{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"lm-evaluation-harness","slug":"lm-evaluation-harness","name":"lm-evaluation-harness","type":"benchmark","url":"https://github.com/EleutherAI/lm-evaluation-harness","page_url":"https://unfragile.ai/lm-evaluation-harness","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"lm-evaluation-harness__cap_0","uri":"capability://tool.use.integration.multi.backend.language.model.instantiation.with.unified.interface","name":"multi-backend language model instantiation with unified interface","description":"Provides a registry-based abstraction layer that instantiates language models from 25+ backends (HuggingFace, vLLM, OpenAI, Anthropic, local Ollama, etc.) through a single Python API. The registry pattern decouples task definitions from model implementations, allowing users to swap backends without changing evaluation code. Each backend implements a common interface supporting loglikelihood scoring and text generation with automatic tokenization, BOS token handling, and context window management.","intents":["I want to benchmark the same task across multiple model backends without rewriting evaluation code","I need to compare a local HuggingFace model against OpenAI's API using identical prompts","I want to evaluate a vLLM-optimized model with the same metrics as a standard transformers model"],"best_for":["researchers comparing models across different inference frameworks","teams evaluating both proprietary and open-source models in a single pipeline","organizations migrating from one inference backend to another"],"limitations":["Backend-specific features (e.g., vLLM's tensor parallelism) require explicit configuration; abstraction doesn't auto-optimize","API-based models (OpenAI, Anthropic) incur per-token costs; local models don't, creating cost asymmetry in benchmarks","Tokenizer differences between backends can cause slight loglikelihood score variations even for identical models"],"requires":["Python 3.9+","Backend-specific dependencies (transformers, vllm, openai, anthropic, etc.)","API keys for cloud-based backends (OpenAI, Anthropic, etc.)","CUDA/GPU drivers if using GPU-accelerated backends"],"input_types":["model identifier string (e.g., 'meta-llama/Llama-2-7b-hf', 'gpt-4')","backend configuration dict with parameters (batch_size, dtype, device_map, etc.)"],"output_types":["instantiated model object implementing LM interface","loglikelihood scores for prompt-completion pairs","generated text completions"],"categories":["tool-use-integration","model-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_1","uri":"capability://data.processing.analysis.yaml.based.task.definition.with.inheritance.and.templating","name":"yaml-based task definition with inheritance and templating","description":"Enables users to define evaluation tasks declaratively via YAML configuration files with support for Jinja2 templating, task inheritance, and document processing. Tasks specify prompts, few-shot examples, metrics, and answer extraction logic without writing Python code. The TaskManager loads YAML configs, resolves inheritance chains, and instantiates Task objects that generate evaluation requests. This approach separates task logic from evaluation infrastructure, allowing non-engineers to create benchmarks.","intents":["I want to create a new evaluation task without writing Python code","I need to define a task with few-shot examples and custom prompt templates","I want to reuse common task structure across multiple related benchmarks using inheritance"],"best_for":["researchers designing new benchmarks without deep Python expertise","teams maintaining large task suites (200+ tasks) with shared configuration patterns","organizations needing version-controlled, human-readable task definitions"],"limitations":["Complex metric computation (e.g., custom statistical tests) still requires Python; YAML is limited to built-in metrics","Jinja2 templating adds parsing overhead (~5-10ms per task instantiation); not suitable for real-time prompt generation","Task inheritance can create deep dependency chains that are hard to debug if misconfigured"],"requires":["YAML syntax knowledge","Jinja2 template syntax understanding for dynamic prompts","Access to task directory structure (lm_eval/tasks/)","Python 3.9+ for YAML parsing"],"input_types":["YAML file with task definition (prompt, few_shot_num_shots, metric, etc.)","Jinja2 template strings for dynamic prompt generation","Dataset files (JSON, CSV, HuggingFace datasets)"],"output_types":["Task object with request generator","List of evaluation requests (prompt + expected output pairs)","Metric computation functions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_10","uri":"capability://data.processing.analysis.benchmark.suite.composition.and.aggregation","name":"benchmark suite composition and aggregation","description":"Enables grouping of related tasks into benchmark suites (e.g., MMLU, BigBench, HELM) with aggregated metrics and reporting. Suites can be defined in YAML or Python, with support for task groups, weighted aggregation, and suite-level metrics. The system computes both per-task and suite-level results, with confidence intervals propagated through aggregation. Supports standard NLP benchmarks, multilingual benchmarks, robustness frameworks (SCORE), and custom suites.","intents":["I want to evaluate my model on MMLU and report aggregated accuracy across all subjects","I need to create a custom benchmark suite combining tasks from multiple sources","I want to weight tasks differently in suite aggregation (e.g., 50% reasoning, 50% knowledge)"],"best_for":["researchers publishing results on standard benchmarks (MMLU, BigBench, etc.)","teams creating custom benchmark suites for domain-specific evaluation","organizations comparing models using weighted aggregation"],"limitations":["Weighted aggregation requires manual weight specification; no automatic weighting based on task difficulty","Suite-level confidence intervals assume independence between tasks; correlated errors violate this assumption","Large suites (100+ tasks) can take hours to evaluate; no built-in prioritization or early stopping"],"requires":["Task definitions for all suite members","Suite definition (YAML or Python) with task list","Optional: task weights for weighted aggregation","Optional: suite-level metrics definition"],"input_types":["suite_name string","task_list list of task names","task_weights dict (optional)","aggregation_method string ('mean', 'weighted_mean', 'custom')"],"output_types":["per-task results dict","suite-level aggregated metrics","confidence intervals for aggregated metrics","suite report (JSON or formatted text)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_11","uri":"capability://code.generation.editing.custom.task.definition.via.python.classes.with.metric.registration","name":"custom task definition via python classes with metric registration","description":"Allows advanced users to define evaluation tasks as Python classes extending the Task base class, with custom metric functions and request generation logic. Custom tasks can implement arbitrary evaluation logic beyond YAML capabilities, including complex metrics, multi-stage evaluation, and dynamic request generation. Metrics are registered in a global registry and can be reused across tasks. This provides maximum flexibility for researchers designing novel evaluation approaches.","intents":["I want to implement a custom metric that's not available in the built-in set","I need to define a task with complex, multi-stage evaluation logic","I want to create a task that dynamically generates requests based on model outputs"],"best_for":["researchers designing novel evaluation methodologies","teams with domain-specific metrics not covered by built-in options","organizations extending the framework with custom task types"],"limitations":["Custom task implementation requires Python expertise; not accessible to non-programmers","Custom metrics must handle edge cases (empty predictions, malformed outputs) explicitly","Custom tasks may not benefit from framework optimizations (batching, caching) if not implemented carefully"],"requires":["Python 3.9+","Understanding of Task base class interface","Metric function signature (predictions, references) -> float","Knowledge of request generation and evaluation pipeline"],"input_types":["Python class extending Task","Custom metric function","Dataset with examples","Model backend object"],"output_types":["Task instance with custom request generator","Custom metric results","Registered metric in global registry"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_12","uri":"capability://data.processing.analysis.model.agnostic.evaluation.with.tokenizer.abstraction","name":"model-agnostic evaluation with tokenizer abstraction","description":"Abstracts tokenizer differences across models by providing a unified tokenization interface that handles special tokens, padding, and attention masks consistently. The system automatically selects the correct tokenizer for each model backend and applies model-specific token handling (e.g., BOS token prepending for certain models). This enables fair comparison across models with different tokenization schemes, which would otherwise produce different loglikelihood scores for identical prompts.","intents":["I want to compare loglikelihood scores across models with different tokenizers fairly","I need to handle BOS token differences between models automatically","I want to evaluate models without worrying about tokenizer-specific edge cases"],"best_for":["researchers comparing models across different architectures and tokenizers","teams evaluating both open-source and proprietary models with different tokenization","organizations ensuring fair comparison by controlling for tokenizer effects"],"limitations":["Tokenizer abstraction cannot fully eliminate differences; some models have fundamentally different tokenization (e.g., character-level vs. BPE)","BOS token handling is model-specific and sometimes undocumented; heuristics may be incorrect for novel models","Tokenizer loading adds startup overhead (~1-2 seconds per model); significant for evaluating many models"],"requires":["Model with associated tokenizer (HuggingFace, custom, or API-based)","Tokenizer config with special token definitions","Optional: BOS token handling configuration"],"input_types":["model_name string","prompt string","tokenizer config dict"],"output_types":["tokenized input (input_ids, attention_mask)","token count","special token positions"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_13","uri":"capability://automation.workflow.command.line.interface.with.flexible.task.and.model.specification","name":"command-line interface with flexible task and model specification","description":"Provides a comprehensive CLI (lm_eval/__main__.py) that accepts task names, model names, and evaluation parameters as command-line arguments. Supports task filtering (e.g., 'mmlu_*' to run all MMLU variants), model specification with backend selection, and output format configuration. The CLI integrates all framework capabilities (batching, caching, distributed evaluation, logging) without requiring Python code, making the framework accessible to non-programmers.","intents":["I want to run a quick evaluation from the command line without writing Python code","I need to evaluate multiple tasks and models with a single command","I want to specify evaluation parameters (batch size, num_fewshot, etc.) via CLI flags"],"best_for":["researchers and practitioners using the framework for standard evaluations","teams integrating evaluation into CI/CD pipelines","organizations running evaluations without custom Python code"],"limitations":["CLI is limited to built-in tasks and metrics; custom tasks require Python API","Complex evaluation logic (e.g., conditional task execution) requires Python scripts","CLI argument parsing can be verbose for complex configurations; YAML config files are more readable"],"requires":["lm-evaluation-harness installed","Model backend dependencies (transformers, vllm, etc.)","Optional: API keys for cloud-based models"],"input_types":["task_names string (comma-separated or glob pattern)","model_name string","model_args string (backend-specific config)","batch_size integer","num_fewshot integer","output_path string"],"output_types":["JSON results file","Console output with metrics","Optional: W&B/HuggingFace Hub uploads"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_14","uri":"capability://data.processing.analysis.benchmark.suite.composition.and.leaderboard.aggregation","name":"benchmark suite composition and leaderboard aggregation","description":"Enables creation of custom benchmark suites by composing multiple tasks and aggregating their metrics into a single leaderboard score. The system supports weighted aggregation (e.g., MMLU counts more than HellaSwag), per-task metric selection, and hierarchical grouping (e.g., 'reasoning' group contains multiple reasoning tasks). Leaderboard scores are computed with optional normalization and ranking.","intents":["I want to create a custom leaderboard that combines MMLU, HellaSwag, and TruthfulQA with custom weights","I need to aggregate metrics across 50 tasks to create a single overall score","I want to rank models on a leaderboard and track their performance over time"],"best_for":["leaderboard maintainers creating custom benchmark suites","researchers designing evaluation methodologies with multiple tasks","teams comparing models across diverse capabilities"],"limitations":["Weighted aggregation is opinionated; different weight schemes produce different rankings","No built-in validation of weight schemes; biased weights can skew results","Hierarchical grouping can be complex; deep nesting makes aggregation logic hard to follow","Leaderboard scores are not comparable across different suite definitions"],"requires":["List of task names to include in suite","Optional: weights for each task (default: equal weighting)","Optional: metric selection per task (default: primary metric)"],"input_types":["task list (list of task names)","weight dict (task_name -> weight)","aggregation method ('mean', 'weighted_mean', 'harmonic_mean')"],"output_types":["leaderboard score (float)","per-task scores (dict)","ranking (list of models sorted by score)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_2","uri":"capability://data.processing.analysis.few.shot.example.sampling.with.stratification.and.caching","name":"few-shot example sampling with stratification and caching","description":"Implements configurable few-shot sampling strategies that select examples from the training set to include in prompts. Supports random sampling, stratified sampling (balanced across classes), and deterministic seeding for reproducibility. The system caches sampled examples to avoid recomputation and integrates with the request generation pipeline to prepend examples to each evaluation instance. Sampling respects task-specific constraints (e.g., max tokens, example diversity).","intents":["I want to evaluate my model with 5-shot examples to improve performance on a classification task","I need to ensure few-shot examples are balanced across all classes in my dataset","I want reproducible few-shot sampling across multiple evaluation runs"],"best_for":["researchers studying few-shot learning effects on model performance","teams evaluating models on imbalanced datasets where stratified sampling matters","organizations running repeated evaluations and needing deterministic example selection"],"limitations":["Stratified sampling requires labeled data; unsupervised tasks fall back to random sampling","Few-shot examples increase prompt length, which can exceed context windows for long documents or high shot counts","Sampling overhead scales with dataset size; sampling 10 examples from 100k instances requires iterating through full dataset"],"requires":["Task dataset with examples","few_shot_num_shots parameter (integer >= 0)","Optional: class labels for stratified sampling","Random seed for reproducibility"],"input_types":["dataset with examples and optional labels","few_shot_num_shots integer","sampling_strategy string ('random', 'stratified')","random_seed integer"],"output_types":["list of sampled example dicts","cached sampling results (JSON)","prompts with examples prepended"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_3","uri":"capability://data.processing.analysis.loglikelihood.and.text.generation.request.generation.with.batching","name":"loglikelihood and text generation request generation with batching","description":"Generates two types of evaluation requests from task definitions: (1) loglikelihood scoring, which computes the model's probability of correct answers given prompts, and (2) text generation, which samples model outputs and compares them to references. The request generator creates batches of requests optimized for the target model backend, handling tokenization, padding, and attention mask generation. Requests are cached to avoid recomputation across multiple evaluation runs.","intents":["I want to evaluate my model on multiple-choice questions by scoring each option's loglikelihood","I need to generate text completions and compare them against reference answers using BLEU or ROUGE","I want to batch requests efficiently to maximize GPU utilization during evaluation"],"best_for":["researchers evaluating models on classification and generation tasks simultaneously","teams with GPU-constrained environments needing efficient batching","organizations running large-scale evaluations where caching reduces redundant computation"],"limitations":["Loglikelihood scoring requires models to support probability computation; decoder-only models work, but encoder-only models may not","Text generation requests require sampling, which introduces variance; multiple runs needed for stable metrics","Batching adds latency for small request counts; overhead only amortized for batches > 32 requests"],"requires":["Task with defined prompt template and answer format","Model backend supporting loglikelihood or generation","Batch size parameter (integer, typically 8-128)","Optional: generation parameters (temperature, top_p, max_tokens)"],"input_types":["task instance with prompt and reference answers","model backend object","batch_size integer","generation_kwargs dict (temperature, top_p, etc.)"],"output_types":["list of Request objects (loglikelihood or generation type)","batched request groups with tokenized inputs","cached request results (logits, generated text)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_4","uri":"capability://data.processing.analysis.metric.computation.with.bootstrapped.confidence.intervals","name":"metric computation with bootstrapped confidence intervals","description":"Computes evaluation metrics (accuracy, F1, BLEU, ROUGE, etc.) from model predictions and references, with automatic bootstrapped confidence interval calculation. The metrics system supports both built-in metrics (via lm_eval/api/metrics.py) and custom metric functions. Bootstrapping resamples predictions with replacement to estimate metric variance and generate 95% confidence intervals, providing statistical rigor beyond point estimates. Metrics are aggregated at task and suite levels.","intents":["I want to compute accuracy with 95% confidence intervals to assess statistical significance","I need to evaluate text generation using BLEU, ROUGE, and custom metrics in a single pass","I want to aggregate metrics across multiple tasks and report suite-level performance with uncertainty"],"best_for":["researchers publishing results that require confidence intervals for statistical rigor","teams comparing models where uncertainty quantification is critical","organizations evaluating on multiple benchmarks and needing aggregated metrics"],"limitations":["Bootstrapping adds computational overhead (~100-500ms per task depending on sample size and metric complexity)","Confidence intervals assume i.i.d. samples; correlated errors (e.g., systematic model failures) violate this assumption","Custom metrics require Python functions; no declarative metric definition in YAML"],"requires":["Model predictions (logits, generated text, or loglikelihoods)","Reference answers/labels","Metric function (built-in or custom)","Bootstrap sample count (default 100000, configurable)"],"input_types":["predictions list (floats for loglikelihood, strings for generation)","references list (strings or lists of strings)","metric_name string or custom metric function","bootstrap_iters integer"],"output_types":["metric score (float)","confidence interval tuple (lower, upper)","aggregated metrics dict with CI for each metric","per-task and suite-level results"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_5","uri":"capability://automation.workflow.distributed.and.multi.gpu.evaluation.with.automatic.load.balancing","name":"distributed and multi-gpu evaluation with automatic load balancing","description":"Enables evaluation across multiple GPUs and distributed systems using PyTorch's DistributedDataParallel or manual batching strategies. The evaluator automatically partitions tasks across devices, balances load based on task complexity, and aggregates results. Supports both data parallelism (same model on multiple GPUs) and model parallelism (model sharded across GPUs). Caching prevents redundant computation across devices, and results are synchronized before final aggregation.","intents":["I want to evaluate a large model that doesn't fit on a single GPU using tensor parallelism","I need to benchmark multiple tasks in parallel across 8 GPUs to reduce total evaluation time","I want to evaluate a model on a distributed cluster without managing device assignment manually"],"best_for":["teams with multi-GPU setups evaluating large models (70B+ parameters)","organizations running large-scale benchmarks where parallelization reduces wall-clock time","researchers comparing models across different hardware configurations"],"limitations":["Distributed setup requires careful synchronization; race conditions possible if caching not properly locked","Load balancing is static (based on task size); dynamic rebalancing during evaluation not supported","Communication overhead between devices can exceed computation time for small tasks; parallelization only beneficial for large evaluations"],"requires":["Multiple GPUs (2+) or distributed nodes","PyTorch with CUDA support","NCCL backend for distributed communication","num_gpus or num_nodes parameter","Optional: model parallelism configuration (device_map, max_memory)"],"input_types":["task list","model backend object","num_gpus or num_nodes integer","device_map dict for model parallelism","batch_size integer"],"output_types":["aggregated results across all devices","per-device evaluation logs","synchronized metrics with confidence intervals"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_6","uri":"capability://text.generation.language.chat.template.and.multi.turn.prompt.formatting","name":"chat template and multi-turn prompt formatting","description":"Handles formatting of multi-turn conversations using model-specific chat templates (e.g., ChatML, Llama 2 chat format). The system applies templates to convert task prompts into properly formatted chat messages, handling role assignment (user/assistant), special tokens, and message ordering. Templates are loaded from HuggingFace model configs or defined in task YAML. This enables evaluation of instruction-tuned and chat models with their native prompt formats.","intents":["I want to evaluate a chat model using its native chat template instead of raw text prompts","I need to format multi-turn conversations for models like GPT-4 or Llama 2 Chat","I want to test whether models perform better with their intended prompt format vs. generic prompts"],"best_for":["researchers evaluating instruction-tuned and chat models","teams comparing models with different chat template requirements","organizations benchmarking models on conversation-based tasks"],"limitations":["Chat templates vary widely across models; no universal format means task definitions may need model-specific variants","Template application adds tokenization overhead (~10-20ms per prompt); significant for large evaluations","Some models lack official chat templates; fallback to raw prompts may hurt performance"],"requires":["Model with chat_template in config (HuggingFace models) or explicit template definition","Task with prompt and optional multi-turn conversation structure","Tokenizer for the target model","Role definitions (user, assistant, system)"],"input_types":["chat_template string (Jinja2 format)","messages list with role and content","model tokenizer","task prompt string"],"output_types":["formatted prompt string with special tokens","tokenized input with attention masks","conversation history with proper formatting"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_7","uri":"capability://data.processing.analysis.response.filtering.and.answer.extraction.with.regex.and.parsing","name":"response filtering and answer extraction with regex and parsing","description":"Extracts model answers from generated text using task-specific filters and parsers. Supports regex-based extraction, JSON parsing, multiple-choice option selection, and custom Python functions. Filters handle common cases like extracting the first line, finding the last number, or parsing structured outputs. This decouples answer extraction logic from metric computation, allowing flexible handling of different model output formats.","intents":["I want to extract the final answer from a model's reasoning chain (e.g., 'The answer is X')","I need to parse JSON outputs from models and extract specific fields","I want to handle multiple output formats (e.g., 'A', 'Option A', 'The answer is A') as equivalent"],"best_for":["researchers evaluating models on reasoning tasks with verbose outputs","teams handling models that output structured data (JSON, code) requiring parsing","organizations evaluating on tasks where answer format varies across models"],"limitations":["Regex-based extraction is brittle; model outputs that deviate from expected format cause extraction failures","JSON parsing fails if model output is malformed; no automatic repair or fuzzy matching","Custom extraction functions require Python code; no declarative extraction in YAML"],"requires":["Generated text from model","Filter function (regex pattern, JSON parser, or custom function)","Task definition with answer_extraction config","Optional: multiple fallback extraction strategies"],"input_types":["generated_text string","filter_name string or custom function","regex_pattern string (for regex filters)","json_path string (for JSON extraction)"],"output_types":["extracted_answer string","extraction_confidence float (0-1)","fallback_answer if primary extraction fails","extraction_metadata dict with filter used"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_8","uri":"capability://automation.workflow.result.logging.and.persistence.with.multi.sink.support","name":"result logging and persistence with multi-sink support","description":"Logs evaluation results to multiple destinations simultaneously: JSON files, Weights & Biases, HuggingFace Hub, Zeno visualization platform, and custom sinks. The EvaluationTracker (lm_eval/loggers/evaluation_tracker.py) manages result aggregation and formatting for each sink. Results include metrics, confidence intervals, task metadata, model info, and evaluation parameters. Logging is asynchronous to avoid blocking evaluation, and results are cached to enable resumable evaluations.","intents":["I want to log evaluation results to Weights & Biases for experiment tracking and comparison","I need to upload results to the HuggingFace Hub to contribute to the Open LLM Leaderboard","I want to visualize evaluation results interactively using Zeno"],"best_for":["researchers publishing results and needing integration with W&B or HuggingFace Hub","teams tracking experiments across multiple evaluation runs","organizations sharing results publicly via HuggingFace Hub"],"limitations":["Asynchronous logging can cause data loss if process crashes before flushing; no built-in durability guarantees","HuggingFace Hub upload requires authentication and network access; failures are not retried automatically","Zeno visualization requires specific result format; custom metrics may not visualize correctly"],"requires":["EvaluationTracker instance","Optional: W&B API key and project name","Optional: HuggingFace token and repo name","Optional: Zeno API key","Result dict with metrics, model info, and task metadata"],"input_types":["results dict with metrics and metadata","model_name string","task_names list","evaluation_params dict","logger_config dict with sink specifications"],"output_types":["JSON file with results","W&B run with logged metrics","HuggingFace Hub dataset/model card update","Zeno visualization link","Custom sink output (user-defined)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__cap_9","uri":"capability://memory.knowledge.caching.system.with.request.deduplication.and.result.reuse","name":"caching system with request deduplication and result reuse","description":"Implements a multi-level caching system that stores model outputs, loglikelihoods, and metrics to avoid redundant computation. Caches are keyed by model name, task name, and request hash, enabling result reuse across evaluation runs. The system supports both in-memory and disk-based caches, with automatic cache invalidation when task definitions change. Caching is transparent to users and significantly reduces evaluation time for repeated benchmarks.","intents":["I want to re-run evaluation with different metrics without re-computing model outputs","I need to resume an interrupted evaluation without losing progress","I want to compare results across multiple evaluation runs without re-running the model"],"best_for":["researchers iterating on metrics and task definitions","teams running long evaluations that may be interrupted","organizations evaluating the same model on the same tasks multiple times"],"limitations":["Cache invalidation is manual; changing task definitions requires explicit cache clearing","Disk-based caches can consume significant storage (100GB+ for large models); no automatic cleanup","Cache keys are sensitive to tokenizer changes; switching tokenizers invalidates cache"],"requires":["Cache directory (default: ~/.cache/lm_eval/)","Disk space for cache storage","Optional: cache_dir parameter to specify custom location","Optional: no_cache flag to disable caching"],"input_types":["model name string","task name string","request hash (computed from prompt + model config)","model output or loglikelihood"],"output_types":["cached model output (logits, generated text, or loglikelihood)","cache hit/miss indicator","cache metadata (timestamp, model version)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lm-evaluation-harness__headline","uri":"capability://testing.quality.language.model.evaluation.framework","name":"language model evaluation framework","description":"A comprehensive framework for evaluating language models across diverse tasks, supporting over 200 benchmarks and multiple model backends, enabling reproducible and comparable assessments.","intents":["best language model evaluation framework","language model evaluation for research","top benchmarks for language models","how to evaluate language models","language model assessment tools"],"best_for":["research institutions","AI developers","data scientists"],"limitations":["requires Python knowledge","may need GPU for large evaluations"],"requires":["Python","YAML configurations"],"input_types":["language model configurations","evaluation tasks"],"output_types":["evaluation metrics","benchmark results"],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":63,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","Backend-specific dependencies (transformers, vllm, openai, anthropic, etc.)","API keys for cloud-based backends (OpenAI, Anthropic, etc.)","CUDA/GPU drivers if using GPU-accelerated backends","YAML syntax knowledge","Jinja2 template syntax understanding for dynamic prompts","Access to task directory structure (lm_eval/tasks/)","Python 3.9+ for YAML parsing","Task definitions for all suite members","Suite definition (YAML or Python) with task list"],"failure_modes":["Backend-specific features (e.g., vLLM's tensor parallelism) require explicit configuration; abstraction doesn't auto-optimize","API-based models (OpenAI, Anthropic) incur per-token costs; local models don't, creating cost asymmetry in benchmarks","Tokenizer differences between backends can cause slight loglikelihood score variations even for identical models","Complex metric computation (e.g., custom statistical tests) still requires Python; YAML is limited to built-in metrics","Jinja2 templating adds parsing overhead (~5-10ms per task instantiation); not suitable for real-time prompt generation","Task inheritance can create deep dependency chains that are hard to debug if misconfigured","Weighted aggregation requires manual weight specification; no automatic weighting based on task difficulty","Suite-level confidence intervals assume independence between tasks; correlated errors violate this assumption","Large suites (100+ tasks) can take hours to evaluate; no built-in prioritization or early stopping","Custom task implementation requires Python expertise; not accessible to non-programmers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lm-evaluation-harness","compare_url":"https://unfragile.ai/compare?artifact=lm-evaluation-harness"}},"signature":"nccYucH3oe7Ra54WzBzj3HSzYWOxobENxeADjNXzXWAO6WxtjvaR5x5L7X9tYs8JT42QH3mEc+SsE7H98acTCQ==","signedAt":"2026-06-22T00:12:04.116Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lm-evaluation-harness","artifact":"https://unfragile.ai/lm-evaluation-harness","verify":"https://unfragile.ai/api/v1/verify?slug=lm-evaluation-harness","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}