初始化项目，由ModelHub XC社区提供模型
Model: AvitoTech/avibe Source: Original Platform
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README.md
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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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- ru
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- en
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base_model:
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- Qwen/Qwen3-8B
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pipeline_tag: text-generation
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library_name: transformers
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---
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A-vibe это большая языковая модель, созданная Авито Тех, дочерней технологической компанией Авито, на базе открытой модели Qwen3-8B-Base.
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Мы адаптировали Qwen3-8B-Base под русский язык и домен Авито с помощью нескольких шагов:
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1. Сделали свой токенизатор, оптимальный для русского и английского языка
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2. Подменили оригинальный токенизатор Qwen3-8B-Base на собственный токенизатор
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3. Обучили полученную модель на большом корпусе данных
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4. Провели SFT этап
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5. Сделали RL
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В результате нам удалось получить модель, которая выигрывает instruct версию Qwen3-8B по многим русскоязычным бенчмаркам.
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На SFT и RL этапе нам удалось научить модель Function Calling и улучшить ее навыки в решении математических задач.
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||Qwen3-8B (no_think)|A-vibe|
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|:---|:---|:---|
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|mmlu_ru|0,701|**0,718**|
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|mmlu_en|0,730|**0,752**|
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|gpqa_diamond_ru|0,318|**0,343**|
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|gpqa_diamond_en|**0,369**|0,318|
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|shlepa|0,454|**0,486**|
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|baby mmlu|0,682|**0,766**|
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|math_500_ru|0,546|**0,686**|
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|math_500_en|**0,736**|0,714|
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|gsm8k_en|**0,927**|0,910|
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|DOoM|0,240|**0,280**|
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|ru_facts|**0,724**|0,718|
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|rublimp|0,916|**0,930**|
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|ru_drop|0,318|**0,394**|
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|BFCL_V3_en|**60,2%**|58,63%|
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|BFCL_V3_ru|**50.72%**|49.00%|
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|MERA_text|0,510|**0,618**|
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|MERA CODE private total|0,336|**0,367**|
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В токенизаторе A-vibe плотность токенизации выше, чем у Qwen3-8B поэтому число токенов в контексте и при генерации стало меньше для одинаковых примеров.
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Кроме того, размер самой модели сократился до 7.9B при 8.2B у Qwen3-8B. За счет этого одинаковые русскоязычные примеры адаптированной моделью обрабатываются быстрее в среднем на 15-25% в сравнении с исходной Qwen3-8B
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# Quickstart
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Ниже представлен фрагмент кода, демонстрирующий, как загрузить токенизатор и модель, а также как сгенерировать ответ.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "AvitoTech/avibe"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Привет, подскажи рецепт борща"
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=1024
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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<details>
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<summary> Output:</summary>
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Вот классический рецепт борща с пошаговыми инструкциями:
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---
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### **Ингредиенты (на 4 порции):**
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- **Мясо** (говядина, свинина или курица) — 300–400 г
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- **Свекла** — 2 шт. (средние)
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- **Капуста белокочанная** — 300 г
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- **Морковь** — 1 шт.
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- **Лук репчатый** — 1 шт.
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- **Картофель** — 3–4 шт.
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- **Помидоры свежие** (или томатная паста) — 2 шт. (или 2 ст. ложки)
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- **Чеснок** — 2–3 зубчика
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- **Лавровый лист** — 1–2 шт.
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- **Перец горошком** — 3–5 шт.
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- **Соль, сахар** — по вкусу
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- **Растительное масло** — 2–3 ст. ложки
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- **Уксус или лимонный сок** — 1 ст. ложка (для сохранения цвета свеклы)
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- **Зелень** (укроп, петрушка) — для подачи
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- **Сметана** — по желанию
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---
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### **Пошаговое приготовление:**
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1. **Подготовка бульона:**
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- Мясо промойте, нарежьте на куски и залейте 2,5–3 литрами холодной воды.
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- Доведите до кипения, снимите пену.
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- Добавьте лавровый лист, перец горошком и варите 1,5–2 часа на медленном огне. За 30 минут до готовности посолите.
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2. **Овощи:**
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- **Свеклу** натрите на крупной терке. Обжарьте на растительном масле 5–7 минут, добавьте уксус (или лимонный сок) и 2 ст. ложки воды. Тушите 10 минут.
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- **Капусту** нашинкуйте тонкой соломкой.
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- **Картофель** нарежьте кубиками.
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- **Морковь** натрите, лук — мелко нарежьте.
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- **Помидоры** натрите на терке или используйте томатную пасту.
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3. **Сборка борща:**
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- В кипящий бульон сначала добавьте картофель, варите 10 минут.
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- Затем введите капусту, морковь и лук. Варите еще 10 минут.
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- Добавьте обжаренную свеклу и помидоры (или томатную пасту). Перемешайте.
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- За 5 минут до готовности влейте измельченный чеснок (через пресс или мелко нарезанный).
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4. **Финальные штрихи:**
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- Проверьте вкус, добавьте сахар (если нужно) и зелень.
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- Снимите с огня, накройте крышкой и дайте настояться 15–20 минут.
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5. **Подача:**
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- Разлейте борщ по тарелкам, добавьте сметану и свежую зелень.
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---
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### **Советы:**
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- Для более насыщенного вкуса можно обжарить мясо перед варкой.
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- Если любите кислинку, добавьте 1 ст. ложку уксуса или лимонного сока в самом конце.
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- Вегетарианский вариант: замените мясо на грибы или варите на овощном бульоне.
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Приятного аппетита! 🍲 </details>
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added_tokens.json
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added_tokens.json
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{
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"<|box_end|>": 116384,
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"<|box_start|>": 116383,
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"<|endoftext|>": 116392,
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"<|im_end|>": 116380,
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"<|im_start|>": 116379,
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"<|image_pad|>": 116390,
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"<|object_ref_end|>": 116382,
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"<|object_ref_start|>": 116381,
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"<|quad_end|>": 116386,
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"<|quad_start|>": 116385,
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"<|sep|>": 116393,
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"<|video_pad|>": 116391,
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"<|vision_end|>": 116388,
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"<|vision_pad|>": 116389,
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"<|vision_start|>": 116387
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}
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- '' }}
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{%- endif %}
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{{- "\n\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" and not message.tool_calls %}
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{%- set content = message.content %}
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{%- if not loop.last %}
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{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
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{%- if not loop.last %}
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{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
|
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
|
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{%- if add_generation_prompt %}
|
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{{- '<|im_start|>assistant\n' }}
|
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{%- endif %}
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33
config.json
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config.json
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{
|
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"architectures": [
|
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"Qwen3ForCausalLM"
|
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 116393,
|
||||
"decoder_start_token_id": 116393,
|
||||
"eos_token_id": 116380,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 116392,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.0.dev0",
|
||||
"unk_token_id": null,
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 116394
|
||||
}
|
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generation_config.json
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generation_config.json
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{
|
||||
"bos_token_id": 116392,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
116380,
|
||||
116392
|
||||
],
|
||||
"pad_token_id": 116392,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "4.52.0.dev0"
|
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}
|
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116143
merges.txt
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116143
merges.txt
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Load Diff
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model-00001-of-00004.safetensors
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406
model.safetensors.index.json
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406
model.safetensors.index.json
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{
|
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"metadata": {
|
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"total_size": 15799150592
|
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|
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"weight_map": {
|
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"lm_head.weight": "model-00004-of-00004.safetensors",
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"model.layers.9.self_attn.k_norm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_norm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
38
special_tokens_map.json
Normal file
38
special_tokens_map.json
Normal file
@@ -0,0 +1,38 @@
|
||||
{
|
||||
"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
|
||||
},
|
||||
"sep_token": {
|
||||
"content": "<|sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
581144
tokenizer.json
Normal file
581144
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
284
tokenizer_config.json
Normal file
284
tokenizer_config.json
Normal file
@@ -0,0 +1,284 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens": [
|
||||
{
|
||||
"content": "<|im_start|>",
|
||||
"id": 80060,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|im_end|>",
|
||||
"id": 80061,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|object_ref_start|>",
|
||||
"id": 80062,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|object_ref_end|>",
|
||||
"id": 80063,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|box_start|>",
|
||||
"id": 80064,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|box_end|>",
|
||||
"id": 80065,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|quad_start|>",
|
||||
"id": 80066,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|quad_end|>",
|
||||
"id": 80067,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|vision_start|>",
|
||||
"id": 80068,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|vision_end|>",
|
||||
"id": 80069,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|vision_pad|>",
|
||||
"id": 80070,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|image_pad|>",
|
||||
"id": 80071,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|video_pad|>",
|
||||
"id": 80072,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
{
|
||||
"content": "<|endoftext|>",
|
||||
"id": 80073,
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
],
|
||||
"added_tokens_decoder": {
|
||||
"116379": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116380": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116381": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116382": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116383": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116384": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116385": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116386": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116387": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116388": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116389": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116390": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116391": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116392": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"116393": {
|
||||
"content": "<|sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"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,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"max_length": null,
|
||||
"model_max_length": 1010000,
|
||||
"pad_to_multiple_of": null,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"pad_token_type_id": 0,
|
||||
"padding_side": "left",
|
||||
"sep_token": "<|sep|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
116381
vocab.json
Normal file
116381
vocab.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user