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Upload VK Education GQA-ru LoRA adapter

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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: Qwen/Qwen3.5-0.8B
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ tags:
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+ - lora
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+ - peft
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+ - vk-education
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+ - deepvk
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+ - gqa-ru
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+ - visual-question-answering
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+ datasets:
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+ - deepvk/GQA-ru
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+ ---
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+
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+ # vk-vlm-gqa-ru-qwen35-08b-lora
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+
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+ LoRA-адаптер, обученный для проекта VK Education Vision-Language Modeling на открытых данных
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+ VK/DeepVK GQA-ru.
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+
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+ Автор: Ибрагимов Далгат Магомедалиевич, МАИ институт 8, группа М8О-308Б-32.
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+
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+ ## Данные
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+
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+ Использован открытый датасет `deepvk/GQA-ru` из коллекции DeepVK VLM на Hugging Face. Данные были
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+ приведены к JSONL-формату image/question/answer и использованы для обучения VQA-style модели.
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+
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+ Локальные размеры split в запуске:
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+
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+ | Split | Samples |
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+ |---|---:|
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+ | train | 38 019 |
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+ | validation | 1 981 |
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+ | test | 12 216 |
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+
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+ ## Обучение
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+
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+ | Параметр | Значение |
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+ |---|---|
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+ | Base model | `Qwen/Qwen3.5-0.8B` |
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+ | Adapter | LoRA |
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+ | Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
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+ | Rank / alpha / dropout | `16 / 32 / 0.05` |
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+ | Epochs | `1.0` |
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+ | Batch size | `8` |
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+ | Learning rate | `2e-4` |
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+ | Precision | `bf16` |
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+ | Seed | `42` |
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+
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+ Лучший checkpoint: `checkpoint-4560`, выбран по `eval_loss`.
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+
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+ ## Метрики
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+
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+ | Metric | Value |
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+ |---|---:|
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+ | train_loss | 0.04432422036801592 |
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+ | eval_loss | 0.4337001144886017 |
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+ | train_runtime_sec | 6219.1947 |
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+ | train_samples_per_second | 6.113 |
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+ | eval_samples_per_second | 17.075 |
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+
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+ Ограничение: сохраненная метрика является validation loss от `transformers.Trainer`. Полный
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+ benchmark scoring для GQA-ru/MMBench-ru accuracy в текущем репозитории еще не реализован.
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+
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+ ## Использование
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+
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+ ```python
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+ from peft import PeftModel
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B", trust_remote_code=True)
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+ tokenizer = AutoTokenizer.from_pretrained("lockR/vk-vlm-gqa-ru-qwen35-08b-lora", trust_remote_code=True)
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+ model = PeftModel.from_pretrained(base_model, "lockR/vk-vlm-gqa-ru-qwen35-08b-lora")
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+ ```
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+
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+ ## Репозиторий проекта
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+
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+ https://github.com/L0ckR/VK_education_vllm
adapter_config.json ADDED
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+ "trainable_token_indices": null,
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+ "use_qalora": false,
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+ "use_rslora": false
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+ {
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+ "project": "VK Education Vision-Language Modeling",
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+ "author": {
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+ "name": "Ибрагимов Далгат Магомедалиевич",
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+ "institution": "МАИ, институт 8",
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+ "group": "М8О-308Б-32"
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+ },
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+ "hf_artifact": "https://huggingface.co/lockR/vk-vlm-gqa-ru-qwen35-08b-lora",
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+ "best_run": "gqa_ru_qwen35_0_8b_lora_fast_v1",
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+ "adapter_type": "LoRA",
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+ "dataset": {
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+ "name": "deepvk/GQA-ru",
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+ "source": "https://huggingface.co/datasets/deepvk/GQA-ru",
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+ "train_samples": 38019,
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+ "validation_samples": 1981,
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+ "test_samples": 12216,
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+ "usage": "Train/validation split for Russian visual question answering fine-tuning."
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+ },
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+ "training": {
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+ "seed": 42,
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+ "epochs": 1.0,
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+ "batch_size": 8,
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+ "learning_rate": 0.0002,
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+ "mixed_precision": "bf16",
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+ "lora_r": 16,
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+ "lora_alpha": 32,
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+ "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"],
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+ "best_metric_name": "eval_loss",
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+ },
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+ "limitations": [
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+ "scripts/eval.py currently does not implement full GQA-ru/MMBench-ru generative benchmark scoring.",
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+ "Reported metric is validation loss from Trainer, not leaderboard accuracy.",
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+ "MMBench-ru was documented and configured but not measured in the available run artifacts."
49
+ ]
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+ }
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