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языковых моделей (LLM). Созданный сообществом EleutherAI, он стандартизирует процесс тестирования LLM на различных задачах, метриках и наборах данных. В корпоративном LLMOps он служит основой для выбора моделей, валидации дообучения и непрерывного мониторинга.\u003C\u002Fp>\n\u003Cp>Основные преимущества включают:\n- **Последовательность**: Единые протоколы оценки для различных моделей и задач.\n- **Масштабируемость**: Эффективная обработка огромных наборов данных и нескольких моделей.\n- **Расширяемость**: Поддержка пользовательских задач, наборов данных и метрик.\n- **Воспроизводимость**: Детерминированные результаты благодаря фиксированным значениям случайности и кэшированию.\u003C\u002Fp>\n\u003Cp>Идеально подходит для команд, переходящих от разового тестирования к оценке LLM промышленного уровня.\u003C\u002Fp>\n\u003Cp>## Предварительные условия и установка\u003C\u002Fp>\n\u003Cp>Перед началом работы убедитесь, что ваша среда соответствует следующим требованиям:\n- Python 3.10+.\n- Ускорение GPU\u002FTPU (рекомендуется для больших моделей).\n- Достаточное количество оперативной памяти (16 ГБ+ для моделей среднего размера).\u003C\u002Fp>\n\u003Cp>### Step-by-Step Installation\n1. Clone the repository: ```bash\ngit clone https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness\ngit checkout main\n```\u003C\u002Fp>\n\u003Cp>2. Install dependencies: ```bash\npip install -e .\npip install torch transformers datasets\n```\u003C\u002Fp>\n\u003Cp>3. For specific tasks (e.g., vision-language models): ```bash\npip install timm pillow\n```\u003C\u002Fp>\n\u003Cp>4. Verify installation: ```bash\nlm_eval --help\n```\u003C\u002Fp>\n\u003Cp>Совет: используйте виртуальное окружение, такое как `venv` или `conda`, чтобы изолировать зависимости.\u003C\u002Fp>\n\u003Cp>## Основные концепции\u003C\u002Fp>\n\u003Cp>### Задачи и наборы данных\nEvaluation Harness поддерживает более 200 задач «из коробки», разделенных на категории:\n- **Классификация**: ARC, BoolQ, HellaSwag.\n- **Генеративные**: AlpacaEval, MT-Bench.\n- **Рассуждение**: GSM8K, MATH.\n- **Мультимодальные**: MMMU, MathVista.\u003C\u002Fp>\n\u003Cp>Датасеты автоматически загружаются из Hugging Face Hub.\u003C\u002Fp>\n\u003Cp>### Метрики\nОбщие метрики включают:\n- **Accuracy**: Точное совпадение для классификации.\n- **F1**: Сбалансированная точность\u002Fполнота.\n- **Perplexity**: Для беглости генерации.\n- **BLEU\u002FROUGE**: Перевод и суммаризация.\u003C\u002Fp>\n\u003Cp>Пользовательские метрики через флаг `--metric`.\u003C\u002Fp>\n\u003Cp>### Загрузка моделей\nПоддерживает HF Transformers, Llama.cpp, vLLM и другие:\n- Hugging Face: `meta-llama\u002FLlama-2-7b-chat-hf`\n- Локальные: Пользовательские пути с квантованием (например, 4-битным).\u003C\u002Fp>\n\u003Cp>## Запуск базовых оценок\u003C\u002Fp>\n\u003Cp>### Command-Line Interface (CLI)\nStart with a simple benchmark:\n```bash\nlm_eval --model hf --model_args pretrained=model_name,trust_remote_code=True --tasks hellaswag,arc_easy --device cuda:0 --batch_size auto\n```\u003C\u002Fp>\n\u003Cp>Разбор:\n- `--model hf`: загрузчик Hugging Face.\n- `--tasks`: задачи, разделенные запятыми.\n- `--batch_size auto`: оптимизирует под оборудование.\u003C\u002Fp>\n\u003Cp>### Интерпретация результатов\nВывод включает:\n- **acc**: Показатель точности (Accuracy).\n- **acc_stderr**: Стандартная ошибка.\n- JSON, совместимый с таблицей лидеров.\u003C\u002Fp>\n\u003Cp>Example output:\n```\nhellaswag: acc=0.9123 (±0.0012)\narc_easy: acc=0.7845 (±0.0021)\n```\u003C\u002Fp>\n\u003Cp>## Расширенное использование\u003C\u002Fp>\n\u003Cp>### Multi-Model Leaderboards\nCompare models:\n```bash\nlm_eval --model hf --model_args pretrained=model1 --tasks all --limit 1000\nlm_eval --model hf --model_args pretrained=model2 --tasks all --limit 1000\n```\nAggregate with `--save_jsonl` and external tools.\u003C\u002Fp>\n\u003Cp>### Пользовательские задачи\n1. Определите задачу в `lm_eval\u002Ftasks\u002F`: - YAML-конфигурация для набора данных. - Python-процессор для few-shot промптинга.\u003C\u002Fp>\n\u003Cp>2. Example custom task YAML: ```yaml\ntask: my_custom_task\ndataset_path: huggingface\ndataset_name: my_dataset\ntraining_split: train\nfewshot_split: validation\nmetric_list: - metric: acc aggregation: mean higher_is_better: true\n```\u003C\u002Fp>\n\u003Cp>3. Запуск: `lm_eval --tasks my_custom_task`\u003C\u002Fp>\n\u003Cp>### Few-Shot и Chain-of-Thought Prompting\n- `--num_fewshot 5`: Примеры в контексте.\n- Пользовательские шаблоны через `--gen_kwargs temperature=0.7`.\u003C\u002Fp>\n\u003Cp>Для CoT: Используйте задачи вроде `gsm8k_cot`.\u003C\u002Fp>\n\u003Cp>## Оптимизация и лучшие практики\u003C\u002Fp>\n\u003Cp>### Настройка производительности\n- **Batching**: `--batch_size 32` или `auto`.\n- **Квантование**: `--model_args dtype=bfloat16,load_in_4bit=True`.\n- **Распределенные вычисления**: `--multi_gpu` для интеграции с Ray.\u003C\u002Fp>\n\u003Cp>### Экономическая эффективность\n- Ограничение выборки: `--limit 500`.\n- Использование меньших подмножеств: `--subsample 0.1`.\n- Кэширование результатов: `--cache_dir \u002Fpath\u002Fto\u002Fcache`.\u003C\u002Fp>\n\u003Cp>### Советы по надежности\n- Запуск с несколькими сидами: `--num_generations 8`.\n- Бутстреп доверительных интервалов.\n- Логирование всего с помощью `--log_samples`.\u003C\u002Fp>\n\u003Cp>## Интеграция в пайплайны LLMOps\u003C\u002Fp>\n\u003Cp>Embed in CI\u002FCD:\n1. GitHub Actions YAML: ```yaml - name: Evaluate Model run: lm_eval --model hf --model_args pretrained=${{ inputs.model }} --tasks core --batch_size auto > results.json ```\u003C\u002Fp>\n\u003Cp>2. MLflow tracking: ```python\nimport mlflow\nmlflow.log_metrics(results)\n```\u003C\u002Fp>\n\u003Cp>3. Prometheus\u002FGrafana для дашбордов.\u003C\u002Fp>\n\u003Cp>## Устранение распространенных проблем\u003C\u002Fp>\n\u003Cp>- **Ошибки OOM**: Уменьшите размер пакета или используйте градиентное чекпоинтирование.\n- **CUDA Out of Memory**: Включите `torch.backends.cuda.enable_flash_sdp(True)`.\n- **Медленный инференс**: Переключитесь на загрузчик vLLM: `--model vllm`.\n- **Набор данных не найден**: Проверьте токен доступа HF.\u003C\u002Fp>\n\u003Cp>## Заключение и следующие шаги\u003C\u002Fp>\n\u003Cp>Evaluation Harness превращает субъективную оценку LLM в процесс, основанный на данных. Начните с основных задач, переходите к пользовательским оценкам и интегрируйте их в свой рабочий процесс LLMOps.\u003C\u002Fp>\n\u003Cp>Ресурсы:\n- GitHub: [EleutherAI\u002Flm-evaluation-harness](https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness)\n- Таблица лидеров: [Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fopen-llm-leaderboard\u002Fopen_llm_leaderboard)\n- Discord: сообщество EleutherAI.\u003C\u002Fp>\n\u003Cp>Начните экспериментировать сегодня, чтобы получить точные сведения о моделях.\u003C\u002Fp>",{"time":228,"blocks":229,"version":363},1775468954690,[230,234,237,240,243,246,249,252,255,258,261,264,267,270,273,276,279,282,285,288,291,294,297,300,303,306,309,312,315,318,321,324,327,330,333,336,339,342,345,348,351,354,357,360],{"data":231,"type":233},{"text":232},"# Руководство по Evaluation Harness","paragraph",{"data":235,"type":233},{"text":236},"## Введение в Evaluation Harness",{"data":238,"type":233},{"text":239},"Evaluation Harness — это мощный фреймворк с открытым исходным кодом, разработанный специально для оценки больших языковых моделей (LLM). Созданный сообществом EleutherAI, он стандартизирует процесс тестирования LLM на различных задачах, метриках и наборах данных. В корпоративном LLMOps он служит основой для выбора моделей, валидации дообучения и непрерывного мониторинга.",{"data":241,"type":233},{"text":242},"Основные преимущества включают:\n- **Последовательность**: Единые протоколы оценки для различных моделей и задач.\n- **Масштабируемость**: Эффективная обработка огромных наборов данных и нескольких моделей.\n- **Расширяемость**: Поддержка пользовательских задач, наборов данных и метрик.\n- **Воспроизводимость**: Детерминированные результаты благодаря фиксированным значениям случайности и кэшированию.",{"data":244,"type":233},{"text":245},"Идеально подходит для команд, переходящих от разового тестирования к оценке LLM промышленного уровня.",{"data":247,"type":233},{"text":248},"## Предварительные условия и установка",{"data":250,"type":233},{"text":251},"Перед началом работы убедитесь, что ваша среда соответствует следующим требованиям:\n- Python 3.10+.\n- Ускорение GPU\u002FTPU (рекомендуется для больших моделей).\n- Достаточное количество оперативной памяти (16 ГБ+ для моделей среднего размера).",{"data":253,"type":233},{"text":254},"### Step-by-Step Installation\n1. Clone the repository: ```bash\ngit clone https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness\ngit checkout main\n```",{"data":256,"type":233},{"text":257},"2. Install dependencies: ```bash\npip install -e .\npip install torch transformers datasets\n```",{"data":259,"type":233},{"text":260},"3. For specific tasks (e.g., vision-language models): ```bash\npip install timm pillow\n```",{"data":262,"type":233},{"text":263},"4. Verify installation: ```bash\nlm_eval --help\n```",{"data":265,"type":233},{"text":266},"Совет: используйте виртуальное окружение, такое как `venv` или `conda`, чтобы изолировать зависимости.",{"data":268,"type":233},{"text":269},"## Основные концепции",{"data":271,"type":233},{"text":272},"### Задачи и наборы данных\nEvaluation Harness поддерживает более 200 задач «из коробки», разделенных на категории:\n- **Классификация**: ARC, BoolQ, HellaSwag.\n- **Генеративные**: AlpacaEval, MT-Bench.\n- **Рассуждение**: GSM8K, MATH.\n- **Мультимодальные**: MMMU, MathVista.",{"data":274,"type":233},{"text":275},"Датасеты автоматически загружаются из Hugging Face Hub.",{"data":277,"type":233},{"text":278},"### Метрики\nОбщие метрики включают:\n- **Accuracy**: Точное совпадение для классификации.\n- **F1**: Сбалансированная точность\u002Fполнота.\n- **Perplexity**: Для беглости генерации.\n- **BLEU\u002FROUGE**: Перевод и суммаризация.",{"data":280,"type":233},{"text":281},"Пользовательские метрики через флаг `--metric`.",{"data":283,"type":233},{"text":284},"### Загрузка моделей\nПоддерживает HF Transformers, Llama.cpp, vLLM и другие:\n- Hugging Face: `meta-llama\u002FLlama-2-7b-chat-hf`\n- Локальные: Пользовательские пути с квантованием (например, 4-битным).",{"data":286,"type":233},{"text":287},"## Запуск базовых оценок",{"data":289,"type":233},{"text":290},"### Command-Line Interface (CLI)\nStart with a simple benchmark:\n```bash\nlm_eval --model hf --model_args pretrained=model_name,trust_remote_code=True --tasks hellaswag,arc_easy --device cuda:0 --batch_size auto\n```",{"data":292,"type":233},{"text":293},"Разбор:\n- `--model hf`: загрузчик Hugging Face.\n- `--tasks`: задачи, разделенные запятыми.\n- `--batch_size auto`: оптимизирует под оборудование.",{"data":295,"type":233},{"text":296},"### Интерпретация результатов\nВывод включает:\n- **acc**: Показатель точности (Accuracy).\n- **acc_stderr**: Стандартная ошибка.\n- JSON, совместимый с таблицей лидеров.",{"data":298,"type":233},{"text":299},"Example output:\n```\nhellaswag: acc=0.9123 (±0.0012)\narc_easy: acc=0.7845 (±0.0021)\n```",{"data":301,"type":233},{"text":302},"## Расширенное использование",{"data":304,"type":233},{"text":305},"### Multi-Model Leaderboards\nCompare models:\n```bash\nlm_eval --model hf --model_args pretrained=model1 --tasks all --limit 1000\nlm_eval --model hf --model_args pretrained=model2 --tasks all --limit 1000\n```\nAggregate with `--save_jsonl` and external tools.",{"data":307,"type":233},{"text":308},"### Пользовательские задачи\n1. Определите задачу в `lm_eval\u002Ftasks\u002F`: - YAML-конфигурация для набора данных. - Python-процессор для few-shot промптинга.",{"data":310,"type":233},{"text":311},"2. Example custom task YAML: ```yaml\ntask: my_custom_task\ndataset_path: huggingface\ndataset_name: my_dataset\ntraining_split: train\nfewshot_split: validation\nmetric_list: - metric: acc aggregation: mean higher_is_better: true\n```",{"data":313,"type":233},{"text":314},"3. Запуск: `lm_eval --tasks my_custom_task`",{"data":316,"type":233},{"text":317},"### Few-Shot и Chain-of-Thought Prompting\n- `--num_fewshot 5`: Примеры в контексте.\n- Пользовательские шаблоны через `--gen_kwargs temperature=0.7`.",{"data":319,"type":233},{"text":320},"Для CoT: Используйте задачи вроде `gsm8k_cot`.",{"data":322,"type":233},{"text":323},"## Оптимизация и лучшие практики",{"data":325,"type":233},{"text":326},"### Настройка производительности\n- **Batching**: `--batch_size 32` или `auto`.\n- **Квантование**: `--model_args dtype=bfloat16,load_in_4bit=True`.\n- **Распределенные вычисления**: `--multi_gpu` для интеграции с Ray.",{"data":328,"type":233},{"text":329},"### Экономическая эффективность\n- Ограничение выборки: `--limit 500`.\n- Использование меньших подмножеств: `--subsample 0.1`.\n- Кэширование результатов: `--cache_dir \u002Fpath\u002Fto\u002Fcache`.",{"data":331,"type":233},{"text":332},"### Советы по надежности\n- Запуск с несколькими сидами: `--num_generations 8`.\n- Бутстреп доверительных интервалов.\n- Логирование всего с помощью `--log_samples`.",{"data":334,"type":233},{"text":335},"## Интеграция в пайплайны LLMOps",{"data":337,"type":233},{"text":338},"Embed in CI\u002FCD:\n1. GitHub Actions YAML: ```yaml - name: Evaluate Model run: lm_eval --model hf --model_args pretrained=${{ inputs.model }} --tasks core --batch_size auto > results.json ```",{"data":340,"type":233},{"text":341},"2. MLflow tracking: ```python\nimport mlflow\nmlflow.log_metrics(results)\n```",{"data":343,"type":233},{"text":344},"3. Prometheus\u002FGrafana для дашбордов.",{"data":346,"type":233},{"text":347},"## Устранение распространенных проблем",{"data":349,"type":233},{"text":350},"- **Ошибки OOM**: Уменьшите размер пакета или используйте градиентное чекпоинтирование.\n- **CUDA Out of Memory**: Включите `torch.backends.cuda.enable_flash_sdp(True)`.\n- **Медленный инференс**: Переключитесь на загрузчик vLLM: `--model vllm`.\n- **Набор данных не найден**: Проверьте токен доступа HF.",{"data":352,"type":233},{"text":353},"## Заключение и следующие шаги",{"data":355,"type":233},{"text":356},"Evaluation Harness превращает субъективную оценку LLM в процесс, основанный на данных. Начните с основных задач, переходите к пользовательским оценкам и интегрируйте их в свой рабочий процесс LLMOps.",{"data":358,"type":233},{"text":359},"Ресурсы:\n- GitHub: [EleutherAI\u002Flm-evaluation-harness](https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness)\n- Таблица лидеров: [Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fopen-llm-leaderboard\u002Fopen_llm_leaderboard)\n- Discord: сообщество EleutherAI.",{"data":361,"type":233},{"text":362},"Начните экспериментировать сегодня, чтобы получить точные сведения о моделях.","2.31","Это руководство содержит подробный обзор Evaluation Harness — важного фреймворка для строгой оценки возможностей больших языковых моделей (LLM) в корпоративных конвейерах LLMOps. Узнайте о настройке, лучших практиках и продвинутых методах для обеспечения надежного бенчмаркинга и оптимизации моделей.","\u002Fuploads\u002F2026\u002F04\u002Fevaluation-harness-1775466944495-4s0xv2.webp","evaluation-harness-1775466944495-4s0xv2","PUBLISHED","2026-03-01T17:50:00.000Z","2026-03-01T18:50:33.351Z","2026-04-06T09:49:14.730Z",{"en":372,"de":373,"sr":374,"es":375,"fr":376,"it":377,"ru":378,"zh":379},"\u002Fblog\u002Fevaluation-harness","\u002Fde\u002Fblog\u002Fevaluation-harness","\u002Fsr\u002Fblog\u002Fevaluation-harness","\u002Fes\u002Fblog\u002Fevaluation-harness","\u002Ffr\u002Fblog\u002Fevaluation-harness","\u002Fit\u002Fblog\u002Fevaluation-harness","\u002Fru\u002Fblog\u002Fevaluation-harness","\u002Fzh\u002Fblog\u002Fevaluation-harness",[381],{"id":382,"name":383,"slug":225},89,"Стенд оценки",{"id":385,"login":386,"email":387,"displayName":388},"20","rooth8233","","Aleksandar Stajić",[390,520],{"lang":391,"title":392,"content":393,"contentJson":394,"excerpt":519},"en","Comprehensive Guide to Evaluation Harness: Mastering LLM Performance Evaluation","{\"time\":1772391033350,\"blocks\":[{\"data\":{\"text\":\"# Evaluation Harness Guide\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Introduction to Evaluation Harness\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Evaluation Harness is a powerful, open-source framework designed specifically for evaluating large language models (LLMs). Developed by the EleutherAI community, it standardizes the process of benchmarking LLMs across diverse tasks, metrics, and datasets. In enterprise LLMOps, it serves as a cornerstone for model selection, fine-tuning validation, and continuous monitoring.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Key benefits include:\\n- **Consistency**: Uniform evaluation protocols across models and tasks.\\n- **Scalability**: Handles massive datasets and multiple models efficiently.\\n- **Extensibility**: Supports custom tasks, datasets, and metrics.\\n- **Reproducibility**: Deterministic results with seeded randomness and caching.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Ideal for teams transitioning from ad-hoc testing to production-grade LLM evaluation.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Prerequisites and Installation\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Before diving in, ensure your environment meets these requirements:\\n- Python 3.10+.\\n- GPU\u002FTPU acceleration (recommended for large models).\\n- Sufficient RAM (16GB+ for mid-sized models).\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Step-by-Step Installation\\n1. Clone the repository: ```bash\\ngit clone https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness\\ngit checkout main\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"2. Install dependencies: ```bash\\npip install -e .\\npip install torch transformers datasets\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"3. For specific tasks (e.g., vision-language models): ```bash\\npip install timm pillow\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"4. Verify installation: ```bash\\nlm_eval --help\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Pro tip: Use a virtual environment like `venv` or `conda` to isolate dependencies.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Core Concepts\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Tasks and Datasets\\nEvaluation Harness supports 200+ tasks out-of-the-box, categorized as:\\n- **Classification**: ARC, BoolQ, HellaSwag.\\n- **Generative**: AlpacaEval, MT-Bench.\\n- **Reasoning**: GSM8K, MATH.\\n- **Multimodal**: MMMU, MathVista.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Datasets auto-download from Hugging Face Hub.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Metrics\\nCommon metrics include:\\n- **Accuracy**: Exact match for classification.\\n- **F1**: Balanced precision\u002Frecall.\\n- **Perplexity**: For generative fluency.\\n- **BLEU\u002FROUGE**: Translation and summarization.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Custom metrics via `--metric` flag.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Model Loading\\nSupports HF Transformers, Llama.cpp, vLLM, and more:\\n- Hugging Face: `meta-llama\u002FLlama-2-7b-chat-hf`\\n- Local: Custom paths with quantization (e.g., 4-bit).\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Running Basic Evaluations\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Command-Line Interface (CLI)\\nStart with a simple benchmark:\\n```bash\\nlm_eval --model hf --model_args pretrained=model_name,trust_remote_code=True --tasks hellaswag,arc_easy --device cuda:0 --batch_size auto\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Breakdown:\\n- `--model hf`: Hugging Face loader.\\n- `--tasks`: Comma-separated tasks.\\n- `--batch_size auto`: Optimizes for hardware.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Interpreting Results\\nOutput includes:\\n- **acc**: Accuracy score.\\n- **acc_stderr**: Standard error.\\n- Leaderboard-compatible JSON.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Example output:\\n```\\nhellaswag: acc=0.9123 (±0.0012)\\narc_easy: acc=0.7845 (±0.0021)\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Advanced Usage\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Multi-Model Leaderboards\\nCompare models:\\n```bash\\nlm_eval --model hf --model_args pretrained=model1 --tasks all --limit 1000\\nlm_eval --model hf --model_args pretrained=model2 --tasks all --limit 1000\\n```\\nAggregate with `--save_jsonl` and external tools.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Custom Tasks\\n1. Define task in `lm_eval\u002Ftasks\u002F`: - YAML config for dataset. - Python processor for few-shot prompting.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"2. Example custom task YAML: ```yaml\\ntask: my_custom_task\\ndataset_path: huggingface\\ndataset_name: my_dataset\\ntraining_split: train\\nfewshot_split: validation\\nmetric_list: - metric: acc aggregation: mean higher_is_better: true\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"3. Run: `lm_eval --tasks my_custom_task`\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Few-Shot and Chain-of-Thought Prompting\\n- `--num_fewshot 5`: In-context examples.\\n- Custom templates via `--gen_kwargs temperature=0.7`.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"For CoT: Use tasks like `gsm8k_cot`.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Optimization and Best Practices\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Performance Tuning\\n- **Batching**: `--batch_size 32` or `auto`.\\n- **Quantization**: `--model_args dtype=bfloat16,load_in_4bit=True`.\\n- **Distributed**: `--multi_gpu` for Ray integration.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Cost Efficiency\\n- Limit samples: `--limit 500`.\\n- Use smaller subsets: `--subsample 0.1`.\\n- Cache results: `--cache_dir \u002Fpath\u002Fto\u002Fcache`.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"### Reliability Tips\\n- Run multiple seeds: `--num_generations 8`.\\n- Bootstrap confidence intervals.\\n- Log everything with `--log_samples`.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Integration in LLMOps Pipelines\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Embed in CI\u002FCD:\\n1. GitHub Actions YAML: ```yaml - name: Evaluate Model run: lm_eval --model hf --model_args pretrained=${{ inputs.model }} --tasks core --batch_size auto > results.json ```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"2. MLflow tracking: ```python\\nimport mlflow\\nmlflow.log_metrics(results)\\n```\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"3. Prometheus\u002FGrafana for dashboards.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Troubleshooting Common Issues\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"- **OOM Errors**: Reduce batch size or use gradient checkpointing.\\n- **CUDA Out of Memory**: Enable `torch.backends.cuda.enable_flash_sdp(True)`.\\n- **Slow Inference**: Switch to vLLM loader: `--model vllm`.\\n- **Dataset Not Found**: Check HF access token.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"## Conclusion and Next Steps\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Evaluation Harness transforms subjective LLM assessment into a data-driven process. Start with core tasks, scale to custom evals, and integrate into your LLMOps workflow.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Resources:\\n- GitHub: [EleutherAI\u002Flm-evaluation-harness](https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness)\\n- Leaderboard: [Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fopen-llm-leaderboard\u002Fopen_llm_leaderboard)\\n- Discord: EleutherAI community.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Experiment today to unlock precise model insights.\"},\"type\":\"paragraph\"}],\"version\":\"2.31\"}",{"time":395,"blocks":396,"version":363},1772391033350,[397,400,403,406,409,412,415,418,420,422,424,426,429,432,435,438,441,444,447,450,452,455,458,460,463,465,468,470,473,476,479,482,485,488,491,494,496,498,501,504,507,510,513,516],{"data":398,"type":233},{"text":399},"# Evaluation Harness Guide",{"data":401,"type":233},{"text":402},"## Introduction to Evaluation Harness",{"data":404,"type":233},{"text":405},"Evaluation Harness is a powerful, open-source framework designed specifically for evaluating large language models (LLMs). Developed by the EleutherAI community, it standardizes the process of benchmarking LLMs across diverse tasks, metrics, and datasets. In enterprise LLMOps, it serves as a cornerstone for model selection, fine-tuning validation, and continuous monitoring.",{"data":407,"type":233},{"text":408},"Key benefits include:\n- **Consistency**: Uniform evaluation protocols across models and tasks.\n- **Scalability**: Handles massive datasets and multiple models efficiently.\n- **Extensibility**: Supports custom tasks, datasets, and metrics.\n- **Reproducibility**: Deterministic results with seeded randomness and caching.",{"data":410,"type":233},{"text":411},"Ideal for teams transitioning from ad-hoc testing to production-grade LLM evaluation.",{"data":413,"type":233},{"text":414},"## Prerequisites and Installation",{"data":416,"type":233},{"text":417},"Before diving in, ensure your environment meets these requirements:\n- Python 3.10+.\n- GPU\u002FTPU acceleration (recommended for large models).\n- Sufficient RAM (16GB+ for mid-sized models).",{"data":419,"type":233},{"text":254},{"data":421,"type":233},{"text":257},{"data":423,"type":233},{"text":260},{"data":425,"type":233},{"text":263},{"data":427,"type":233},{"text":428},"Pro tip: Use a virtual environment like `venv` or `conda` to isolate dependencies.",{"data":430,"type":233},{"text":431},"## Core Concepts",{"data":433,"type":233},{"text":434},"### Tasks and Datasets\nEvaluation Harness supports 200+ tasks out-of-the-box, categorized as:\n- **Classification**: ARC, BoolQ, HellaSwag.\n- **Generative**: AlpacaEval, MT-Bench.\n- **Reasoning**: GSM8K, MATH.\n- **Multimodal**: MMMU, MathVista.",{"data":436,"type":233},{"text":437},"Datasets auto-download from Hugging Face Hub.",{"data":439,"type":233},{"text":440},"### Metrics\nCommon metrics include:\n- **Accuracy**: Exact match for classification.\n- **F1**: Balanced precision\u002Frecall.\n- **Perplexity**: For generative fluency.\n- **BLEU\u002FROUGE**: Translation and summarization.",{"data":442,"type":233},{"text":443},"Custom metrics via `--metric` flag.",{"data":445,"type":233},{"text":446},"### Model Loading\nSupports HF Transformers, Llama.cpp, vLLM, and more:\n- Hugging Face: `meta-llama\u002FLlama-2-7b-chat-hf`\n- Local: Custom paths with quantization (e.g., 4-bit).",{"data":448,"type":233},{"text":449},"## Running Basic Evaluations",{"data":451,"type":233},{"text":290},{"data":453,"type":233},{"text":454},"Breakdown:\n- `--model hf`: Hugging Face loader.\n- `--tasks`: Comma-separated tasks.\n- `--batch_size auto`: Optimizes for hardware.",{"data":456,"type":233},{"text":457},"### Interpreting Results\nOutput includes:\n- **acc**: Accuracy score.\n- **acc_stderr**: Standard error.\n- Leaderboard-compatible JSON.",{"data":459,"type":233},{"text":299},{"data":461,"type":233},{"text":462},"## Advanced Usage",{"data":464,"type":233},{"text":305},{"data":466,"type":233},{"text":467},"### Custom Tasks\n1. Define task in `lm_eval\u002Ftasks\u002F`: - YAML config for dataset. - Python processor for few-shot prompting.",{"data":469,"type":233},{"text":311},{"data":471,"type":233},{"text":472},"3. Run: `lm_eval --tasks my_custom_task`",{"data":474,"type":233},{"text":475},"### Few-Shot and Chain-of-Thought Prompting\n- `--num_fewshot 5`: In-context examples.\n- Custom templates via `--gen_kwargs temperature=0.7`.",{"data":477,"type":233},{"text":478},"For CoT: Use tasks like `gsm8k_cot`.",{"data":480,"type":233},{"text":481},"## Optimization and Best Practices",{"data":483,"type":233},{"text":484},"### Performance Tuning\n- **Batching**: `--batch_size 32` or `auto`.\n- **Quantization**: `--model_args dtype=bfloat16,load_in_4bit=True`.\n- **Distributed**: `--multi_gpu` for Ray integration.",{"data":486,"type":233},{"text":487},"### Cost Efficiency\n- Limit samples: `--limit 500`.\n- Use smaller subsets: `--subsample 0.1`.\n- Cache results: `--cache_dir \u002Fpath\u002Fto\u002Fcache`.",{"data":489,"type":233},{"text":490},"### Reliability Tips\n- Run multiple seeds: `--num_generations 8`.\n- Bootstrap confidence intervals.\n- Log everything with `--log_samples`.",{"data":492,"type":233},{"text":493},"## Integration in LLMOps Pipelines",{"data":495,"type":233},{"text":338},{"data":497,"type":233},{"text":341},{"data":499,"type":233},{"text":500},"3. Prometheus\u002FGrafana for dashboards.",{"data":502,"type":233},{"text":503},"## Troubleshooting Common Issues",{"data":505,"type":233},{"text":506},"- **OOM Errors**: Reduce batch size or use gradient checkpointing.\n- **CUDA Out of Memory**: Enable `torch.backends.cuda.enable_flash_sdp(True)`.\n- **Slow Inference**: Switch to vLLM loader: `--model vllm`.\n- **Dataset Not Found**: Check HF access token.",{"data":508,"type":233},{"text":509},"## Conclusion and Next Steps",{"data":511,"type":233},{"text":512},"Evaluation Harness transforms subjective LLM assessment into a data-driven process. Start with core tasks, scale to custom evals, and integrate into your LLMOps workflow.",{"data":514,"type":233},{"text":515},"Resources:\n- GitHub: [EleutherAI\u002Flm-evaluation-harness](https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness)\n- Leaderboard: [Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fopen-llm-leaderboard\u002Fopen_llm_leaderboard)\n- Discord: EleutherAI community.",{"data":517,"type":233},{"text":518},"Experiment today to unlock precise model insights.","This guide provides a detailed walkthrough of Evaluation Harness, an essential framework for rigorously assessing large language model (LLM) capabilities in enterprise LLMOps pipelines. Learn setup, best practices, and advanced techniques to ensure reliable model benchmarking and optimization.",{"lang":7,"title":224,"content":226,"contentJson":521,"excerpt":364},{"time":228,"blocks":522,"version":363},[523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609],{"data":524,"type":233},{"text":232},{"data":526,"type":233},{"text":236},{"data":528,"type":233},{"text":239},{"data":530,"type":233},{"text":242},{"data":532,"type":233},{"text":245},{"data":534,"type":233},{"text":248},{"data":536,"type":233},{"text":251},{"data":538,"type":233},{"text":254},{"data":540,"type":233},{"text":257},{"data":542,"type":233},{"text":260},{"data":544,"type":233},{"text":263},{"data":546,"type":233},{"text":266},{"data":548,"type":233},{"text":269},{"data":550,"type":233},{"text":272},{"data":552,"type":233},{"text":275},{"data":554,"type":233},{"text":278},{"data":556,"type":233},{"text":281},{"data":558,"type":233},{"text":284},{"data":560,"type":233},{"text":287},{"data":562,"type":233},{"text":290},{"data":564,"type":233},{"text":293},{"data":566,"type":233},{"text":296},{"data":568,"type":233},{"text":299},{"data":570,"type":233},{"text":302},{"data":572,"type":233},{"text":305},{"data":574,"type":233},{"text":308},{"data":576,"type":233},{"text":311},{"data":578,"type":233},{"text":314},{"data":580,"type":233},{"text":317},{"data":582,"type":233},{"text":320},{"data":584,"type":233},{"text":323},{"data":586,"type":233},{"text":326},{"data":588,"type":233},{"text":329},{"data":590,"type":233},{"text":332},{"data":592,"type":233},{"text":335},{"data":594,"type":233},{"text":338},{"data":596,"type":233},{"text":341},{"data":598,"type":233},{"text":344},{"data":600,"type":233},{"text":347},{"data":602,"type":233},{"text":350},{"data":604,"type":233},{"text":353},{"data":606,"type":233},{"text":356},{"data":608,"type":233},{"text":359},{"data":610,"type":233},{"text":362},"Post erfolgreich abgerufen",{"items":613,"source":642,"manualIds":643,"manualMatchedIds":644},[614,621,628,635],{"id":615,"slug":616,"title":617,"excerpt":618,"featuredImage":619,"publishedAt":620},"469","rag-failed-but-which-layer-actually-failed-a-diagnostic-method","RAG не сработал — но какой именно слой на самом деле отказал? Метод диагностики","Когда ответ RAG неверен, обвинять поиск или модель — слишком расплывчато. Этот диагностический метод изолирует покрытие источников, построение запроса, поиск, ранжирование, сборку контекста, генерацию, атрибуцию доказательств и актуальность — так что фактический сбой можно воспроизвести и исправить.","\u002Fuploads\u002F2026\u002F09\u002Frag-failed-but-which-layer-actually-failed-a-diagnostic-method-1790350847177-pior4c.webp","2026-09-24T19:39:00.000Z",{"id":622,"slug":623,"title":624,"excerpt":625,"featuredImage":626,"publishedAt":627},"475","managed-agent-harness-vs-self-hosted-agent-loop-what-you-gain-what-you-lose","Управляемая обвязка агента против self-hosted цикла агента: что вы приобретаете, что теряете","“Self-hosted агент” может означать совершенно разные архитектуры. Это руководство разделяет управляемую обвязку, self-hosted среду выполнения и полностью самостоятельно управляемый цикл агента — и показывает, какая граница контроля на самом деле нужна командам.","\u002Fuploads\u002F2026\u002F09\u002Fmanaged-agent-harness-vs-self-hosted-agent-loop-what-you-gain-what-you-lose-1790352403475-kj10jh.webp","2026-09-25T12:05:00.000Z",{"id":629,"slug":630,"title":631,"excerpt":632,"featuredImage":633,"publishedAt":634},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps против LLMOps: что меняется, когда модель — это LLM","MLOps управляет системами машинного обучения; LLMOps распространяет эти практики на промпты, контекст, извлечение, провайдеров, инструменты, оценки и поведение во время выполнения вокруг больших языковых моделей.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":636,"slug":637,"title":638,"excerpt":639,"featuredImage":640,"publishedAt":641},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama — это не продукт: создание готовых к продакшену приложений на базе открытых LLM","Запустить локальную модель с Ollama просто. Создать готовое к продакшену Open-LLM-приложение сложнее: для этого требуются RAG, контроль доступа, абстракция провайдеров, оценка, логирование, дисциплина развертывания и контролируемый уровень приложения вокруг модели.","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z","fallback",[],[]]