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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,166 @@
+
+---
+library_name: transformers
+model_name: QVikhr-3-4B-Instruction
+base_model:
+- Qwen/Qwen3-4B
+language:
+- ru
+- en
+license: apache-2.0
+datasets:
+- Vikhrmodels/GrandMaster2
+---
+
+# QVikhr-3-4B-Instruction
+
+#### RU
+
+Инструктивная модель на основе **Qwen/Qwen3-4B**, обученная на русскоязычном датасете **GrandMaster2**. Создана для высокоэффективной обработки текстов на русском и английском языках, обеспечивая точные ответы и быстрое выполнение задач.
+
+#### EN
+
+Instructive model based on **Qwen/Qwen3-4B**, trained on the Russian-language dataset **GrandMaster2**. Designed for high-efficiency text processing in Russian and English, delivering precise responses and fast task execution.
+
+## Quantized variants:
+
+- GGUF [Vikhrmodels/QVikhr-3-4B-Instruction-GGUF](https://huggingface.co/Vikhrmodels/QVikhr-3-4B-Instruction-GGUF)
+- MLX
+  - 4 bit [Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit](https://huggingface.co/Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit)
+  - 8 bit [Vikhrmodels/QVikhr-3-4B-Instruction-MLX_8bit](https://huggingface.co/Vikhrmodels/QVikhr-3-4B-Instruction-MLX_8bit)
+
+## Особенности / Features:
+
+- 📚 Основа / Base: [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
+- 🇷🇺 Специализация / Specialization: **RU**
+- 💾 Датасет / Dataset: [GrandMaster2](https://huggingface.co/datasets/Vikhrmodels/GrandMaster2)
+- 🌍 Поддержка / Support: **Bilingual RU/EN**
+
+## Попробовать / Try now:
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DvostFGC_7jnziSUaZ0gJnADhOi5lrSD?usp=sharing)
+
+## Ru Arena General
+
+| Model                                    | Score | 95% CI    | Avg. #Tokens |
+|------------------------------------------|-------|-----------|--------------|
+| Vikhrmodels-QVikhr-3-4B-Instruction  |78.2   |(-1.6, 1.9)  |1854          |
+| Qwen3-4B      |64.8   |(-1.9, 1.9)  |2161          |
+| Vikhrmodels-QVikhr-3-1.7B-Instruction-noreasoning  |59.2   |(-2.1, 1.8)  |1053          |
+| noresoning-Qwen3-1.7B                    |51.9   |(-1.9, 1.5)  |999           |
+| Qwen3-1.7B                               |49.7   |(-1.8, 1.9)   |1918          |
+
+## Описание / Description:
+
+#### RU
+
+**QVikhr-3-4B-Instruction** — мощная языковая модель, обученная на датасете **GrandMaster-2**, поддерживает генерацию инструкций, контекстные ответы и анализ текста на русском языке. Эта модель оптимизирована для задач инструктивного обучения и обработки текстов. Она подходит для использования в профессиональной среде, а также для интеграции в пользовательские приложения и сервисы.
+
+Модель построена на базе архитектуры [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) и была дообучена на большом русскоязычном датасете [GrandMaster2](https://huggingface.co/datasets/Vikhrmodels/GrandMaster2). Такое специализированное обучение значительно улучшило её способность генерировать точные, контекстно-зависимые ответы и быстро выполнять задачи на русском языке.
+
+Тесты производительности подтверждают значительные улучшения модели. В рейтинге «Ru Arena General» QVikhr-3-4B-Instruction получила оценку 78.2, что существенно превосходит результат базовой модели Qwen3-4B (64.8). Это доказывает её превосходные возможности для решения задач на русском языке. 
+
+#### EN
+
+**QVikhr-3-4B-Instruction** is a powerful language model trained on the **GrandMaster-2** dataset, supporting instruction generation, contextual responses and text analysis in Russian. This model is optimized for instructional learning and text processing tasks. It is suitable for use in professional environments as well as for integration into user applications and services.
+
+The model is based on the [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) architecture and has been further trained on the large Russian-language dataset [GrandMaster2](https://huggingface.co/datasets/Vikhrmodels/GrandMaster2). This specialized training significantly improved its ability to generate accurate, context-sensitive answers and to perform tasks quickly in Russian.
+
+Performance tests confirm the model's significant improvements. In the Ru Arena General ranking, QVikhr-3-4B-Instruction received a score of 78.2, which is a significant improvement over the base model Qwen3-4B (64.8). This proves its excellent capabilities for solving problems in Russian.
+
+Translated with DeepL.com (free version)
+
+## Обучение / Training:
+
+#### RU
+
+**QVikhr-3-4B-Instruction** была создана с использованием метода SFT (Supervised Fine-Tuning). Мы использовали синтетический датасет **GrandMaster-2**.
+
+#### EN
+
+**QVikhr-3-4B-Instruction** was developed using the SFT (Supervised Fine-Tuning). The synthetic dataset **GrandMaster-2**.
+
+## 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
+
+# Load the model and tokenizer
+model_name = "Vikhrmodels/QVikhr-3-4B-Instruction"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Prepare the input text
+input_text = "Напиши краткое описание книги Гарри Поттер."
+
+messages = [
+    {"role": "user", "content": input_text},
+]
+
+# Tokenize and generate text
+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,
+)
+
+# Decode and print result
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+````
+
+#### Ответ модели / Model response:
+
+> \<think\>
+Хорошо, мне нужно написать краткое описание книги "Гарри Поттер и Философский камень". Сначала проверю, что именно требуется: пользователь просит краткое описание, а не резюме или анализ. Важно сохранить ключевые моменты, но не перегружать текст.
+>
+>Начну с основных элементов: Гарри Поттер, его происхождение, школа Хогвартс, главную магию, врагов (Волан-де-Морт, Драко Малфой, Слизни) и финальный бой. Нужно упомянуть, что это начало саги, и включить основные события первой книги.
+>
+>Следует избегать деталей, которые могут быть важны только для следующих книг, чтобы не создать путаницы. Также важно подчеркнуть, что это история о дружбе, борьбе с злом и поиске себя.
+>
+>Проверю структуру: введение, основные персонажи, сюжетные линии, конфликт и финал. Убедюсь, что текст плавный и лаконичный, без лишних слов. Возможно, стоит добавить, что книга создала культовый феномен, чтобы показать её влияние.
+>
+>Теперь соберу всё вместе в логичном порядке, убирая повторы и упрощая сложные моменты. Убедюсь, что описание соответствует ожиданиям пользователя и не содержит ошибок.
+> \</think\>
+>
+>**Краткое описание книги "Гарри Поттер и Философский камень":**  
+Год 1991. Малыш Гарри Поттер, чья мать погибла, убив Волан-де-Морта, был спасён и воспитан Дамблдором, а позже отправлен в школу Хогвартс. Там он узнаёт, что является потомком великих магов и врагом смерти — чудовища, убившего его родителей. Вместе с двумя друзьями Роном Уизли и Гермионой Грейнджер Гарри участвует в тайном охоте на Философский камень, чтобы предотвратить вторжение Волан-де-Морта. Столкнувшись с ловушками, загадками и предательством, мальчик проходит испытания, доказывая свою силу и веру. В финале он побеждает врага, спасая мир, а Хогвартс открывает ему двери в новую, более опасную сагу.  
+>
+>Эта книга — начало ве��икой истории о дружбе, магии, предательстве и поиске истины, ставшей культовой во всём мире.
+
+### Авторы / Authors
+
+  - Sergei Bratchikov, [NLP Wanderer](https://t.me/nlpwanderer), [Vikhr Team](https://t.me/vikhrlabs)
+  - Nikolay Kompanets, [LakoMoor](https://t.me/lakomoordev), [Vikhr Team](https://t.me/vikhrlabs)
+  - Konstantin Korolev, [Vikhr Team](https://t.me/vikhrlabs)
+  - Aleksandr Nikolich, [Vikhr Team](https://t.me/vikhrlabs)
+
+### Как цитировать / How to Cite
+
+```bibtex
+@inproceedings{nikolich2024vikhr,
+  title={Vikhr: Advancing Open-Source Bilingual Instruction-Following Large Language Models for Russian and English},
+  author={Aleksandr Nikolich and Konstantin Korolev and Sergei Bratchikov and Nikolay Kompanets and Igor Kiselev and Artem Shelmanov},
+  booktitle={Proceedings of the 4th Workshop on Multilingual Representation Learning (MRL) @ EMNLP-2024},
+  year={2024},
+  publisher={Association for Computational Linguistics},
+  url={[https://arxiv.org/pdf/2405.13929](https://arxiv.org/pdf/2405.13929)}
+}
+
+@misc{qwen3technicalreport,
+      title={Qwen3 Technical Report}, 
+      author={Qwen Team},
+      year={2025},
+      eprint={2505.09388},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.09388}, 
+}
+```

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.52.4"
+}

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