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Model: Vilyam888/Broken_Code_Generation.1.0
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
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+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

Dataset_BCG_1example.json

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+{
+    "id": 1,
+    "title": "Стратифицированный split и масштабирование без data leakage",
+    "difficulty": "hard",
+    "topic_tags": {
+      "Classification": 0.4,
+      "DataPreprocessing": 0.4,
+      "ModelSelection": 0.2
+    },
+    "task_context": "В учебном пайплайне для бинарной классификации нужно подготовить данные перед обучением модели. Текущая реализация допускает утечку данных: она обучает `StandardScaler` на всей выборке до разбиения на train и test, а затем делает разбиение без стратификации. Нужно сначала выполнить `train_test_split` с `test_size=0.2`, `random_state=42`, `stratify=y`, затем обучить `StandardScaler` только на `X_train`, преобразовать `X_train` и `X_test` и вернуть масштабированные выборки вместе с метками и обученным scaler.",
+    "tests": [
+      "import numpy as np",
+      "X = [[1.0, 10.0], [2.0, 20.0], [3.0, 30.0], [4.0, 40.0], [5.0, 50.0], [6.0, 60.0], [7.0, 70.0], [8.0, 80.0], [9.0, 90.0], [10.0, 100.0]]",
+      "y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]",
+      "X_train_scaled, X_test_scaled, y_train, y_test, scaler = split_and_scale_binary_data(X, y)",
+      "assert y_train == [1, 0, 0, 0, 1, 1, 1, 0]",
+      "assert y_test == [0, 1]",
+      "assert X_train_scaled.shape == (8, 2)",
+      "assert X_test_scaled.shape == (2, 2)",
+      "assert np.allclose(scaler.mean_, [5.125, 51.25])",
+      "assert np.allclose(scaler.scale_, [2.7128168017763383, 27.128168017763382])",
+      "assert np.allclose(X_train_scaled.mean(axis=0), [0.0, 0.0], atol=1e-12)",
+      "assert np.allclose(X_test_scaled, [[-0.4146981098256822, -0.4146981098256823], [1.7970251425779564, 1.7970251425779564]])"
+    ],
+    "expected_output": "Функция должна вернуть кортеж `(X_train_scaled, X_test_scaled, y_train, y_test, scaler)`, где scaler обучен только на тренировочной части, а разбиение выполнено стратифицированно.",
+    "input_example": "Пример входа: `X = [[1.0, 10.0], [2.0, 20.0], [3.0, 30.0], [4.0, 40.0], [5.0, 50.0], [6.0, 60.0], [7.0, 70.0], [8.0, 80.0], [9.0, 90.0], [10.0, 100.0]]`, `y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]`.",
+    "output_example": "Пример ожидаемого возврата: `y_train = [1, 0, 0, 0, 1, 1, 1, 0]`, `y_test = [0, 1]`, `scaler.mean_ = [5.125, 51.25]`, `scaler.scale_ = [2.7128168017763383, 27.128168017763382]`, `X_test_scaled = [[-0.4146981098256822, -0.4146981098256823], [1.7970251425779564, 1.7970251425779564]]`.",
+    "requirements": [
+      "Сначала выполнить стратифицированное разбиение данных на train и test.",
+      "Обучить `StandardScaler` только на `X_train`.",
+      "Преобразовать и `X_train`, и `X_test` одним и тем же scaler.",
+      "Вернуть результат в порядке `X_train_scaled, X_test_scaled, y_train, y_test, scaler`."
+    ],
+    "constraints": [
+      "Не менять имя функции `split_and_scale_binary_data`.",
+      "Не изменять входные `X` и `y` на месте.",
+      "Не обучать scaler на всей выборке до split.",
+      "Нельзя хардкодить значения из примеров входа и выхода."
+    ],
+    "broken_code": "from sklearn.model_selection import train_test_split\\nfrom sklearn.preprocessing import StandardScaler\\n\\n\\ndef split_and_scale_binary_data(X, y):\\n    scaler = StandardScaler()\\n    X_scaled = scaler.fit_transform(X)  # ВОТ ТУТ НУЖНО ИСПРАВИТЬ КОД: scaler нельзя обучать на всей выборке до split\\n    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)  # ВОТ ТУТ НУЖНО ИСПРАВИТЬ КОД: нужен stratify=y и split должен быть до масштабирования\\n    return X_train, X_test, y_train, y_test, scaler"
+  }

LICENSE

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NOTICE

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+Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
+
+This repository contains a merged fine-tuned derivative of:
+Qwen/Qwen2.5-Coder-3B-Instruct
+
+Modified files and weights were produced as part of a fine-tuning and merge workflow for ML bugfix task generation.
+
+Built with Qwen.

README.md

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+---
+license: other
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
+tags:
+  - qwen
+  - qwen2.5-coder
+  - transformers
+  - text-generation
+  - code
+  - fine-tuned
+  - russian
+---
+
+# Broken_Code_Generation1.0
+
+`Broken_Code_Generation1.0` - это модель для генерации задач по программированию в стиле ML bugfix.
+
+Если совсем просто: ты задаешь **3 тега** и **сложность**, а модель возвращает **одну готовую задачу** в JSON-формате: с названием, контекстом, тестами, требованиями, ограничениями и сломанным кодом, который нужно исправить.
+
+Модель основана на `Qwen/Qwen2.5-Coder-3B-Instruct`, была дообучена через `QLoRA`, а затем смержена в полноценную модель для инференса и публикации.
+
+Built with Qwen.
+
+## Что делает модель
+
+Модель принимает:
+
+- ровно 3 тега
+- одну сложность: `easy`, `medium` или `hard`
+
+И возвращает:
+
+- один JSON-объект
+- без Markdown
+- без дополнительных пояснений
+- в формате, похожем на обучающий датасет
+
+## Что будет в ответе
+
+На выходе ожидается JSON с такими полями:
+
+- `id`
+- `title`
+- `difficulty`
+- `topic_tags`
+- `task_context`
+- `tests`
+- `expected_output`
+- `input_example`
+- `output_example`
+- `requirements`
+- `constraints`
+- `broken_code`
+
+## Где модель полезна
+
+Эта модель подойдет, если тебе нужно:
+
+- генерировать новые ML bugfix-задачи
+- собирать учебные примеры для студентов
+- делать синтетические данные для обучения и тестирования
+- быстро получать задачи в одном и том же структурированном формате
+- использовать ее вместе с анализом кода
+
+## Основное подключение
+
+Подключение через `transformers` напрямую:
+
+```python
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_path = "Vilyam888/Broken_Code_Generation.1.0"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "left"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else (
+        torch.float16 if torch.cuda.is_available() else torch.float32
+    ),
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+SYSTEM_PROMPT = (
+    "Ты генерируешь новую ML bugfix-задачу строго в формате объектов из датасета. "
+    "Верни только один JSON-объект без Markdown и без пояснений. "
+    "Порядок полей должен быть ровно таким: "
+    "`title`, `difficulty`, `topic_tags`, `task_context`, `tests`, "
+    "`expected_output`, `input_example`, `output_example`, `requirements`, "
+    "`constraints`, `broken_code`. "
+    "`tests`, `requirements` и `constraints` должны быть массивами строк. "
+    "`broken_code` должен быть одной строкой с полным Python-кодом и символами `\\n`. "
+    "Не добавляй лишние поля и не обрывай JSON."
+)
+
+topic_tags = {
+    "TabularData": 0.4,
+    "Statistics": 0.3,
+    "DataPreprocessing": 0.3,
+}
+
+payload = {
+    "difficulty": "medium",
+    "topic_tags": topic_tags,
+}
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {
+        "role": "user",
+        "content": (
+            "Сгенерируй новую ML bugfix-задачу по параметрам.\n"
+            "Формат должен совпадать со структурой датасета: "
+            "все поля обязательны, `tests`/`requirements`/`constraints` - это списки строк, "
+            "`broken_code` - полная строка кода с ошибками и комментариями `ВОТ ТУТ НУЖНО ИСПРАВИТЬ КОД`.\n"
+            "Поля должны идти в порядке: "
+            "title, difficulty, topic_tags, task_context, tests, expected_output, "
+            "input_example, output_example, requirements, constraints, broken_code.\n"
+            + json.dumps(payload, ensure_ascii=False, indent=2)
+        ),
+    },
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+prompt_length = inputs["input_ids"].shape[1]
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=1200,
+        temperature=0.7,
+        top_p=0.95,
+        do_sample=True,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+completion_tokens = output[0][prompt_length:]
+completion = tokenizer.decode(completion_tokens, skip_special_tokens=True).strip()
+print(completion)
+```
+
+После этого модели нужно передать:
+
+- 3 тега
+- сложность `easy`, `medium` или `hard`
+- промпт с просьбой вернуть один JSON-объект
+
+Для этой модели это важно: она обучена не на обычный разговорный чат, а на генерацию задач.
+
+Поэтому хороший запрос для нее выглядит так:
+
+- "Сгенерируй ML bugfix-задачу по таким тегам и такой сложности"
+
+А вот запросы вроде:
+
+- `Who are you?`
+- `Hello`
+- `Tell me a joke`
+
+для этой модели не являются целевым сценарием и обычно не дают полезного результата.
+
+Если нужен более простой запуск именно внутри этого проекта, ниже есть второй вариант через готовый скрипт.
+
+Если говорить совсем коротко: для обычного подключения другим людям достаточно `transformers`, `torch` и имени репозитория:
+
+- `Vilyam888/Broken_Code_Generation.1.0`
+
+## Основной инференс в проекте
+
+Самый простой и понятный способ запуска в этом проекте:
+
+```powershell
+.\.venv\Scripts\python.exe .\HF_Release\infer_merged_model.py --tag1 TabularData --tag2 Statistics --tag3 DataPreprocessing --difficulty medium
+```
+
+Что произойдет после запуска:
+
+- загрузится смерженная модель
+- в модель передадутся 3 тега и сложность
+- модель сгенерирует задачу
+- готовый JSON сохранится в `HF_Release/inference_output/generated_task.json`
+- сырой текст ответа сохранится в `HF_Release/inference_output/raw_output.txt`
+
+Еще один пример:
+
+```powershell
+.\.venv\Scripts\python.exe .\HF_Release\infer_merged_model.py --tag1 Classification --tag2 Evaluation --tag3 Metrics --difficulty hard
+```
+
+## Что можно менять
+
+В основной команде ты обычно меняешь только это:
+
+- `--tag1`, `--tag2`, `--tag3` - любые 3 нужных тега
+- `--difficulty` - `easy`, `medium` или `hard`
+
+Например, если хочешь другую генерацию, просто подставляешь другие значения в ту же команду.
+
+## Как это работает
+
+Внутри все довольно просто:
+
+1. из трех тегов собирается `topic_tags`
+2. в промпт подставляются теги и сложность
+3. модель генерирует текст
+4. из текста извлекается JSON
+5. JSON сохраняется в итоговый файл
+
+То есть в обычной работе тебе не нужно менять код модели. Достаточно менять входные теги и сложность.
+
+## Совместимость с Code Analyze
+
+Эта модель хорошо работает в связке с [`Vilyam888/Code_analyze.1.0`](https://huggingface.co/Vilyam888/Code_analyze.1.0).
+
+Удобный сценарий такой:
+
+1. `Code_analyze.1.0` анализирует код и определяет тип ошибки
+2. по этому анализу выбираются подходящие теги
+3. `Broken_Code_Generation1.0` генерирует новую bugfix-задачу в нужном формате
+
+Это удобно для:
+
+- учебных пайплайнов
+- генерации новых примеров
+- полуавтоматической подготовки задач
+- систем, где сначала анализируется решение, а потом создается похожая задача на закрепление
+
+## Как лучше формулировать запрос
+
+Модель обычно отвечает лучше, если:
+
+- давать ровно 3 тега
+- явно указывать сложность
+- просить вернуть ровно один JSON-объект
+- отдельно писать, что не нужно добавлять Markdown и пояснения
+
+## Ограничения
+
+Важно помнить:
+
+- модель все еще может иногда выдавать неполный JSON
+- качество зависит от промпта и параметров генерации
+- иногда ответы могут быть стилистически похожими друг на друга
+- генерации лучше просматривать вручную перед использованием в важном датасете или продукте
+
+## Кратко об обучении
+
+- Базовая модель: `Qwen/Qwen2.5-Coder-3B-Instruct`
+- Метод дообучения: `QLoRA`
+- Итоговая версия: merged-модель после вливания LoRA-адаптера в базовую
+- Целевая задача: генерация структурированных ML bugfix-задач
+
+## Что лежит в репозитории
+
+Главные файлы:
+
+- шарды модели: `model-00001-of-00004.safetensors` ... `model-00004-of-00004.safetensors`
+- файлы токенизатора
+- `chat_template.jinja`
+- `config.json`
+- `generation_config.json`
+
+## Лицензия
+
+Этот репозиторий является производной работой от `Qwen/Qwen2.5-Coder-3B-Instruct`.
+
+Базовая модель распространяется по лицензии `Qwen RESEARCH LICENSE AGREEMENT`. На Hugging Face для этой модели используется `license: other`.
+
+Важно:
+
+- лицензия Qwen ориентирована на research / non-commercial использование
+- для коммерческого использования нужно отдельно проверить условия исходной лицензии
+- при распространении нужно сохранять `LICENSE` и `NOTICE`
+
+## Атрибуция
+
+Improved using Qwen.
+

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,69 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.4"
+}

metrics/01_training_perplexity.json

@@ -0,0 +1,39 @@
+{
+  "metric_group": "training_perplexity",
+  "model": "Broken_Code_Generation.1.0",
+  "hf_model": "Vilyam888/Broken_Code_Generation.1.0",
+  "base_model": "Qwen/Qwen2.5-Coder-3B-Instruct",
+  "adapter_dir": "outputs/qwen25-coder-3b-qlora",
+  "checkpoint": "outputs/qwen25-coder-3b-qlora/checkpoint-501",
+  "source": "outputs/qwen25-coder-3b-qlora/checkpoint-501/trainer_state.json",
+  "validation_file": "prepared_data/val.json",
+  "evaluation_date": "2026-06-11",
+  "metrics": {
+    "train_loss_final": 0.1867,
+    "eval_loss_final": 0.2523,
+    "eval_mean_token_accuracy": 0.9323,
+    "perplexity_validation": 1.29,
+    "num_train_epochs": 3,
+    "global_step": 501,
+    "eval_by_epoch": [
+      {
+        "epoch": 1.0,
+        "eval_loss": 0.2812,
+        "eval_mean_token_accuracy": 0.9243,
+        "perplexity": 1.3247
+      },
+      {
+        "epoch": 2.0,
+        "eval_loss": 0.2512,
+        "eval_mean_token_accuracy": 0.9317,
+        "perplexity": 1.2856
+      },
+      {
+        "epoch": 3.0,
+        "eval_loss": 0.2523,
+        "eval_mean_token_accuracy": 0.9323,
+        "perplexity": 1.2869
+      }
+    ]
+  }
+}

metrics/02_json_validity.json

@@ -0,0 +1,27 @@
+{
+  "metric_group": "json_validity",
+  "model": "Broken_Code_Generation.1.0",
+  "hf_model": "Vilyam888/Broken_Code_Generation.1.0",
+  "adapter_dir": "outputs/qwen25-coder-3b-qlora",
+  "evaluation_file": "prepared_data/test.json",
+  "evaluation_date": "2026-06-11",
+  "samples_evaluated": 100,
+  "generation_params": {
+    "temperature": 0.2,
+    "top_p": 0.95,
+    "max_new_tokens": 1200,
+    "seed": 42
+  },
+  "metrics": {
+    "valid_json_rate": 0.94,
+    "required_fields_rate": 0.92,
+    "difficulty_match_rate": 0.96,
+    "topic_tag_key_match_rate": 0.97
+  },
+  "metrics_counts": {
+    "valid_json": 94,
+    "required_fields_complete": 92,
+    "difficulty_match": 96,
+    "topic_tag_keys_match": 97
+  }
+}

metrics/03_bleu_rouge.json

@@ -0,0 +1,29 @@
+{
+  "metric_group": "bleu_rouge",
+  "model": "Broken_Code_Generation.1.0",
+  "hf_model": "Vilyam888/Broken_Code_Generation.1.0",
+  "evaluation_file": "prepared_data/test.json",
+  "evaluation_date": "2026-06-11",
+  "text_fields": [
+    "title",
+    "task_context",
+    "expected_output",
+    "input_example",
+    "output_example"
+  ],
+  "pairs_evaluated": 94,
+  "generation_params": {
+    "temperature": 0.2,
+    "top_p": 0.95,
+    "max_new_tokens": 1200,
+    "seed": 42
+  },
+  "metrics": {
+    "bleu4_corpus": 0.68,
+    "bleu4_title": 0.74,
+    "bleu4_task_context": 0.66,
+    "rouge1_f1": 0.73,
+    "rouge2_f1": 0.58,
+    "rougeL_f1": 0.71
+  }
+}

metrics/04_code_metrics.json

@@ -0,0 +1,19 @@
+{
+  "metric_group": "code_metrics",
+  "model": "Broken_Code_Generation.1.0",
+  "hf_model": "Vilyam888/Broken_Code_Generation.1.0",
+  "evaluation_file": "prepared_data/test.json",
+  "evaluation_date": "2026-06-11",
+  "samples_evaluated": 100,
+  "generation_params": {
+    "temperature": 0.2,
+    "top_p": 0.95,
+    "max_new_tokens": 1200,
+    "seed": 42
+  },
+  "metrics": {
+    "broken_code_syntax_valid_rate": 0.91,
+    "code_token_f1_broken_code": 0.47,
+    "codebleu_broken_code": 0.47
+  }
+}

metrics/README.md

@@ -0,0 +1,36 @@
+# Метрики оценки Broken_Code_Generation.1.0
+
+Автоматическая оценка дообученной модели [Vilyam888/Broken_Code_Generation.1.0](https://huggingface.co/Vilyam888/Broken_Code_Generation.1.0) на hold-out выборке.
+
+## Протокол оценки
+
+| Параметр | Значение |
+|----------|----------|
+| Базовая модель | Qwen/Qwen2.5-Coder-3B-Instruct |
+| Метод дообучения | QLoRA (4-bit NF4), 3 эпохи, checkpoint-501 |
+| Тестовая выборка | `prepared_data/test.json`, N = 100 |
+| Reference | Поля JSON из test split |
+| Temperature | 0.2 |
+| max_new_tokens | 1200 |
+
+## Итоговые метрики (QLoRA)
+
+| Метрика | Значение | Baseline |
+|---------|----------|----------|
+| Perplexity (validation) | **1.29** | — |
+| valid_json_rate | **94 %** | 78 % |
+| required_fields_rate | **92 %** | 74 % |
+| BLEU-4 (corpus) | **0.68** | 0.52 |
+| ROUGE-L F1 | **0.71** | 0.54 |
+| CodeBLEU (broken_code) | **0.47** | — |
+| Синтаксис broken_code (AST) | **91 %** | — |
+
+## Файлы
+
+- `evaluation_report.json` / `evaluation_report.txt` — сводный отчёт с сравнением baseline vs QLoRA
+- `01_training_perplexity.json` — метрики обучения (loss, PPL по эпохам)
+- `02_json_validity.json` — валидность и полнота JSON
+- `03_bleu_rouge.json` — BLEU и ROUGE
+- `04_code_metrics.json` — CodeBLEU и синтаксис `broken_code`
+
+Human Evaluation в протокол оценки **не входит**.

metrics/evaluation_report.json

@@ -0,0 +1,108 @@
+{
+  "title": "Отчёт об оценке модели Broken_Code_Generation.1.0",
+  "model": "Broken_Code_Generation.1.0",
+  "hf_model": "Vilyam888/Broken_Code_Generation.1.0",
+  "base_model": "Qwen/Qwen2.5-Coder-3B-Instruct",
+  "evaluation_date": "2026-06-11",
+  "evaluation_sample": "test.json, N = 100",
+  "reference_split": "hold-out test",
+  "generation": {
+    "temperature": 0.2,
+    "max_new_tokens": 1200
+  },
+  "training": {
+    "train_loss_final": 0.1867,
+    "eval_loss_final": 0.2523,
+    "eval_mean_token_accuracy": 0.9323,
+    "perplexity_validation": 1.29,
+    "num_train_epochs": 3,
+    "global_step": 501,
+    "eval_by_epoch": [
+      {
+        "epoch": 1.0,
+        "eval_loss": 0.2812,
+        "eval_mean_token_accuracy": 0.9243,
+        "perplexity": 1.3247
+      },
+      {
+        "epoch": 2.0,
+        "eval_loss": 0.2512,
+        "eval_mean_token_accuracy": 0.9317,
+        "perplexity": 1.2856
+      },
+      {
+        "epoch": 3.0,
+        "eval_loss": 0.2523,
+        "eval_mean_token_accuracy": 0.9323,
+        "perplexity": 1.2869
+      }
+    ]
+  },
+  "finetuned_metrics": {
+    "valid_json_rate": 0.94,
+    "required_fields_rate": 0.92,
+    "difficulty_match_rate": 0.96,
+    "topic_tag_key_match_rate": 0.97,
+    "bleu4_corpus": 0.68,
+    "bleu4_title": 0.74,
+    "bleu4_task_context": 0.66,
+    "rouge1_f1": 0.73,
+    "rouge2_f1": 0.58,
+    "rougeL_f1": 0.71,
+    "broken_code_syntax_valid_rate": 0.91,
+    "code_token_f1_broken_code": 0.47,
+    "codebleu_broken_code": 0.47
+  },
+  "baseline_metrics": {
+    "valid_json_rate": 0.78,
+    "required_fields_rate": 0.74,
+    "difficulty_match_rate": 0.85,
+    "topic_tag_key_match_rate": 0.83,
+    "bleu4_corpus": 0.52,
+    "rouge1_f1": 0.57,
+    "rouge2_f1": 0.41,
+    "rougeL_f1": 0.54
+  },
+  "baseline_vs_finetuned": {
+    "bleu4_corpus": {
+      "baseline": 0.52,
+      "finetuned": 0.68,
+      "delta": 0.16
+    },
+    "difficulty_match_rate": {
+      "baseline": 0.85,
+      "finetuned": 0.96,
+      "delta": 0.11
+    },
+    "required_fields_rate": {
+      "baseline": 0.74,
+      "finetuned": 0.92,
+      "delta": 0.18
+    },
+    "rouge1_f1": {
+      "baseline": 0.57,
+      "finetuned": 0.73,
+      "delta": 0.16
+    },
+    "rouge2_f1": {
+      "baseline": 0.41,
+      "finetuned": 0.58,
+      "delta": 0.17
+    },
+    "rougeL_f1": {
+      "baseline": 0.54,
+      "finetuned": 0.71,
+      "delta": 0.17
+    },
+    "topic_tag_key_match_rate": {
+      "baseline": 0.83,
+      "finetuned": 0.97,
+      "delta": 0.14
+    },
+    "valid_json_rate": {
+      "baseline": 0.78,
+      "finetuned": 0.94,
+      "delta": 0.16
+    }
+  }
+}

metrics/evaluation_report.txt

@@ -0,0 +1,35 @@
+ОТЧЁТ ОБ ОЦЕНКЕ МОДЕЛИ Broken_Code_Generation.1.0
+Репозиторий: Vilyam888/Broken_Code_Generation.1.0
+Базовая модель: Qwen/Qwen2.5-Coder-3B-Instruct
+Дата оценки: 2026-06-11
+Выборка: test.json, N = 100 (hold-out test)
+Генерация: temperature = 0.2, max_new_tokens = 1200
+
+1. Perplexity (validation, checkpoint-501):
+  • train_loss_final: 0.1867
+  • eval_loss_final: 0.2523
+  • eval_mean_token_accuracy: 0.9323
+  • perplexity_validation: 1.29
+  • num_train_epochs: 3
+  • global_step: 501
+
+  По эпохам:
+    epoch 1.0: PPL=1.3247, eval_loss=0.2812, acc=0.9243
+    epoch 2.0: PPL=1.2856, eval_loss=0.2512, acc=0.9317
+    epoch 3.0: PPL=1.2869, eval_loss=0.2523, acc=0.9323
+
+2. JSON validity (QLoRA vs baseline):
+  • valid_json_rate: 0.94 (baseline 0.78, Δ 0.16)
+  • required_fields_rate: 0.92 (baseline 0.74, Δ 0.18)
+  • difficulty_match_rate: 0.96 (baseline 0.85, Δ 0.11)
+  • topic_tag_key_match_rate: 0.97 (baseline 0.83, Δ 0.14)
+
+3. BLEU / ROUGE (QLoRA vs baseline):
+  • bleu4_corpus: 0.68 (baseline 0.52, Δ 0.16)
+  • rouge1_f1: 0.73 (baseline 0.57, Δ 0.16)
+  • rouge2_f1: 0.58 (baseline 0.41, Δ 0.17)
+  • rougeL_f1: 0.71 (baseline 0.54, Δ 0.17)
+
+4. Code metrics (поле broken_code):
+  • broken_code_syntax_valid_rate: 0.91
+  • codebleu_broken_code: 0.47

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