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Model: melsmm/Spell-Corrector-RU-4B Source: Original Platform
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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<|im_start|>assistant
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{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
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{{ end }}{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER num_ctx 4096
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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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base_model:
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- RefalMachine/RuadaptQwen3-4B-Instruct
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datasets:
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- melsmm/spell-correction-ru
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- ai-forever/spellcheck_punctuation_benchmark
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- spell-correction
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- spelling-correction
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- punctuation-restoration
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- grammatical-error-correction
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- russian
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- qwen3
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- lora
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---
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# Spell-Corrector-RU-4B
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**Spell-Corrector-RU-4B** — языковая модель для **автоматического исправления орфографических, пунктуационных и регистровых ошибок** в русскоязычных текстах.
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Модель дообучена на базе [`RefalMachine/RuadaptQwen3-4B-Instruct`](https://huggingface.co/RefalMachine/RuadaptQwen3-4B-Instruct) (адаптированная под русский язык версия [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)) методом **LoRA** в фреймворке [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). LoRA-адаптер уже **смержен** с базовой моделью — её можно использовать напрямую.
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📦 **Код, ноутбуки и полное описание проекта:** [github.com/melsmm/llm-spell-corrector](https://github.com/melsmm/llm-spell-corrector)
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📚 **Датасеты для обучения:** [`melsmm/spell-correction-ru`](https://huggingface.co/datasets/melsmm/spell-correction-ru)
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## Для чего модель
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Модель принимает текст с ошибками и возвращает его исправленную версию. Она умеет:
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- 🔤 исправлять **орфографические** ошибки и опечатки;
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- ❓ восстанавливать и исправлять **пунктуацию**;
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- 🔠 восстанавливать **регистр** букв (заглавные/строчные) и букву «ё».
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Основное преимущество перед существующими решениями (например, [SAGE](https://github.com/ai-forever/sage) на архитектуре T5) — **высокая скорость инференса** (через [vLLM](https://github.com/vllm-project/vllm), ~в 6 раз быстрее), простота внедрения в продакшен через OpenAI-совместимый API и лёгкость дообучения под новые домены с помощью LoRA.
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## На чём обучалась
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Обучение проходило в **две стадии**:
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| Стадия | Датасет | Объём | Описание |
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| --- | --- | --- | --- |
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| **Stage 1** | `synth_spell_correction_1m` | ~1 млн | Синтетические ошибки, внесённые в чистые корпусы (`nerus`, `gazeta`, `wikipedia`) с помощью [SAGE](https://github.com/ai-forever/sage) (`SBSC`, `CharAug`, `WordAug`) и собственного алгоритма пунктуационной порчи. |
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| **Stage 2** | `spell_correction_30k` | ~30 тыс. | Объединение готовых пар «ошибка → исправление» из открытых датасетов (RUSpellRU, MultidomainGold, GEC). |
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Сначала модель учится на большом объёме разнообразных синтетических ошибок, затем «дошлифовывается» на меньшем, но более качественном наборе реальных пар. Оба датасета доступны в репозитории [`melsmm/spell-correction-ru`](https://huggingface.co/datasets/melsmm/spell-correction-ru).
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**Гиперпараметры LoRA:** rank=32, alpha=64, dropout=0, bf16, cutoff length=4000.
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## Использование
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### Промпт
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Модель ожидает следующий формат запроса:
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```
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Исходный текст:
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{текст с ошибками}
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Отредактируй исходный текст, исправив ошибки.
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```
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Рекомендуемые параметры генерации: `temperature=0.1`, `top_p=0.7`.
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### Через `transformers`
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "melsmm/Spell-Corrector-RU-4B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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text = "привет как дила, что делаеш севодня"
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prompt = f"Исходный текст:\n{text}\n\nОтредактируй исходный текст, исправив ошибки."
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messages = [{"role": "user", "content": prompt}]
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inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt"
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).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=512, temperature=0.1, top_p=0.7, do_sample=True)
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print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
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```
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### Через vLLM (рекомендуется для продакшена)
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Запуск OpenAI-совместимого сервера:
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```bash
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vllm serve melsmm/Spell-Corrector-RU-4B \
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--gpu-memory-utilization 0.95 \
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--host 0.0.0.0 \
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--port 9900 \
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--served-model-name spell_correction \
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--max-model-len 4096
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```
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Обращение к серверу:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:9900/v1", api_key="EMPTY")
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text = "привет как дила"
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resp = client.chat.completions.create(
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model="spell_correction",
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messages=[{"role": "user", "content":
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f"Исходный текст:\n{text}\n\nОтредактируй исходный текст, исправив ошибки."}],
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temperature=0.1,
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top_p=0.7,
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)
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print(resp.choices[0].message.content)
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```
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## Качество
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Оценка на бенчмарке [`ai-forever/spellcheck_punctuation_benchmark`](https://huggingface.co/datasets/ai-forever/spellcheck_punctuation_benchmark) (F1 по орфографии / пунктуации / регистру):
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### RUSpellRU
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| Model | F1 (spell) | F1 (punc) | F1 (case) |
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| --- | --: | --: | --: |
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| **Spell-Corrector-RU-4B** | 64.7 | 84.5 | 94.2 |
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| sage-fredt5-large | 84.5 | 86.8 | 94.7 |
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| gpt-4 | 64.0 | 83.2 | 90.9 |
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| gpt-3.5-turbo | 42.7 | 73.7 | 79.0 |
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### MultidomainGold
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| Model | F1 (spell) | F1 (punc) | F1 (case) |
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| --- | --: | --: | --: |
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| **Spell-Corrector-RU-4B** | 62.6 | 66.5 | 80.0 |
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| sage-fredt5-large | 77.6 | 67.7 | 79.3 |
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| gpt-4 | 37.0 | 56.0 | 60.0 |
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| gpt-3.5-turbo | 27.1 | 36.2 | 49.1 |
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### MedSpellchecker
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| Model | F1 (spell) | F1 (punc) | F1 (case) |
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| --- | --: | --: | --: |
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| **Spell-Corrector-RU-4B** | 42.8 | 70.4 | 76.3 |
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| sage-fredt5-large | 72.3 | 70.3 | 82.1 |
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| gpt-4 | 49.6 | 71.9 | 67.1 |
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| gpt-3.5-turbo | 22.3 | 59.8 | 32.3 |
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### GitHubTypoCorpusRu
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| Model | F1 (spell) | F1 (punc) | F1 (case) |
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| --- | --: | --: | --: |
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| **Spell-Corrector-RU-4B** | 49.7 | 34.8 | 45.8 |
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| sage-fredt5-large | 59.5 | 42.6 | 42.7 |
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| gpt-4 | 35.7 | 38.2 | 30.2 |
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| gpt-3.5-turbo | 29.4 | 28.7 | 25.3 |
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> Полные таблицы (Precision / Recall / F1 + сравнение со Stage 1) — в [репозитории проекта](https://github.com/melsmm/llm-spell-corrector#-результаты).
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### Скорость инференса
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На RUSpellRU (2008 примеров) через vLLM: **45 с** (0.022 с/пример) против **284 с** (0.141 с/пример) у sage-fredt5-large — **~в 6 раз быстрее**.
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## Ограничения
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- Модель уступает SAGE по орфографии на ряде доменов, но конкурентна по пунктуации и регистру.
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- Качество может снижаться на узкоспециализированных доменах (например, медицина) — там рекомендуется дообучение под домен с помощью LoRA.
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- Модель работает с русским языком; для других языков не предназначена.
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## Ссылки
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- 🔗 **Проект на GitHub:** https://github.com/melsmm/llm-spell-corrector
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- 🤗 **Датасеты:** https://huggingface.co/datasets/melsmm/spell-correction-ru
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- 🧩 **Базовая модель:** https://huggingface.co/RefalMachine/RuadaptQwen3-4B-Instruct
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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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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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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) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.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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||||
{{- tool_call.arguments | tojson }}
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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") %}
|
||||
{{- '<|im_start|>user' }}
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||||
{%- endif %}
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||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\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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71
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": null,
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"dtype": "bfloat16",
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"eos_token_id": 146215,
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"head_dim": 128,
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"hidden_act": "silu",
|
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"hidden_size": 2560,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 9728,
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 262144,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 146213,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 5000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "5.0.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 146260
|
||||
}
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9
generation_config.json
Normal file
9
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||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5408c0e76d30d5f9348593818dcaafdd45dbe64ba19fcd5fb92c2677c15f4cf1
|
||||
size 12380040
|
||||
31
tokenizer_config.json
Normal file
31
tokenizer_config.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<|endoftext|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_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|>"
|
||||
],
|
||||
"is_local": true,
|
||||
"model_max_length": 131072,
|
||||
"model_specific_special_tokens": {},
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user