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Model: Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning Source: Original Platform
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---
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library_name: transformers
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model_name: QVikhr-3-1.7B-Instruction-noreasoning
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base_model:
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- Qwen/Qwen3-1.7B
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language:
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- ru
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- en
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license: apache-2.0
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datasets:
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- Vikhrmodels/GrandMaster2
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---
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# QVikhr-3-1.7B-Instruction-noreasoning
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#### RU
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Инструктивная модель на основе **Qwen/Qwen3-1.7B**, обученная на русскоязычном датасете **GrandMaster2**. Создана для высокоэффективной обработки текстов на русском и английском языках, обеспечивая точные ответы и быстрое выполнение задач.
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#### EN
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Instructive model based on **Qwen/Qwen3-1.7B**, trained on the Russian-language dataset **GrandMaster2**. Designed for high-efficiency text processing in Russian and English, delivering precise responses and fast task execution.
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## Quantized variants:
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- GGUF [Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-GGUF](https://huggingface.co/Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-GGUF)
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- MLX
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- 4 bit [Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_4bit](https://huggingface.co/Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_4bit)
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- 8 bit [Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_8bit](https://huggingface.co/Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_8bit)
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## Особенности / Features:
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- 📚 Основа / Base: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
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- 🇷🇺 Специализация / Specialization: **RU**
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- 💾 Датасет / Dataset: [GrandMaster2](https://huggingface.co/datasets/Vikhrmodels/GrandMaster2)
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- 🌍 Поддержка / Support: **Bilingual RU/EN**
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## Попробовать / Try now:
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[](https://colab.research.google.com/drive/1EjliojdR7A9EQmsV8aLE7Rq_Lr_594gy?usp=sharing)
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## Ru Arena General
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| Model | Score | 95% CI | Avg. #Tokens |
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|------------------------------------------|-------|-----------|--------------|
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| Vikhrmodels-QVikhr-3-1.7B-Instruction-noreasoning | | | |
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| noresoning-Qwen3-1.7B | | | |
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| Qwen3-1.7B | | | |
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## Описание / Description:
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#### RU
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**QVikhr-3-1.7B-Instruction-noreasoning** — 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.
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#### EN
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**QVikhr-3-1.7B-Instruction-noreasoning** is a robust language model trained on the **GrandMaster-2** dataset. It excels in instruction generation, contextual responses, and text analysis in Russian. The model is optimized for instructional tasks and textual data processing, suitable for professional use as well as integration into user-facing applications and services.
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## Обучение / Training:
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#### RU
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**QVikhr-3-1.7B-Instruction-noreasoning** была создана с использованием метода SFT (Supervised Fine-Tuning) FFT (Full Fine-Tune). Мы использовали синтетический датасет **GrandMaster-2**.
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#### EN
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**QVikhr-3-1.7B-Instruction-noreasoning** was developed using the SFT (Supervised Fine-Tuning) FFT (Full Fine-Tune) method. The synthetic dataset **GrandMaster-2**.
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## Пример кода для запуска / Sample code to run:
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**Рекомендуемая температура для генерации: 0.3** / **Recommended generation temperature: 0.3**.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load the model and tokenizer
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model_name = "Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning"
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Prepare the input text
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input_text = "Напиши краткое описание книги Гарри Поттер."
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messages = [
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{"role": "user", "content": input_text},
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]
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# Tokenize and generate text
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input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
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output = model.generate(
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input_ids,
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max_length=1512,
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temperature=0.3,
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num_return_sequences=1,
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no_repeat_ngram_size=2,
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top_k=50,
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top_p=0.95,
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)
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# Decode and print result
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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print(generated_text)
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````
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#### Ответ модели / Model response:
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> \<think\>
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>
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> \</think\>
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>
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> **Краткое описание книги Гарри Поттера:**
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>
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> *«Гарри Поттер и философский камень»* — это история о мальчике, который, несмотря на слабость, стал свидетелем великих событий, связанных с магией, мистикой и поиском истинного смысла. Гарри, живущий в маленьком городке, влюбляется в магию, когда случайно участвует в неожиданном приключении, где он стал свидетелем загадочного события, связанного с «Философским камнем».
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>
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> Во время этого события он сталкивается с тремя магами, которые, по их словам, «все видят в будущем». Они предупреждают о приближении опасного события, которое может изменить мир. Гарри, несмотря на страх и неопределенность, решает следовать за магами, чтобы спасти мир.
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>
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> Вместе с Дорис и Ниммом он отправляется в путешествие в прошлое, чтобы изменить судьбу, но столкнувшись с трудностями и непредвиденными обстоятельствами, они находят силы преодолеть их.
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>
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> Конец книги — это встреча с «Философским камнем», который, как оказалось, может изменить реальность. Гарри понимает, что его выбор и действия могут изменить мир, и решает не бежать от ответственности, а стать частью великой миссии.
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>
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> *«Гарри Поттер и философский камень»* стал символом магии, веры в чудеса и силы, а также оставит на века глубокий след в литературном наследии.
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### Авторы / Authors
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- Sergei Bratchikov, [NLP Wanderer](https://t.me/nlpwanderer), [Vikhr Team](https://t.me/vikhrlabs)
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- Nikolay Kompanets, [LakoMoor](https://t.me/lakomoordev), [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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### Как цитировать / How to Cite
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```bibtex
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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](https://arxiv.org/pdf/2405.13929)}
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}
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@misc{qwen3technicalreport,
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title={Qwen3 Technical Report},
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author={Qwen Team},
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year={2025},
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eprint={2505.09388},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2505.09388},
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}
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```
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"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '/no_think' + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 12000,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
153
README.md
Normal file
153
README.md
Normal file
@@ -0,0 +1,153 @@
|
||||
|
||||
---
|
||||
library_name: transformers
|
||||
model_name: QVikhr-3-1.7B-Instruction-noreasoning
|
||||
base_model:
|
||||
- Qwen/Qwen3-1.7B
|
||||
language:
|
||||
- ru
|
||||
- en
|
||||
license: apache-2.0
|
||||
datasets:
|
||||
- Vikhrmodels/GrandMaster2
|
||||
---
|
||||
|
||||
# QVikhr-3-1.7B-Instruction-noreasoning
|
||||
|
||||
#### RU
|
||||
|
||||
Инструктивная модель на основе **Qwen/Qwen3-1.7B**, 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/Qwen3-1.7B**, 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-3-1.7B-Instruction-noreasoning-GGUF](https://huggingface.co/Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-GGUF)
|
||||
- MLX
|
||||
- 4 bit [Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_4bit](https://huggingface.co/Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_4bit)
|
||||
- 8 bit [Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_8bit](https://huggingface.co/Vikhrmodels/QVikhr-3-1.7B-Instruction-noreasoning-MLX_8bit)
|
||||
|
||||
## Особенности / Features:
|
||||
|
||||
- 📚 Основа / Base: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
|
||||
- 🇷🇺 Специализация / Specialization: **RU**
|
||||
- 💾 Датасет / Dataset: [GrandMaster2](https://huggingface.co/datasets/Vikhrmodels/GrandMaster2)
|
||||
- 🌍 Поддержка / Support: **Bilingual RU/EN**
|
||||
|
||||
## Попробовать / Try now:
|
||||
|
||||
[](https://colab.research.google.com/drive/1EjliojdR7A9EQmsV8aLE7Rq_Lr_594gy?usp=sharing)
|
||||
|
||||
## Ru Arena General
|
||||
|
||||
| Model | Score | 95% CI | Avg. #Tokens |
|
||||
|------------------------------------------|-------|-----------|--------------|
|
||||
| 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-3-1.7B-Instruction-noreasoning** — 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.
|
||||
|
||||
#### EN
|
||||
|
||||
**QVikhr-3-1.7B-Instruction-noreasoning** is a robust language model trained on the **GrandMaster-2** dataset. It excels in instruction generation, contextual responses, and text analysis in Russian. The model is optimized for instructional tasks and textual data processing, suitable for professional use as well as integration into user-facing applications and services.
|
||||
|
||||
## Обучение / Training:
|
||||
|
||||
#### RU
|
||||
|
||||
**QVikhr-3-1.7B-Instruction-noreasoning** была создана с использованием метода SFT (Supervised Fine-Tuning) FFT (Full Fine-Tune). Мы использовали синтетический датасет **GrandMaster-2**.
|
||||
|
||||
#### EN
|
||||
|
||||
**QVikhr-3-1.7B-Instruction-noreasoning** was developed using the SFT (Supervised Fine-Tuning) FFT (Full Fine-Tune) method. 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-3-1.7B-Instruction-noreasoning"
|
||||
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\>
|
||||
>
|
||||
> \</think\>
|
||||
>
|
||||
> **Краткое описание книги Гарри Поттера:**
|
||||
>
|
||||
> *«Гарри Поттер и философский камень»* — это история о мальчике, который, несмотря на слабость, стал свидетелем великих событий, связанных с магией, мистикой и поиском истинного смысла. Гарри, живущий в маленьком городке, влюбляется в магию, когда случайно участвует в неожиданном приключении, где он стал свидетелем загадочного события, связанного с «Философским камнем».
|
||||
>
|
||||
> Во время этого события он сталкивается с тремя магами, которые, по их словам, «все видят в будущем». Они предупреждают о приближении опасного события, которое может изменить мир. Гарри, несмотря на страх и неопределенность, решает следовать за магами, чтобы спасти мир.
|
||||
>
|
||||
> Вместе с Дорис и Ниммом он отправляется в путешествие в прошлое, чтобы изменить судьбу, но столкнувшись с трудностями и непредвиденными обстоятельствами, они находят силы преодолеть их.
|
||||
>
|
||||
> Конец книги — это встреча с «Философским камнем», который, как оказалось, может изменить реальность. Гарри понимает, что его выбор и действия могут изменить мир, и решает не бежать от ответственности, а стать частью великой миссии.
|
||||
>
|
||||
> *«Гарри Поттер и философский камень»* стал символом магии, веры в чудеса и силы, а также оставит на века глубокий след в литературном наследии.
|
||||
|
||||
### 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},
|
||||
}
|
||||
```
|
||||
28
added_tokens.json
Normal file
28
added_tokens.json
Normal file
@@ -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
|
||||
}
|
||||
30
config.json
Normal file
30
config.json
Normal file
@@ -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": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 6144,
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 28,
|
||||
"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": "bfloat16",
|
||||
"transformers_version": "4.51.3",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"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": "4.51.3"
|
||||
}
|
||||
BIN
merges.txt
(Stored with Git LFS)
Normal file
BIN
merges.txt
(Stored with Git LFS)
Normal file
Binary file not shown.
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8c18861c5c1eb26003dead027e2a723c4fe63f1bf16406db6c5a4c2fc4941742
|
||||
size 3441185608
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
240
tokenizer_config.json
Normal file
240
tokenizer_config.json
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '/no_think' + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 12000,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f29e2a6259050a0fcb2a9ea5569c0bf913a8f3d0c534cc2408ad1c767367d75f
|
||||
size 5688
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user