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langage (LLM). Développé par la communauté EleutherAI, il standardise le processus d'analyse comparative (benchmarking) des LLM à travers diverses tâches, métriques et jeux de données. Dans le cadre du LLMOps en entreprise, il sert de pierre angulaire pour la sélection de modèles, la validation du fine-tuning et la surveillance continue.\u003C\u002Fp>\n\u003Cp>Les principaux avantages incluent :\n- **Cohérence** : Protocoles d'évaluation uniformes pour l'ensemble des modèles et des tâches.\n- **Évolutivité** : Gère efficacement des jeux de données massifs et plusieurs modèles.\n- **Extensibilité** : Prend en charge des tâches, des jeux de données et des métriques personnalisés.\n- **Reproductibilité** : Résultats déterministes grâce à l'aléa contrôlé (seeding) et à la mise en cache.\u003C\u002Fp>\n\u003Cp>Idéal pour les équipes passant de tests ad-hoc à une évaluation de LLM de niveau production.\u003C\u002Fp>\n\u003Cp>## Prérequis et Installation\u003C\u002Fp>\n\u003Cp>Avant de commencer, assurez-vous que votre environnement répond à ces exigences :\n- Python 3.10+.\n- Accélération GPU\u002FTPU (recommandée pour les grands modèles).\n- RAM suffisante (16 Go+ pour les modèles de taille moyenne).\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>Conseil de pro : Utilisez un environnement virtuel comme `venv` ou `conda` pour isoler les dépendances.\u003C\u002Fp>\n\u003Cp>## Concepts fondamentaux\u003C\u002Fp>\n\u003Cp>### Tâches et jeux de données\nEvaluation Harness prend en charge plus de 200 tâches prêtes à l'emploi, classées comme suit :\n- **Classification** : ARC, BoolQ, HellaSwag.\n- **Génératif** : AlpacaEval, MT-Bench.\n- **Raisonnement** : GSM8K, MATH.\n- **Multimodal** : MMMU, MathVista.\u003C\u002Fp>\n\u003Cp>Les jeux de données se téléchargent automatiquement depuis le Hugging Face Hub.\u003C\u002Fp>\n\u003Cp>### Métriques\nLes métriques courantes incluent :\n- **Précision (Accuracy)** : Correspondance exacte pour la classification.\n- **F1** : Équilibre entre précision et rappel.\n- **Perplexité** : Pour la fluidité générative.\n- **BLEU\u002FROUGE** : Traduction et résumé.\u003C\u002Fp>\n\u003Cp>Métriques personnalisées via le drapeau `--metric`.\u003C\u002Fp>\n\u003Cp>### Chargement du modèle\nPrend en charge HF Transformers, Llama.cpp, vLLM, et plus encore :\n- Hugging Face : `meta-llama\u002FLlama-2-7b-chat-hf`\n- Local : Chemins personnalisés avec quantification (ex. : 4 bits).\u003C\u002Fp>\n\u003Cp>## Exécution d'évaluations de base\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>Détails :\n- `--model hf` : Chargeur Hugging Face.\n- `--tasks` : Tâches séparées par des virgules.\n- `--batch_size auto` : Optimise pour le matériel.\u003C\u002Fp>\n\u003Cp>### Interprétation des résultats\nLa sortie comprend :\n- **acc** : Score de précision.\n- **acc_stderr** : Erreur type.\n- JSON compatible avec le classement.\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>## Utilisation avancée\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>### Tâches personnalisées\n1. Définir la tâche dans `lm_eval\u002Ftasks\u002F` : - Configuration YAML pour le jeu de données. - Processeur Python pour le prompting few-shot.\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. Exécuter : `lm_eval --tasks my_custom_task`\u003C\u002Fp>\n\u003Cp>### Prompting Few-Shot et Chain-of-Thought\n- `--num_fewshot 5` : Exemples en contexte.\n- Modèles personnalisés via `--gen_kwargs temperature=0.7`.\u003C\u002Fp>\n\u003Cp>Pour le CoT : Utilisez des tâches comme `gsm8k_cot`.\u003C\u002Fp>\n\u003Cp>## Optimisation et bonnes pratiques\u003C\u002Fp>\n\u003Cp>### Réglage des performances\n- **Batching** : `--batch_size 32` ou `auto`.\n- **Quantification** : `--model_args dtype=bfloat16,load_in_4bit=True`.\n- **Distribué** : `--multi_gpu` pour l'intégration Ray.\u003C\u002Fp>\n\u003Cp>### Efficacité des coûts\n- Limiter les échantillons : `--limit 500`.\n- Utiliser des sous-ensembles plus petits : `--subsample 0.1`.\n- Mettre en cache les résultats : `--cache_dir \u002Fpath\u002Fto\u002Fcache`.\u003C\u002Fp>\n\u003Cp>### Conseils de fiabilité\n- Exécuter plusieurs graines : `--num_generations 8`.\n- Intervalles de confiance bootstrap.\n- Tout journaliser avec `--log_samples`.\u003C\u002Fp>\n\u003Cp>## Intégration dans les pipelines 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 pour les tableaux de bord.\u003C\u002Fp>\n\u003Cp>## Dépannage des problèmes courants\u003C\u002Fp>\n\u003Cp>- **Erreurs OOM** : Réduisez la taille des lots ou utilisez le gradient checkpointing.\n- **CUDA Out of Memory** : Activez `torch.backends.cuda.enable_flash_sdp(True)`.\n- **Inférence lente** : Passez au chargeur vLLM : `--model vllm`.\n- **Jeu de données non trouvé** : Vérifiez le jeton d'accès HF.\u003C\u002Fp>\n\u003Cp>## Conclusion et prochaines étapes\u003C\u002Fp>\n\u003Cp>Evaluation Harness transforme l'évaluation subjective des LLM en un processus axé sur les données. Commencez par les tâches de base, passez aux évaluations personnalisées et intégrez-les dans votre flux de travail LLMOps.\u003C\u002Fp>\n\u003Cp>Ressources :\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 : Communauté EleutherAI.\u003C\u002Fp>\n\u003Cp>Expérimentez dès aujourd'hui pour obtenir des informations précises sur vos modèles.\u003C\u002Fp>",{"time":228,"blocks":229,"version":363},1775468341710,[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},"# Guide d'Evaluation Harness","paragraph",{"data":235,"type":233},{"text":236},"## Introduction à Evaluation Harness",{"data":238,"type":233},{"text":239},"Evaluation Harness est un framework open-source puissant, conçu spécifiquement pour l'évaluation des grands modèles de langage (LLM). Développé par la communauté EleutherAI, il standardise le processus d'analyse comparative (benchmarking) des LLM à travers diverses tâches, métriques et jeux de données. Dans le cadre du LLMOps en entreprise, il sert de pierre angulaire pour la sélection de modèles, la validation du fine-tuning et la surveillance continue.",{"data":241,"type":233},{"text":242},"Les principaux avantages incluent :\n- **Cohérence** : Protocoles d'évaluation uniformes pour l'ensemble des modèles et des tâches.\n- **Évolutivité** : Gère efficacement des jeux de données massifs et plusieurs modèles.\n- **Extensibilité** : Prend en charge des tâches, des jeux de données et des métriques personnalisés.\n- **Reproductibilité** : Résultats déterministes grâce à l'aléa contrôlé (seeding) et à la mise en cache.",{"data":244,"type":233},{"text":245},"Idéal pour les équipes passant de tests ad-hoc à une évaluation de LLM de niveau production.",{"data":247,"type":233},{"text":248},"## Prérequis et Installation",{"data":250,"type":233},{"text":251},"Avant de commencer, assurez-vous que votre environnement répond à ces exigences :\n- Python 3.10+.\n- Accélération GPU\u002FTPU (recommandée pour les grands modèles).\n- RAM suffisante (16 Go+ pour les modèles de taille moyenne).",{"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},"Conseil de pro : Utilisez un environnement virtuel comme `venv` ou `conda` pour isoler les dépendances.",{"data":268,"type":233},{"text":269},"## Concepts fondamentaux",{"data":271,"type":233},{"text":272},"### Tâches et jeux de données\nEvaluation Harness prend en charge plus de 200 tâches prêtes à l'emploi, classées comme suit :\n- **Classification** : ARC, BoolQ, HellaSwag.\n- **Génératif** : AlpacaEval, MT-Bench.\n- **Raisonnement** : GSM8K, MATH.\n- **Multimodal** : MMMU, MathVista.",{"data":274,"type":233},{"text":275},"Les jeux de données se téléchargent automatiquement depuis le Hugging Face Hub.",{"data":277,"type":233},{"text":278},"### Métriques\nLes métriques courantes incluent :\n- **Précision (Accuracy)** : Correspondance exacte pour la classification.\n- **F1** : Équilibre entre précision et rappel.\n- **Perplexité** : Pour la fluidité générative.\n- **BLEU\u002FROUGE** : Traduction et résumé.",{"data":280,"type":233},{"text":281},"Métriques personnalisées via le drapeau `--metric`.",{"data":283,"type":233},{"text":284},"### Chargement du modèle\nPrend en charge HF Transformers, Llama.cpp, vLLM, et plus encore :\n- Hugging Face : `meta-llama\u002FLlama-2-7b-chat-hf`\n- Local : Chemins personnalisés avec quantification (ex. : 4 bits).",{"data":286,"type":233},{"text":287},"## Exécution d'évaluations de base",{"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},"Détails :\n- `--model hf` : Chargeur Hugging Face.\n- `--tasks` : Tâches séparées par des virgules.\n- `--batch_size auto` : Optimise pour le matériel.",{"data":295,"type":233},{"text":296},"### Interprétation des résultats\nLa sortie comprend :\n- **acc** : Score de précision.\n- **acc_stderr** : Erreur type.\n- JSON compatible avec le classement.",{"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},"## Utilisation avancée",{"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},"### Tâches personnalisées\n1. Définir la tâche dans `lm_eval\u002Ftasks\u002F` : - Configuration YAML pour le jeu de données. - Processeur Python pour le prompting few-shot.",{"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. Exécuter : `lm_eval --tasks my_custom_task`",{"data":316,"type":233},{"text":317},"### Prompting Few-Shot et Chain-of-Thought\n- `--num_fewshot 5` : Exemples en contexte.\n- Modèles personnalisés via `--gen_kwargs temperature=0.7`.",{"data":319,"type":233},{"text":320},"Pour le CoT : Utilisez des tâches comme `gsm8k_cot`.",{"data":322,"type":233},{"text":323},"## Optimisation et bonnes pratiques",{"data":325,"type":233},{"text":326},"### Réglage des performances\n- **Batching** : `--batch_size 32` ou `auto`.\n- **Quantification** : `--model_args dtype=bfloat16,load_in_4bit=True`.\n- **Distribué** : `--multi_gpu` pour l'intégration Ray.",{"data":328,"type":233},{"text":329},"### Efficacité des coûts\n- Limiter les échantillons : `--limit 500`.\n- Utiliser des sous-ensembles plus petits : `--subsample 0.1`.\n- Mettre en cache les résultats : `--cache_dir \u002Fpath\u002Fto\u002Fcache`.",{"data":331,"type":233},{"text":332},"### Conseils de fiabilité\n- Exécuter plusieurs graines : `--num_generations 8`.\n- Intervalles de confiance bootstrap.\n- Tout journaliser avec `--log_samples`.",{"data":334,"type":233},{"text":335},"## Intégration dans les pipelines 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 pour les tableaux de bord.",{"data":346,"type":233},{"text":347},"## Dépannage des problèmes courants",{"data":349,"type":233},{"text":350},"- **Erreurs OOM** : Réduisez la taille des lots ou utilisez le gradient checkpointing.\n- **CUDA Out of Memory** : Activez `torch.backends.cuda.enable_flash_sdp(True)`.\n- **Inférence lente** : Passez au chargeur vLLM : `--model vllm`.\n- **Jeu de données non trouvé** : Vérifiez le jeton d'accès HF.",{"data":352,"type":233},{"text":353},"## Conclusion et prochaines étapes",{"data":355,"type":233},{"text":356},"Evaluation Harness transforme l'évaluation subjective des LLM en un processus axé sur les données. Commencez par les tâches de base, passez aux évaluations personnalisées et intégrez-les dans votre flux de travail LLMOps.",{"data":358,"type":233},{"text":359},"Ressources :\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 : Communauté EleutherAI.",{"data":361,"type":233},{"text":362},"Expérimentez dès aujourd'hui pour obtenir des informations précises sur vos modèles.","2.31","Ce guide propose une présentation détaillée d'Evaluation Harness, un framework essentiel pour évaluer rigoureusement les capacités des grands modèles de langage (LLM) dans les pipelines LLMOps d'entreprise. Découvrez la configuration, les meilleures pratiques et les techniques avancées pour garantir un benchmarking et une optimisation fiables des modèles.","\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,"Harnais d’évaluation",{"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","Échec du RAG — mais quelle couche a réellement échoué ? Une méthode de diagnostic","Lorsqu'une réponse RAG est erronée, blâmer la récupération ou le modèle est trop vague. Cette méthode de diagnostic isole la couverture des sources, la construction de la requête, la récupération, le classement, l'assemblage du contexte, la génération, l'attribution des preuves et la fraîcheur—afin que la défaillance réelle puisse être reproduite et corrigée.","\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},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs LLMOps : qu'est-ce qui change lorsque le modèle est un LLM","MLOps exploite les systèmes d'apprentissage automatique ; LLMOps étend ces pratiques aux prompts, au contexte, à la récupération, aux fournisseurs, aux outils, aux évaluations et au comportement d'exécution autour des grands modèles de langage.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":629,"slug":630,"title":631,"excerpt":632,"featuredImage":633,"publishedAt":634},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama n'est pas le produit : construire des applications Open-LLM prêtes pour la production","Exécuter un modèle local avec Ollama est facile. Construire une application Open-LLM prête pour la production est plus difficile : cela nécessite du RAG, du contrôle d'accès, de l'abstraction de fournisseur, de l'évaluation, de la journalisation, de la discipline de déploiement et une couche applicative contrôlée autour du modèle.","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z",{"id":636,"slug":637,"title":638,"excerpt":639,"featuredImage":640,"publishedAt":641},"475","managed-agent-harness-vs-self-hosted-agent-loop-what-you-gain-what-you-lose","Harnais d'agent géré vs boucle d'agent auto-hébergée : ce que vous gagnez, ce que vous perdez","“Agent auto-hébergé” peut désigner des architectures très différentes. Ce guide distingue le harnais géré, l'environnement d'exécution auto-hébergé et la boucle d'agent entièrement auto-opérée—et montre de quelle frontière de contrôle les équipes ont réellement besoin.","\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","fallback",[],[]]