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Razvijen od strane EleutherAI zajednice, on standardizuje proces benčmarkovanja LLM-ova kroz različite zadatke, metrike i skupove podataka. U korporativnom LLMOps-u, on služi kao kamen temeljac za selekciju modela, validaciju finog podešavanja i kontinuirano praćenje.\u003C\u002Fp>\n\u003Cp>Ključne prednosti uključuju:\n- **Doslednost**: Uniformni protokoli evaluacije kroz modele i zadatke.\n- **Skalabilnost**: Efikasno upravlja masivnim skupovima podataka i višestrukim modelima.\n- **Proširivost**: Podržava prilagođene zadatke, skupove podataka i metrike.\n- **Reproduktibilnost**: Deterministički rezultati sa postavljenim nasumičnim vrednostima i keširanjem.\u003C\u002Fp>\n\u003Cp>Idealno za timove koji prelaze sa ad-hoc testiranja na evaluaciju LLM-ova produkcionog nivoa.\u003C\u002Fp>\n\u003Cp>## Preduslovi i instalacija\u003C\u002Fp>\n\u003Cp>Pre nego što počnete, uverite se da vaše okruženje ispunjava ove zahteve:\n- Python 3.10+.\n- GPU\u002FTPU akceleracija (preporučeno za velike modele).\n- Dovoljno RAM-a (16GB+ za modele srednje veličine).\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>Pro savet: Koristite virtuelno okruženje kao što je `venv` ili `conda` da biste izolovali zavisnosti.\u003C\u002Fp>\n\u003Cp>## Osnovni koncepti\u003C\u002Fp>\n\u003Cp>### Zadaci i skupovi podataka\nEvaluation Harness podržava više od 200 zadataka odmah po instalaciji, kategorisanih kao:\n- **Klasifikacija**: ARC, BoolQ, HellaSwag.\n- **Generativni**: AlpacaEval, MT-Bench.\n- **Rezonovanje**: GSM8K, MATH.\n- **Multimodalni**: MMMU, MathVista.\u003C\u002Fp>\n\u003Cp>Skupovi podataka se automatski preuzimaju sa Hugging Face Hub-a.\u003C\u002Fp>\n\u003Cp>### Metrike\nUobičajene metrike uključuju:\n- **Tačnost (Accuracy)**: Tačno podudaranje za klasifikaciju.\n- **F1**: Balansirana preciznost\u002Fodziv.\n- **Perpleksnost (Perplexity)**: Za generativnu tečnost.\n- **BLEU\u002FROUGE**: Prevođenje i sumiranje.\u003C\u002Fp>\n\u003Cp>Prilagođene metrike putem `--metric` zastavice.\u003C\u002Fp>\n\u003Cp>### Učitavanje modela\nPodržava HF Transformers, Llama.cpp, vLLM i druge:\n- Hugging Face: `meta-llama\u002FLlama-2-7b-chat-hf`\n- Lokalno: Prilagođene putanje sa kvantizacijom (npr. 4-bitna).\u003C\u002Fp>\n\u003Cp>## Pokretanje osnovnih evaluacija\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>Analiza:\n- `--model hf`: Hugging Face učitavač.\n- `--tasks`: Zadaci razdvojeni zarezom.\n- `--batch_size auto`: Optimizuje za hardver.\u003C\u002Fp>\n\u003Cp>### Interpretacija rezultata\nIzlaz uključuje:\n- **acc**: Rezultat tačnosti.\n- **acc_stderr**: Standardna greška.\n- JSON kompatibilan sa rang listom.\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>## Napredno korišćenje\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>### Prilagođeni zadaci\n1. Definišite zadatak u `lm_eval\u002Ftasks\u002F`: - YAML konfiguracija za skup podataka. - Python procesor za few-shot prompting.\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. Pokrenite: `lm_eval --tasks my_custom_task`\u003C\u002Fp>\n\u003Cp>### Few-Shot i Chain-of-Thought Prompting\n- `--num_fewshot 5`: Primeri u kontekstu.\n- Prilagođeni šabloni putem `--gen_kwargs temperature=0.7`.\u003C\u002Fp>\n\u003Cp>Za CoT: Koristite zadatke kao što je `gsm8k_cot`.\u003C\u002Fp>\n\u003Cp>## Optimizacija i najbolje prakse\u003C\u002Fp>\n\u003Cp>### Podešavanje performansi\n- **Batching**: `--batch_size 32` ili `auto`.\n- **Quantization**: `--model_args dtype=bfloat16,load_in_4bit=True`.\n- **Distributed**: `--multi_gpu` za Ray integraciju.\u003C\u002Fp>\n\u003Cp>### Isplativost\n- Ograničite uzorke: `--limit 500`.\n- Koristite manje podskupove: `--subsample 0.1`.\n- Keširajte rezultate: `--cache_dir \u002Fputanja\u002Fdo\u002Fkeša`.\u003C\u002Fp>\n\u003Cp>### Saveti za pouzdanost\n- Pokrenite više nasumičnih vrednosti (seeds): `--num_generations 8`.\n- Bootstrap intervali poverenja.\n- Zabeležite sve pomoću `--log_samples`.\u003C\u002Fp>\n\u003Cp>## Integracija u LLMOps pajplajne\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 za kontrolne table.\u003C\u002Fp>\n\u003Cp>## Rešavanje uobičajenih problema\u003C\u002Fp>\n\u003Cp>- **OOM greške**: Smanjite veličinu serije (batch size) ili koristite gradient checkpointing.\n- **CUDA Out of Memory**: Omogućite `torch.backends.cuda.enable_flash_sdp(True)`.\n- **Sporo zaključivanje**: Prebacite se na vLLM loader: `--model vllm`.\n- **Skup podataka nije pronađen**: Proverite HF pristupni token.\u003C\u002Fp>\n\u003Cp>## Zaključak i sledeći koraci\u003C\u002Fp>\n\u003Cp>Evaluation Harness transformiše subjektivnu procenu LLM-ova u proces zasnovan na podacima. Počnite sa osnovnim zadacima, skalirajte do prilagođenih evaluacija i integrišite u svoj LLMOps radni tok.\u003C\u002Fp>\n\u003Cp>Resursi:\n- GitHub: [EleutherAI\u002Flm-evaluation-harness](https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness)\n- Rang lista: [Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fopen-llm-leaderboard\u002Fopen_llm_leaderboard)\n- Discord: EleutherAI zajednica.\u003C\u002Fp>\n\u003Cp>Eksperimentišite danas kako biste otključali precizne uvide u modele.\u003C\u002Fp>",{"time":228,"blocks":229,"version":363},1775467629935,[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},"# Vodič za Evaluation Harness","paragraph",{"data":235,"type":233},{"text":236},"## Uvod u Evaluation Harness",{"data":238,"type":233},{"text":239},"Evaluation Harness je moćan radni okvir otvorenog koda dizajniran specifično za evaluaciju velikih jezičkih modela (LLM). Razvijen od strane EleutherAI zajednice, on standardizuje proces benčmarkovanja LLM-ova kroz različite zadatke, metrike i skupove podataka. U korporativnom LLMOps-u, on služi kao kamen temeljac za selekciju modela, validaciju finog podešavanja i kontinuirano praćenje.",{"data":241,"type":233},{"text":242},"Ključne prednosti uključuju:\n- **Doslednost**: Uniformni protokoli evaluacije kroz modele i zadatke.\n- **Skalabilnost**: Efikasno upravlja masivnim skupovima podataka i višestrukim modelima.\n- **Proširivost**: Podržava prilagođene zadatke, skupove podataka i metrike.\n- **Reproduktibilnost**: Deterministički rezultati sa postavljenim nasumičnim vrednostima i keširanjem.",{"data":244,"type":233},{"text":245},"Idealno za timove koji prelaze sa ad-hoc testiranja na evaluaciju LLM-ova produkcionog nivoa.",{"data":247,"type":233},{"text":248},"## Preduslovi i instalacija",{"data":250,"type":233},{"text":251},"Pre nego što počnete, uverite se da vaše okruženje ispunjava ove zahteve:\n- Python 3.10+.\n- GPU\u002FTPU akceleracija (preporučeno za velike modele).\n- Dovoljno RAM-a (16GB+ za modele srednje veličine).",{"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},"Pro savet: Koristite virtuelno okruženje kao što je `venv` ili `conda` da biste izolovali zavisnosti.",{"data":268,"type":233},{"text":269},"## Osnovni koncepti",{"data":271,"type":233},{"text":272},"### Zadaci i skupovi podataka\nEvaluation Harness podržava više od 200 zadataka odmah po instalaciji, kategorisanih kao:\n- **Klasifikacija**: ARC, BoolQ, HellaSwag.\n- **Generativni**: AlpacaEval, MT-Bench.\n- **Rezonovanje**: GSM8K, MATH.\n- **Multimodalni**: MMMU, MathVista.",{"data":274,"type":233},{"text":275},"Skupovi podataka se automatski preuzimaju sa Hugging Face Hub-a.",{"data":277,"type":233},{"text":278},"### Metrike\nUobičajene metrike uključuju:\n- **Tačnost (Accuracy)**: Tačno podudaranje za klasifikaciju.\n- **F1**: Balansirana preciznost\u002Fodziv.\n- **Perpleksnost (Perplexity)**: Za generativnu tečnost.\n- **BLEU\u002FROUGE**: Prevođenje i sumiranje.",{"data":280,"type":233},{"text":281},"Prilagođene metrike putem `--metric` zastavice.",{"data":283,"type":233},{"text":284},"### Učitavanje modela\nPodržava HF Transformers, Llama.cpp, vLLM i druge:\n- Hugging Face: `meta-llama\u002FLlama-2-7b-chat-hf`\n- Lokalno: Prilagođene putanje sa kvantizacijom (npr. 4-bitna).",{"data":286,"type":233},{"text":287},"## Pokretanje osnovnih evaluacija",{"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},"Analiza:\n- `--model hf`: Hugging Face učitavač.\n- `--tasks`: Zadaci razdvojeni zarezom.\n- `--batch_size auto`: Optimizuje za hardver.",{"data":295,"type":233},{"text":296},"### Interpretacija rezultata\nIzlaz uključuje:\n- **acc**: Rezultat tačnosti.\n- **acc_stderr**: Standardna greška.\n- JSON kompatibilan sa rang listom.",{"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},"## Napredno korišćenje",{"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},"### Prilagođeni zadaci\n1. Definišite zadatak u `lm_eval\u002Ftasks\u002F`: - YAML konfiguracija za skup podataka. - Python procesor za few-shot prompting.",{"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. Pokrenite: `lm_eval --tasks my_custom_task`",{"data":316,"type":233},{"text":317},"### Few-Shot i Chain-of-Thought Prompting\n- `--num_fewshot 5`: Primeri u kontekstu.\n- Prilagođeni šabloni putem `--gen_kwargs temperature=0.7`.",{"data":319,"type":233},{"text":320},"Za CoT: Koristite zadatke kao što je `gsm8k_cot`.",{"data":322,"type":233},{"text":323},"## Optimizacija i najbolje prakse",{"data":325,"type":233},{"text":326},"### Podešavanje performansi\n- **Batching**: `--batch_size 32` ili `auto`.\n- **Quantization**: `--model_args dtype=bfloat16,load_in_4bit=True`.\n- **Distributed**: `--multi_gpu` za Ray integraciju.",{"data":328,"type":233},{"text":329},"### Isplativost\n- Ograničite uzorke: `--limit 500`.\n- Koristite manje podskupove: `--subsample 0.1`.\n- Keširajte rezultate: `--cache_dir \u002Fputanja\u002Fdo\u002Fkeša`.",{"data":331,"type":233},{"text":332},"### Saveti za pouzdanost\n- Pokrenite više nasumičnih vrednosti (seeds): `--num_generations 8`.\n- Bootstrap intervali poverenja.\n- Zabeležite sve pomoću `--log_samples`.",{"data":334,"type":233},{"text":335},"## Integracija u LLMOps pajplajne",{"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 za kontrolne table.",{"data":346,"type":233},{"text":347},"## Rešavanje uobičajenih problema",{"data":349,"type":233},{"text":350},"- **OOM greške**: Smanjite veličinu serije (batch size) ili koristite gradient checkpointing.\n- **CUDA Out of Memory**: Omogućite `torch.backends.cuda.enable_flash_sdp(True)`.\n- **Sporo zaključivanje**: Prebacite se na vLLM loader: `--model vllm`.\n- **Skup podataka nije pronađen**: Proverite HF pristupni token.",{"data":352,"type":233},{"text":353},"## Zaključak i sledeći koraci",{"data":355,"type":233},{"text":356},"Evaluation Harness transformiše subjektivnu procenu LLM-ova u proces zasnovan na podacima. Počnite sa osnovnim zadacima, skalirajte do prilagođenih evaluacija i integrišite u svoj LLMOps radni tok.",{"data":358,"type":233},{"text":359},"Resursi:\n- GitHub: [EleutherAI\u002Flm-evaluation-harness](https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness)\n- Rang lista: [Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fopen-llm-leaderboard\u002Fopen_llm_leaderboard)\n- Discord: EleutherAI zajednica.",{"data":361,"type":233},{"text":362},"Eksperimentišite danas kako biste otključali precizne uvide u modele.","2.31","Ovaj vodič pruža detaljan pregled Evaluation Harness-a, ključnog okvira za rigoroznu procenu sposobnosti velikih jezičkih modela (LLM) u korporativnim LLMOps procesima. Naučite podešavanje, najbolje prakse i napredne tehnike kako biste osigurali pouzdano benčmarkovanje i optimizaciju modela.","\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,"Eval harness",{"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},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs LLMOps: Šta se menja kada je model LLM","MLOps upravlja sistemima mašinskog učenja; LLMOps proširuje te prakse na promptove, kontekst, pretragu, provajdere, alate, evaluacije i ponašanje u vreme izvršavanja oko velikih jezičkih modela.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":622,"slug":623,"title":624,"excerpt":625,"featuredImage":626,"publishedAt":627},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama nije proizvod: Izgradnja aplikacija spremnih za produkciju sa otvorenim LLM-ovima","Pokretanje lokalnog modela pomoću Ollama-e je jednostavno. Izgradnja Open-LLM aplikacije spremne za produkciju je teža: zahteva RAG, kontrolu pristupa, apstrakciju provajdera, evaluaciju, logovanje, disciplinu puštanja u rad i kontrolisani aplikativni sloj oko modela.","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z",{"id":629,"slug":630,"title":631,"excerpt":632,"featuredImage":633,"publishedAt":634},"469","rag-failed-but-which-layer-actually-failed-a-diagnostic-method","RAG nije uspeo — ali koji sloj je zapravo zakazao? Dijagnostička metoda","Kada je RAG odgovor pogrešan, kriviti pretragu ili model je previše neodređeno. Ova dijagnostička metoda izoluje pokrivenost izvora, konstrukciju upita, pretragu, rangiranje, sastavljanje konteksta, generisanje, pripisivanje dokaza i svežinu—tako da se stvarni kvar može reprodukovati i ispraviti.","\u002Fuploads\u002F2026\u002F09\u002Frag-failed-but-which-layer-actually-failed-a-diagnostic-method-1790350847177-pior4c.webp","2026-09-24T19:39:00.000Z",{"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","Upravljani harness za agente naspram samostalno hostovane petlje agenta: Šta dobijate, šta gubite","“Samostalno hostovani agent” može značiti veoma različite arhitekture. Ovaj vodič razgraničava upravljani harness, samostalno hostovano okruženje za izvršavanje i potpuno samostalno upravljanu petlju agenta—i pokazuje koja je granica kontrole timovima zapravo potrebna.","\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",[],[]]