IlyaGusev/rulm inference: parameters: min_length: 20 max_new_tokens: 250 top_k: 50 top_p: 0.9 early_stopping: true no_repeat_ngram_size: 2 use_cache: true repetition_penalty: 1.5 length_penalty: 0.8 num_beams: 2 language:
ru library_name: transformers pipeline_tag: text-generation tags:
finance
code

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

python

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

RichardErkhov/AlexWortega_-_wortegaLM-1b-awq

Original model description:

WortegaLM 109m

Model Summary

Quick Start

RichardErkhov/
AlexWortega_-_wortegaLM-1b-awq