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Model: Vikhrmodels/Vistral-24B-Instruct Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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- ru
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base_model:
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- mistralai/Mistral-Small-3.2-24B-Instruct-2506
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library_name: transformers
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datasets:
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- Vikhrmodels/GrandMaster2
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---
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## Vistral-24B-Instruct
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### Описание
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**Vistral** - это наша новая флагманская унимодальная LLM (Large Language Model) представляющая из себя улучшенную версию [mistralai/Mistral-Small-3.2-24B-Instruct-2506](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506) командой **VikhrModels**, адаптированную преимущественно для русского и английского языков. Удалён визуальный энкодер, убрана мультимодальность. Сохранена стандартная архитектура "MistralForCausalLM" без изменений в базовой структуре модели.
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Весь использованный код для обучения доступен в нашем репозитории [effective_llm_alignment](https://github.com/VikhrModels/effective_llm_alignment/) на GitHub, а основные датасеты доступны в нашем [профиле на HF](https://huggingface.co/Vikhrmodels).
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Модель доступна на нашем сайте [Chat Vikhr](https://chat.vikhr.org)
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## Quantized variants:
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- GGUF [Vikhrmodels/Vistral-24B-Instruct-GGUF](https://huggingface.co/Vikhrmodels/Vistral-24B-Instruct-GGUF)
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- MLX
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- 4 bit [Vikhrmodels/Vistral-24B-Instruct-MLX_4bit](https://huggingface.co/Vikhrmodels/Vistral-24B-Instruct-MLX_4bit)
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- 8 bit [Vikhrmodels/Vistral-24B-Instruct-MLX_8bit](https://huggingface.co/Vikhrmodels/Vistral-24B-Instruct-MLX_8bit)
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### Метрики и оценка качества
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Модель оценивалась на нашем русскоязычном open-source SbS бенчмарке [ru-arena-general](https://github.com/VikhrModels/ru_llm_arena)
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#### Результаты на Ru-Arena-General
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| Model Name | Winrate | 95% CI | Average # Tokens |
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|--------------------------------------------------|--------|--------------------|------------------|
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| **Vistral-24B-Instruct** | **96.1** | (-0.7, 0.8) | 647 |
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| Mistral-Small-3.2-24B-Instruct-2506 | 92.1 | (-0.9, 1.0) | 486 |
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| vikhr-nemo-12b-instruct-r-21-09-24(180 leaked) | 79.8 | (-2.2, 1.9) | 627 |
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#### Пример правильного использования с OpenAI-like API
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Запуск vLLM сервера: `vllm serve --dtype half --max-model-len 32000 -tp 1 Vikhrmodels/Vistral-24B-Instruct --api-key token-abc123`
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8000/v1",
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api_key="token-abc123",
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)
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llm_model = "Vikhrmodels/Vistral-24B-Instruct"
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sample_history = [
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{'role': 'user', 'content': 'Напиши краткое описание книги Гарри Поттер.'}
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]
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final_answer = llm_client.chat.completions.create(
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model=llm_model,
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messages=sample_history,
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temperature=0.3,
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max_tokens=2048
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).choices[0].message.content
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print(final_answer)
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```
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Ответ после выполнения кода будет выглядеть примерно так:
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**Краткое описание книги «Гарри Поттер»:**
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«Гарри Поттер» — это серия фантастических романов Дж. К. Роулинг о мальчике-волшебнике, который узнаёт, что он сын могущественных магов, и отправляется учиться в школу чародейства и волшебства Хогвартс. В первом томе («Гарри Поттер и философский камень») Гарри знакомится с друзьями Роном и Гермионой, раскрывает тайну своего прошлого и сталкивается с опасным тёмным магом Волан-де-Мортом.
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В последующих книгах Гарри и его друзья борются с силами зла, раскрывают древние тайны, переживают взросление и учатся использовать волшебство во благо. Серия сочетает приключения, дружбу, магию и борьбу добра со злом.
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**Основные темы:** волшебный мир, дружба, храбрость, преданность, борьба со злом.
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### Нюансы и ограничения
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- Модель имеет **низкий уровень безопасности ответов** и нацелена на правильное и полное выполенние инструкций, имейте это ввиду при использовании и тестируйте самостоятельно. Частично это исправляется системными промптами и дополнительными указаниями о важности безопасности в промпте пользователя.
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- Системные промпты не предназначены для описание персонажей, мы рекомендуем использовать их для спецификации стиля ответа (вроде "answer only in json format"). Кроме того, желательно, писать их **на английском языке**, так как так было в датасете, от использования английского в системных промтпах не зависит язык ответа.
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- Модель лучше использовать с низкой темптературой (0.1-0.5), а таже использовать top_k (30-50), при температуре 1.0 были замечены случайные дефекты генерации.
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### Авторы
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- Nikolay Kompanets, [LakoMoor](https://t.me/lakomoordev), [Vikhr Team](https://t.me/vikhrlabs)
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- Sergei Bratchikov, [NLP Wanderer](https://t.me/nlpwanderer), [Vikhr Team](https://t.me/vikhrlabs)
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- Konstantin Korolev, [Vikhr Team](https://t.me/vikhrlabs)
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- Aleksandr Nikolich, [Vikhr Team](https://t.me/vikhrlabs)
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```
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@inproceedings{nikolich2024vikhr,
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title={Vikhr: Advancing Open-Source Bilingual Instruction-Following Large Language Models for Russian and English},
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author={Aleksandr Nikolich and Konstantin Korolev and Sergei Bratchikov and Nikolay Kompanets and Igor Kiselev and Artem Shelmanov},
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booktitle={Proceedings of the 4th Workshop on Multilingual Representation Learning (MRL) @ EMNLP-2024},
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year={2024},
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publisher={Association for Computational Linguistics},
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url={https://arxiv.org/pdf/2405.13929}
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}
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```
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{{ bos_token }}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
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'+ message['content'] | trim + '</s>' %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 32768,
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"max_position_embeddings": 131072,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 40,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.0",
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"use_cache": true,
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"vocab_size": 131074
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user