
"no_repeat_ngram_size": 2,
"eos_token_id": tokenizer.eos_token_id,
"pad_token_id": tokenizer.eos_token_id,
"use_cache": True,
"repetition_penalty": 1.5,
"length_penalty": 1.2,
"num_beams": 4,
"num_return_sequences": k
}
t = tokenizer.encode(q, add_special_tokens=False, return_tensors='pt').to(device)
g = model.generate(t, **gen_kwargs)
generated_sequences = tokenizer.batch_decode(g, skip_special_tokens=False)
return generated_sequences
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