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Model: Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct
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---
library_name: transformers
model_name: Vikhr-Qwen-2.5-0.5b-Instruct
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
language:
- ru
- en
license: apache-2.0
datasets:
- Vikhrmodels/GrandMaster-PRO-MAX
---
# 💨📟 Vikhr-Qwen-2.5-0.5B-Instruct
#### RU
Инструктивная модель на основе **Qwen-2.5-0.5B-Instruct**, обученная на русскоязычном датасете **GrandMaster-PRO-MAX**. В **4 раза эффективнее** базовой модели, и идеально подходит для запуска на слабых мобильных устройствах.
#### EN
Instructive model based on **Qwen-2.5-0.5B-Instruct**, trained on the Russian-language dataset **GrandMaster-PRO-MAX**. It is **4 times more efficient** than the base model, making it perfect for deployment on low-end mobile devices.
## GGUF
- [Vikhrmodels/Vikhr-Qwen-2.5-0.5B-instruct-GGUF](https://huggingface.co/Vikhrmodels/Vikhr-Qwen-2.5-0.5B-instruct-GGUF)
## Особенности:
- 📚 Основа / Base: [Qwen-2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
- 🇷🇺 Специализация / Specialization: **RU**
- 💾 Датасет / Dataset: [GrandMaster-PRO-MAX](https://huggingface.co/datasets/Vikhrmodels/GrandMaster-PRO-MAX)
## Попробовать / Try now:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bJpLmplDGkMbfOLO2CH6IO-2uUZEaknf?usp=sharing)
## Описание:
#### RU
**Vikhr-Qwen-2.5-0.5B-instruct** — это компактная языковая модель, обученная на датасете **GrandMaster-PRO-MAX**, специально доученная для обработки русского языка. Эффективность модели **в 4 раза** превышает базовую модель, а её размер составляет **1ГБ** , что делает её отличным выбором для запуска на слабых мобильных устройствах.
#### EN
**Vikhr-Qwen-2.5-0.5B-instruct** is a compact language model trained on the **GrandMaster-PRO-MAX** dataset, specifically designed for processing the Russian language. Its efficiency is **4 times** higher than the base model, and its size is **1GB**, making it an excellent choice for deployment on low-end mobile devices.
## Обучение / Train:
#### RU
Для создания **Vikhr-Qwen-2.5-0.5B-Instruct** использовался метод SFT (Supervised Fine-Tuning). Мы обучили модель на синтетическом датасете **Vikhrmodels/GrandMaster-PRO-MAX** (150k инструкций) с поддержкой CoT (Chain-Of-Thought), используя промпты для GPT-4-turbo.
#### EN
To create **Vikhr-Qwen-2.5-0.5B-Instruct**, the SFT (Supervised Fine-Tuning) method was used. We trained the model on a synthetic dataset **Vikhrmodels/GrandMaster-PRO-MAX** (150k instructions) with support for CoT (Chain-Of-Thought), utilizing prompts for GPT-4-turbo.
## Пример кода для запуска / Sample code to run:
**Рекомендуемая температура для генерации: 0.3** / **Recommended generation temperature: 0.3**.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Загрузка модели и токенизатора
model_name = "Vikhrmodels/Vikhr-Qwen-2.5-0.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Подготовка входного текста
input_text = "Напиши очень краткую рецензию о книге Гарри Поттер."
messages = [
{"role": "system", "content": "Вы - Vikhr, помощник с искусственным интеллектом, созданный компанией Vikhr models, чтобы быть полезным, безобидным и честным."},
{"role": "user", "content": input_text},
]
# Токенизация и генерация текста
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
input_ids,
max_length=1512,
temperature=0.3,
num_return_sequences=1,
no_repeat_ngram_size=2,
top_k=50,
top_p=0.95,
)
# Декодирование и вывод результата
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```
#### Ответ модели / Model response:
>Книга "Гарри Поттер" это серия книг, написанных британским писателем Джоан Роулинг. Это одно из самых известных произведений в мире литературы и популярного детского творчества.
>
>**Основные черты серии:**
>
>1. **Сюжет:** События разворачиваются вокруг мальчика по имени Гарри Поттер, который учится в Школе волшебства и философии в Университете Хогвартс. Он сталкивается с различными препятствиями, включая борьбу со злом, поиск друзей и самопознание.
>
>2. **Персонажи:** В книге представлены множество персонажей, каждый из которых имеет свои уникальные черты характера, мотивации и прошлое. Главный герой, Гарри Поттер, является примером доброго и смелого человека, а также необычной личностью.
>
>3. **Темы и идеи:** Рассказы книги затрагивают темы любви, дружбы, справедливости, морали, человеческой неповиновенности и важности обучения через приключения.
>
>4. **История и развитие персонажей:** Через события и взаимодействие с другими персонажами книга исследует глубокие психологические и философские вопросы.
>
>5. **Влияние на культуру:** "Гарри Поттер" оказал огромное влияние на мировую литературу, превратившись в культовый жанр и символ знаний и мудрости.
>
>6. **Доступность:** Книги серии доступны для широкой аудитории и пользуются большим спросом, что делает их популярным выбором среди читателей всех возрастов.
>
>7. **Развитие жанра:** Несмотря на то что "Гарри Поттер" является частью серии, он продолжает быть любимым и актуальным, так как продолжает удивлять читателей новыми историями и персонажами.
>
>Эта серия книг остается одной из самых значительных и влиятельных в истории литературы, оказав влияние на развитие мировой культуры и образование.
### Авторы / Authors
- Sergei Bratchikov, [NLP Wanderer](https://t.me/nlpwanderer), [Vikhr Team](https://t.me/vikhrlabs)
- Nikolay Kompanets, [LakoMoor](https://t.me/lakomoor), [Vikhr Team](https://t.me/vikhrlabs)
- Konstantin Korolev, [Vikhr Team](https://t.me/vikhrlabs)
- Aleksandr Nikolich, [Vikhr Team](https://t.me/vikhrlabs)
```
@article{nikolich2024vikhr,
title={Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian},
author={Aleksandr Nikolich and Konstantin Korolev and Sergey Bratchikov and Nikolay Kompanets and Artem Shelmanov},
journal={arXiv preprint arXiv:2405.13929},
year={2024},
url={https://arxiv.org/pdf/2405.13929}
}
```

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