charllama-2.6B

CharLLama-2.6B Pretrained Language Model

This repository contains a pre-trained language model based on the Llama architecture, utilizing character-level tokenization. The model was developed for experiments in generating Russian-language accentual-syllabic poetry, as described in our paper: "Generation of Russian Poetry of Different Genres and Styles Using Neural Networks with Character-Level Tokenization". Note that for practical applications, fine-tuning on a specific dataset is recommended.

Model Specifications

• Number of parameters: 2,641,199,664

Pretraining Data

The model was pretrained on a mixed dataset of approximately 100GB of Russian and English texts, with a focus on Russian-language content. The dataset includes diverse domains such as fiction and poetry across various genres and styles. All texts were accentuated.

Character-Level Tokenization

The model employs character-by-character tokenization. To use the tokenizer, install it via:

pip install git+https://github.com/Koziev/character-tokenizer

The tokenizer includes special tokens <s> and </s>.

Usage

To use the model with the transformers library, follow this example:

import torch
import transformers
import charactertokenizer

generation_args = {'max_length': 1024,
                   'num_return_sequences': 1,
                   'do_sample': True,
                   'no_repeat_ngram_size': 10,
                   'temperature': 0.8,
                   'top_p': 0.6,
                   'top_k': 0,
                   }

device = "cuda:0"

model_dir = 'ai-forever/charllama-2.6B'

tokenizer = charactertokenizer.CharacterTokenizer.from_pretrained(model_dir)

model = transformers.AutoModelForCausalLM.from_pretrained(model_dir)
model.to(device)

# Poetry completion
prompt = chr(8) + 'У бу́рных чу́вств неи́стовый коне́ц'

input_ids = tokenizer(prompt, return_tensors='pt').input_ids
out_ids = model.generate(input_ids=input_ids.to(device),
                         eos_token_id=tokenizer.eos_token_id,
                         **generation_args).tolist()

prompt_len = len(input_ids[0])
for seq in out_ids:
    seq = seq[1:]

    output = tokenizer.decode(seq)

    if '</s>' in output:
        output = output[:output.find('</s>')].strip()

    text = output
    print('-'*80)
    print(text)

Example output (may vary):

У бу́рных чу́вств неи́стовый коне́ц,
И в э́том не́т ни ка́пельки сомне́нья.
Прихо́дит сро́к, и го́рестный вене́ц
Наде́нет на себя́ душа́ смире́нно.

И не помо́гут в э́том Небеса́,
И не поми́лует Судьба́ - подру́га.
И бу́дет на душе́ твое́й тоска́,
И ста́нет в жи́зни нестерпи́мо ту́го.

Limitation

The model may generate inappropriate content, including hate speech, offensive language, or biased outputs reflecting the training data. Use with caution and consider post-processing or filtering mechanisms.

Citation

If you use this model in your research, please cite it as follows (citation details will be available soon):

citation information will be available soon