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Model: Vikhrmodels/Vikhr-Llama3.1-8B-Instruct-R-21-09-24
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README.md

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+---
+license: apache-2.0
+datasets:
+- Vikhrmodels/GrandMaster-PRO-MAX
+- Vikhrmodels/Grounded-RAG-RU-v2
+language:
+- en
+- ru
+base_model:
+- meta-llama/Meta-Llama-3.1-8B-Instruct
+---
+
+## Vikhr-Llama3.1-8B-Instruct-R-21-09-24
+
+### Описание
+
+**Vikhr-Llama3.1** - это унимодальная LLM (Large Language Model) на 8B параметров представляющая из себя улучшенную версию [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) командой **VikhrModels**, адаптированную преимущественно для русского и английского языков. Для ее обучения мы использовали несколько этапов включающих в себя **SFT** и **SMPO** - нашу собственную вариацию DPO, подробнее читайте в секции *"Как эта модель создавалась"*.
+
+Модель оптимизированна для различных вариантов использования, включая ризонинг, суммаризацию, код, roleplay, поддержание диалога. Vikhr-Llama обладает возможностью многоязычной генерации, и высокопроизводительными возможностями RAG. Модель имеет лучшие оценки среди прочих на наших инструктивных и RAG бенчарках и, поэтому, мы верим, что во многих задачах может быть лучше чем gpt-3.5-turbo от OpenAI.
+
+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).
+
+### Особенности
+1. Высокое качество генераций на русском и английском языках, а также некоторых других языках, благодаря датасету [Grandmaster-PRO-MAX](https://huggingface.co/datasets/Vikhrmodels/GrandMaster-PRO-MAX) и исходной модели
+2. Поддержка системных промптов для регулирования стиля ответов
+3. Поддержка до 128k токенов контекста благодаря исходной модели (RoPE scaling)
+4. Grounded RAG режим - модель имеет специальную роль documents и специальный режим работы для поиска идентификаторов релевантных вопросу пользователя документов и использования их для ответа на вопрос, вдохновлено аналогичной способностью модели Command-R
+
+### Метрики и оценка качества
+
+Модель оценивалась на нашем русскоязычном open-source SbS бенчмарке [ru-arena-general](https://github.com/VikhrModels/ru_llm_arena) (50 топиков по 10 вопросов), где судьей выступает gpt-4-1106-preview и [бенчмарке](https://colab.research.google.com/drive/16730rWQ4-yGqWoooLs0Ece_16frmOniP?usp=sharing) для RAG на основе тестового сета [Grounded-RAG-v2](https://huggingface.co/datasets/Vikhrmodels/Grounded-RAG-RU-v2), где судей выступала gpt-4o.
+
+#### Результаты на Ru-Arena-General
+
+В качестве рефересных отвеов, с которыми сравниваются модели выступают ответы от gpt-3.5-turbo-0125, поэтому она имеет винрейт 50%.
+
+Здесь приведена лишь часть лидерборда, подробнее смотрите в репозитории бенчмарка.
+
+| Model Name                                       | Winrate  | 95% CI             | Average # Tokens |
+|--------------------------------------------------|--------|--------------------|------------------|
+| gpt-4-1106-preview                               | 90.9   | (-1.3, 1.0)        | 541              |
+| gpt-4o-mini                                      | 83.9   | (-1.8, 1.1)        | 448              |
+| vikhr-nemo-12b-instruct-r-21-09-24               | 79.8   | (-2.2, 1.9)        | 627              |
+| gemma-2-9b-it-sppo-iter3                         | 73.6   | (-1.6, 2.2)        | 509              |
+| gemma-2-9b-it                                    | 69.2   | (-2.5, 1.9)        | 459              |
+| t-lite-instruct-0.1                              | 64.7   | (-2.1, 1.7)        | 810              |
+| **vikhr-llama3.1-8b-instruct-r-21-09-24**            | **63.4**   | (-2.1, 2.5)        | **618**              |
+| suzume-llama-3-8B-multilingual-orpo-borda-half   | 57.1   | (-1.9, 2.2)        | 682              |
+| mistral-nemo-instruct-2407                       | 50.5   | (-2.7, 2.6)        | 403              |
+| gpt-3.5-turbo-0125                               | 50.0   | (0.0, 0.0)         | 220              |
+| c4ai-command-r-v01                               | 49.0   | (-1.7, 2.2)        | 529              |
+| meta-llama-3.1-8b-instruct                       | 43.1   | (-2.8, 2.3)        | 628              |
+
+#### Результаты на бенчмарке RAG
+
+Общий размер тестового сета - 200 примеров, 100 для in_domain вопросов и 100 для out_of_domain.
+
+Тут для оценки качества модель-судья gpt-4o была проинструктирована учитывать релеватность и фактологическую полноту ответов исходя из документов и реферсного ответа от gpt-4-1106-preview.
+
+Подробности промптов и оценок смотрите в коде бенчмарка на [коллабе](https://colab.research.google.com/drive/16730rWQ4-yGqWoooLs0Ece_16frmOniP?usp=sharing)
+
+in_domain - вопросы которые связаны с содержанием предоставленных документов в той или иной степени \
+out_of_domain - вопросы которые специально никак не связаны с содержанием предоставленных документов
+
+<table>
+<thead>
+  <tr>
+    <th rowspan="2">question_type</th>
+    <th colspan="3">gpt-4o</th>
+  </tr>
+  <tr>
+    <th>judge_correct_percent</th>
+    <th>avg_answer_match_rougeL</th>
+    <th>avg_abs_indexes_diff</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>in_domain</td>
+    <td>73%</td>
+    <td>0.34</td>
+    <td>NaN</td>
+  </tr>
+  <tr>
+    <td>out_of_domain</td>
+    <td>81%</td>
+    <td>0.20</td>
+    <td>NaN</td>
+  </tr>
+</tbody>
+</table>
+
+<table>
+<thead>
+  <tr>
+    <th style="visibility: hidden;" rowspan="2">question_type</th>
+    <th colspan="3">Vikhr-Llama3.1-8B-Instruct-R-21-09-24</th>
+  </tr>
+  <tr>
+    <th style="visibility: hidden;">judge_correct_percent</th>
+    <th style="visibility: hidden;">avg_answer_match_rougeL</th>
+    <th style="visibility: hidden;">avg_abs_indexes_diff</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>in_domain</td>
+    <td>64%</td>
+    <td>0.41</td>
+    <td>0</td>
+  </tr>
+  <tr>
+    <td>out_of_domain</td>
+    <td>89%</td>
+    <td>0.51</td>
+    <td>0</td>
+  </tr>
+</tbody>
+</table>
+
+<table>
+<thead>
+  <tr>
+    <th style="visibility: hidden;" rowspan="2">question_type</th>
+    <th colspan="3">gpt-4o-mini</th>
+  </tr>
+  <tr>
+    <th style="visibility: hidden;">judge_correct_percent</th>
+    <th style="visibility: hidden;">avg_answer_match_rougeL</th>
+    <th style="visibility: hidden;">avg_abs_indexes_diff</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>in_domain</td>
+    <td>65%</td>
+    <td>0.33</td>
+    <td>NaN</td>
+  </tr>
+  <tr>
+    <td>out_of_domain</td>
+    <td>73%</td>
+    <td>0.18</td>
+    <td>NaN</td>
+  </tr>
+</tbody>
+</table>
+
+<table>
+<thead>
+  <tr>
+    <th style="visibility: hidden;" rowspan="2">question_type</th>
+    <th colspan="3">gpt-3.5-turbo-0125 </th>
+  </tr>
+  <tr>
+    <th style="visibility: hidden;">judge_correct_percent</th>
+    <th style="visibility: hidden;">avg_answer_match_rougeL</th>
+    <th style="visibility: hidden;">avg_abs_indexes_diff</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>in_domain</td>
+    <td>49%</td>
+    <td>0.28</td>
+    <td>NaN</td>
+  </tr>
+  <tr>
+    <td>out_of_domain</td>
+    <td>76%</td>
+    <td>0.20</td>
+    <td>NaN</td>
+  </tr>
+</tbody>
+</table>
+
+### Как эта модель создавалась
+
+#### Инструктивная SFT часть
+
+Для SFT этапа обучения модели мы подготовили большой (150к инструкций) инструктивный синтетический датасет [Vikhrmodels/GrandMaster-PRO-MAX](https://huggingface.co/datasets/Vikhrmodels/GrandMaster-PRO-MAX). Его особенностью является встроеный CoT (Chain-Of-Thought), для сбора которого мы использовали модифицированный промет для gpt-4-turbo, подробности в карточке датасета.
+
+Кроме того, для того чтобы сделать RAG Grounding, мы подготовили другой синтетический датасет - [Vikhrmodels/Grounded-RAG-RU-v2](https://huggingface.co/datasets/Vikhrmodels/Grounded-RAG-RU-v2) (50k диалогов), его пайплайн сборки достаточно сложный для короткого описания и полробнее об этом вы можете прочитать в его карточке.
+
+#### Этап алайнмента с SMPO
+
+Для дальнейшего улучшения качества ответов мы использовали следущий пайплайн:
+1) Обучили кастомную Reward модель (она пока не будет выкладываться в открытый доступ)
+2) Дедуплицировали и отфилтровали используя RM модель оригинальный датасет Vikhrmodels/GrandMaster-PRO-MAX, получив порядка 10к самых высококачественных и разнообразных диалогов.
+3) Сделали Rejection Sampling с SFT чекпоинтом используя полученный датасет и Reward модель. (Генерировали 7 гипотез и брали только 2 самые худшие как rejected)
+4) Дообучили SFT чекпоинт с помощью нашего метода SMPO используя полученный датасет из этапа 3. SMPO был спроектирован и выбран как метод для повышения стабильности тренировки преференсов в условиях Rejection Sampling и достижения нужного margin.
+
+Реализацию SMPO, rejection sampling и другое можно найти в нашей библиотеке [effective_llm_alignment](https://github.com/VikhrModels/effective_llm_alignment/) на GitHub
+
+Идея использования именно SMPO, а не другого PO метода, возникла в результате проведения большого количества экспериментов с классическими методами, при необходимости лучшего контроля процесса сходимости. При тщательной настройке других методов (например SimPO), можно добится похожего результата, однако мы постарались стаблизировать этот процесс и объединить лучшие практики из других методов.
+
+### Как работать с RAG
+
+Роль documents представляет из себя список словарей с описанием контента документов, с примнением `json.dumps(array, ensure_ascii=False)` (см. пример ниже). \
+Контент документов может быть представлен в **3** различных форматах: **Markdown**, **HTML**, **Plain Text**. Контент каждого документа - может быть чанком текста длиной до 4к символов.
+
+```json
+[
+  {
+    "doc_id": (0..5),
+    "title": "(null or str)",
+    "content": "(html or markdown or plain text)"
+  }
+]
+```
+
+#### Пример правильного использования с OpenAI-like API
+
+Запуск vLLM сервера: `vllm serve --dtype half --max-model-len 32000 -tp 1 Vikhrmodels/Vikhr-Llama3.1-8B-Instruct-R-21-09-24 --api-key token-abc123`
+
+```python
+GROUNDED_SYSTEM_PROMPT = "Your task is to answer the user's questions using only the information from the provided documents. Give two answers to each question: one with a list of relevant document identifiers and the second with the answer to the question itself, using documents with these identifiers."
+
+documents = [
+  {
+    "doc_id": 0,
+    "title": "Глобальное потепление: ледники",
+    "content": "За последние 50 лет объем ледников в мире уменьшился на 30%"
+  },
+  {
+    "doc_id": 1,
+    "title": "Глобальное потепление: Уровень моря",
+    "content": "Уровень мирового океана повысился на 20 см с 1880 года и продолжает расти на 3,3 мм в год"
+  }
+]
+sample_history = [
+    {'role': 'system', 'content': GROUNDED_SYSTEM_PROMPT}, 
+    {'role': 'documents', 'content': json.dumps(documents, ensure_ascii=False)},
+    {'role': 'user', 'content': 'Глоабльное потепление'}
+]
+relevant_indexes = llm_client.chat.completions.create(
+    model=llm_model,
+    messages=sample_history,
+    temperature=0.0,
+    max_tokens=2048
+).choices[0].message.content
+
+print('Using documents: ' + relevant_indexes + '\n----')
+final_answer = llm_client.chat.completions.create(
+    model=llm_model,
+    messages=sample_history + [{'role': 'assistant', 'content': relevant_indexes}],
+    temperature=0.3,
+    max_tokens=2048
+).choices[0].message.content
+
+print(final_answer)
+```
+
+Ответ после выполнения кода будет выглядеть примерно так:
+
+    Using documents: {"relevant_doc_ids": [0, 1]}
+    ----
+
+    Глобальное потепление – это долгосрочное повышение средней температуры атмосферы Земли. Это явление имеет множество последствий, включая таяние ледников и повышение уровня мирового океана.
+    
+    Из доступной мне информации видно, что за последние 50 лет объем ледников в мире уменьшился на 30%. Это свидетельствует о том, что таяние ледников является одним из проявлений глобального потепления. Ледники играют важную роль в регулировании климата, так как они отражают солнечный свет и замедляют таяние снега и льда. Уменьшение их объема может привести к усилению таяния снега и льда в высоких широтах, что, в свою очередь, может привести к изменению климата в этих регионах.
+    
+    Кроме того, уровень мирового океана повысился на 20 см с 1880 года и продолжает расти на 3,3 мм в год. Это повышение уровня моря обусловлено несколькими факторами, включая таяние ледников и ледниковых щитов, а также расширение океанов из-за повышения температуры воды. Повышение уровня моря может привести к затоплению прибрежных территорий, эрозии берегов и увеличению риска наводнений.
+    
+    Глобальное потепление является сложной и многогранной проблемой, которая требует международного сотрудничества и принятия мер для сокращения выбросов парниковых газов, чтобы замедлить и, в конечном счете, остановить этот процесс.
+
+Используя первый ответ модели `relevant_indexes` (JSON), можно понять нашла ли модель информацию в документах или нет, она обучена возврашать пустой массив если ее нет и в таком случае она будет отвечать, что не смогла найти информацию в базе знаний (при генерации второго ответа).
+
+### Нюансы и ограничения
+- 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.
+- 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.
+- RAG режим **требует обязательного** наличия системного промпта `GROUNDED_SYSTEM_PROMPT` описаного в секции *Как работать с RAG*. Так же иногда модель может добавлять общую информацию из своих знаний в ответ к той, что есть в документах.
+- Модель лучше использовать с низкой темптературой (0.1-0.4) и желательно с beam search, а таже использовать top_k (30-50).
+
+### Авторы 
+- Sergei Bratchikov, [NLP Wanderer](https://t.me/nlpwanderer), Vikhr Team
+- Konstantin Korolev, Vikhr Team
+- Aleksandr Nikolich, Vikhr Team

config.json

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+{
+  "_name_or_path": "Vikhrmodels/Vikhr-Llama3.1-8B-Instruct-R-01-09-24",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "unsloth_version": "2024.8",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
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original_adapter/README.md

@@ -0,0 +1,202 @@
+---
+base_model: Vikhrmodels/Vikhr-Llama3.1-8B-Instruct-R-01-09-24
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.12.0

original_adapter/adapter_config.json

@@ -0,0 +1,34 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Vikhrmodels/Vikhr-Llama3.1-8B-Instruct-R-01-09-24",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 96,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 96,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "down_proj",
+    "o_proj",
+    "k_proj",
+    "q_proj",
+    "gate_proj",
+    "v_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

original_adapter/adapter_model.safetensors

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special_tokens_map.json

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+    "single_word": false
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+}