🚨 T-lite is designed for further fine-tuning and is not intended as a ready-to-use conversational assistant. Users are advised to exercise caution and are responsible for any additional training and oversight required to ensure the model's responses meet acceptable ethical and safety standards. The responsibility for incorporating this model into industrial or commercial solutions lies entirely with those who choose to deploy it.

Description

T-lite is a continual pretraining model designed specifically for the Russian language, enabling the creation of large language model applications in Russian. This model aims to improve the quality of Russian text generation and provide domain-specific and cultural knowledge relevant to the Russian context.

Model Training Details

🏛️ Architecture and Configuration

T-lite is a decoder language model with:

pre-normalization via RMSNorm
SwiGLU activation function
rotary positional embeddings (RoPE)
grouped query attention (GQA)

T-lite was trained in bf16.

⚙️ Hyperparameters

We employed the Decoupled AdamW optimizer with β1 = 0.9, β2 = 0.95, and eps = 1.0e-8. The learning rate was set to 1.0e-5 with a constant schedule and a warmup period of 10 steps during stage 1, and a cosine schedule during stage 2. Weight decay was applied at a rate of 1.0e-6, and gradient clipping was performed with a maximum norm of 1.0. The maximum sequence length was set to 8192. Each batch contained approximately 6 million tokens.

🏋🏽 Hardware Configuration & Performance

Training was conducted on 96 A100 GPUs with 80GB memory each, using Fully Sharded Data Parallel (FSDP) with full shard/hybrid shard strategies. The setup achieved a throughput of 3000 tokens/sec/GPU, with 100B tokens being processed in approximately 4 days. We achieved a 0.59 Model FLOPs Utilization (MFU).

📚 Data

Stage 1

Massive continual pre-training

300B tokens * 0.3 epoch
Proportion of data in Russian is 85%, as a trade-off between language adoptation and English language performance
Styles and topics in Common Crawl (CC) data were downsampled
Domains in book datasets were balanced
Proportion of code data was increased

Stage 2

Focuses on refining the quality of the dataset

20B tokens * 3 epochs
Includes instructional sets of smaller volume
Advertisements and news were aggressively downsampled
Instructions and articles were upsampled
Educational content was balanced

📊 Benchmarks

🇷🇺 Russian

MERA benchmark results

Task name	Metric	N shot	Llama-3-8b	T-lite-0.1
Total score			0.445	0.492
BPS	Accuracy	2-shot	0.459	0.358
CheGeKa	F1 / EM	4-shot	0.04/0	0.118/0.06
LCS	Accuracy	2-shot	0.146	0.14
MathLogicQA	Accuracy	5-shot	0.365	0.37
MultiQ	F1-score / EM	0-shot	0.106/0.027	0.383/0.29
PARus	Accuracy	0-shot	0.72	0.858
RCB	Avg F1 / Accuracy	0-shot	0.42/0.434	0.511/0.416
ruHumanEval	pass@k	0-shot	0.017/0.085/0.171	0.023/0.113/0.226
ruMMLU	Accuracy	5-shot	0.693	0.759
ruModAr	EM	0-shot	0.708	0.667
ruMultiAr	EM	5-shot	0.259	0.269
ruOpenBookQA	Avg F1 / Accuracy	5-shot	0.745/0.744	0.783/0.782
ruTiE	Accuracy	0-shot	0.553	0.681
ruWorldTree	Avg F1 / Accuracy	5-shot	0.838/0.839	0.88/0.88
RWSD	Accuracy	0-shot	0.504	0.585
SimpleAr	EM	5-shot	0.954	0.955
USE	Grade Norm	0-shot	0.023	0.05

The evluation was performed using https://github.com/ai-forever/MERA/tree/main

🇬🇧 English

It's consistent that after the model was adapted for the Russian language, performance on English benchmarks declined.

Benchmark	N shot	Llama-3-8b	T-lite-0.1
ARC-challenge	0-shot	0.518	0.489
ARC-easy	0-shot	0.789	0.787
MMLU	0-shot	0.62	0.6
Natural Questions	0-shot	0.162	0.222
TriviaQA	0-shot	0.63	0.539

The evluation was performed using https://github.com/EleutherAI/lm-evaluation-harness.

👨‍💻 Examples of usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
torch.manual_seed(42)

tokenizer = AutoTokenizer.from_pretrained("t-bank-ai/T-lite-0.1")
model = AutoModelForCausalLM.from_pretrained("t-bank-ai/T-lite-0.1", device_map="auto")

input_text = "Машинное обучение нужно для"
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Output:

Машинное обучение нужно для того, чтобы автоматизировать процесс принятия решений. Вместо того, чтобы человеку нужно было вручную просматривать и анализировать данные, алгоритмы машинного обучения могут автоматически выявлять закономерности и делать прогнозы на основе этих данных. Это может быть особенно полезно в таких областях, как финансы, где объем данных огромен, а решения должны приниматься быстро.

Вот несколько примеров того, как машинное обучение используется в финансах:

1. Обнаружение мошенничества: алгоритмы машинного обучения могут анализировать закономерности в транзакциях и выявлять подозрительные действия, которые могут указывать на мошенничество.
2. Управление рисками: Машинное обучение может помочь финансовым учреждениям выявлять и оценивать риски, связанные с различными инвестициями или кредитами.
3. Обработка данных на естественном языке: Машинное обучение может использоваться для анализа финансовых новостей и других текстовых данных, чтобы выявить тенденции