{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"olmo","slug":"olmo","name":"OLMo","type":"model","url":"https://allenai.org/olmo","page_url":"https://unfragile.ai/olmo","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"olmo__cap_0","uri":"capability://text.generation.language.fully.open.transformer.based.language.model.inference.across.multiple.scales","name":"fully open transformer-based language model inference across multiple scales","description":"OLMo provides downloadable, fully open-source transformer model weights in 7B and 32B parameter variants with complete architectural transparency. Users can deploy these models locally or via APIs without proprietary restrictions, with all training code, data, and evaluation artifacts publicly available for reproducibility and modification. The model family includes base, instruction-tuned, and reasoning-focused variants enabling different use cases from raw text generation to multi-turn dialogue.","intents":["Deploy a fully open language model without vendor lock-in or proprietary dependencies","Run inference locally on consumer hardware using the 7B variant","Build production chat applications with the instruction-tuned 32B variant","Conduct reproducible research by accessing complete training data and code","Fine-tune or continue training on custom data using released training infrastructure"],"best_for":["Open-source researchers requiring full transparency and reproducibility","Teams building applications with strict data sovereignty requirements","Solo developers and small teams with limited cloud budgets","Organizations needing to audit model behavior and training data"],"limitations":["Context window length not specified in documentation — maximum sequence length unknown","No quantization formats (GGUF, int8, int4) explicitly documented, limiting deployment on resource-constrained devices","Benchmark performance metrics not provided in public documentation — relative capability vs other open models unclear","Hardware requirements not specified — GPU VRAM and CPU requirements for inference unknown","Inference speed benchmarks unavailable — latency and throughput characteristics not documented"],"requires":["Model weights downloaded from Hugging Face or direct source (32B base ~64GB disk space, 7B ~14GB)","Compatible inference framework (vLLM, llama.cpp, or similar — specific compatibility not documented)","GPU with sufficient VRAM for chosen variant or CPU-only inference capability","Python 3.9+ for training/fine-tuning with OlmoCore framework"],"input_types":["text prompts","multi-turn conversation history","code snippets for programming tasks","mathematical problem statements"],"output_types":["text generation","code generation","reasoning traces (for Think variants)","structured responses (when instruction-tuned)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_1","uri":"capability://text.generation.language.instruction.tuned.multi.turn.dialogue.and.tool.use.capability","name":"instruction-tuned multi-turn dialogue and tool-use capability","description":"OLMo-32B-Instruct and 7B-Instruct variants are post-trained using supervised fine-tuning (SFT) and direct preference optimization (DPO) on instruction-following and dialogue corpora. These models support multi-turn conversation context, tool calling for function invocation, and structured response generation. The instruction tuning pipeline is fully documented and reproducible via the Open Instruct framework, allowing users to understand and modify training data composition.","intents":["Build a chat application with multi-turn conversation memory and context awareness","Enable the model to call external tools and APIs through structured function schemas","Create a customer support chatbot with consistent instruction-following behavior","Fine-tune the instruction-tuned variant on domain-specific dialogue data"],"best_for":["Teams building open-source chatbot applications without cloud dependencies","Researchers studying instruction-tuning and preference optimization techniques","Organizations requiring auditable tool-calling behavior without proprietary function-calling APIs"],"limitations":["Tool-use capability not formally specified — schema format, function registry design, and error handling behavior unknown","No benchmark results comparing instruction-following quality to GPT-4, Claude, or other instruction-tuned models","Multi-turn context handling limits not documented — maximum conversation history length unknown","Preference optimization (DPO) training data composition not detailed — unclear which preference signals were prioritized"],"requires":["OLMo-32B-Instruct or 7B-Instruct model weights downloaded","Inference framework supporting chat template formatting (specific template format not documented)","Open Instruct framework for reproducing or modifying instruction-tuning pipeline","Optional: preference data in DPO format for continued fine-tuning"],"input_types":["natural language instructions","multi-turn conversation history","tool/function schemas (format unspecified)","structured prompts with role definitions"],"output_types":["natural language responses","tool invocation commands (format unspecified)","multi-turn dialogue continuations","structured JSON or function calls"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_10","uri":"capability://text.generation.language.direct.model.weight.download.and.local.deployment","name":"direct model weight download and local deployment","description":"OLMo provides direct download of model weights in standard formats, enabling users to deploy models locally without cloud dependencies or API keys. Model weights are available for all variants (7B, 32B, base, instruct, think) and can be used with standard inference frameworks. This approach provides maximum control, privacy, and reproducibility for deployment.","intents":["Download and deploy OLMo models locally for complete data privacy","Run inference without cloud dependencies or API rate limits","Integrate OLMo into existing ML pipelines and applications","Modify or fine-tune models on custom data using downloaded weights"],"best_for":["Organizations with strict data privacy requirements","Teams building production applications with local inference","Researchers modifying or fine-tuning models","Developers with GPU infrastructure for local deployment"],"limitations":["Model format not specified — whether weights are in safetensors, PyTorch, or other formats unknown","Inference framework compatibility not documented — which frameworks (vLLM, llama.cpp, transformers) are officially supported unknown","Quantization support not documented — no mention of int8, int4, or other quantized variants","Hardware requirements not specified — GPU VRAM, CPU, and storage requirements unknown","Inference optimization guidance not provided — no documentation on batching, caching, or performance tuning"],"requires":["Model weights downloaded from Hugging Face or direct source (32B ~64GB, 7B ~14GB disk space)","Compatible inference framework (vLLM, llama.cpp, Hugging Face transformers, or similar)","GPU with sufficient VRAM or CPU-only inference capability","Python 3.9+ for inference with transformers library"],"input_types":["text prompts","conversation history","code snippets","structured prompts"],"output_types":["text generation","code generation","structured responses","streaming output"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_2","uri":"capability://planning.reasoning.reasoning.focused.model.variants.with.intermediate.thinking.generation","name":"reasoning-focused model variants with intermediate thinking generation","description":"OLMo-32B-Think and 7B-Think variants are trained to generate intermediate reasoning steps before producing final answers, using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL) on reasoning-focused data. These models decompose complex problems into step-by-step reasoning traces, enabling better performance on math, logic, and multi-step reasoning tasks. The thinking training pipeline is fully reproducible via Open Instruct.","intents":["Solve complex math problems by generating step-by-step reasoning traces","Improve accuracy on multi-step logical reasoning tasks through intermediate thinking","Research how chain-of-thought training affects model reasoning capabilities","Build applications requiring transparent, auditable reasoning processes"],"best_for":["Researchers studying reasoning emergence and chain-of-thought training","Teams building math tutoring or technical problem-solving applications","Organizations requiring interpretable reasoning for compliance or audit purposes"],"limitations":["Reasoning trace quality and format not specified — structure of intermediate thinking outputs unknown","No benchmark results on math or reasoning tasks — relative performance vs o1, Claude Opus, or other reasoning models unknown","Inference latency for thinking variants not documented — overhead of generating reasoning traces unknown","RL training details sparse — reward model architecture, RL algorithm (PPO/GRPO), and training stability not documented","Thinking data composition not detailed — ratio of reasoning examples to other training data unknown"],"requires":["OLMo-32B-Think or 7B-Think model weights","Inference framework supporting longer output sequences (reasoning traces add generation length)","Open Instruct framework for reproducing thinking training pipeline","Optional: reasoning-focused training data in SFT or preference format for continued fine-tuning"],"input_types":["math problems","logical reasoning questions","multi-step problem statements","natural language queries requiring step-by-step analysis"],"output_types":["intermediate reasoning traces (format unspecified)","step-by-step solution explanations","final answers with reasoning justification","structured reasoning trees (if supported)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_3","uri":"capability://automation.workflow.reproducible.training.and.fine.tuning.via.olmocore.framework","name":"reproducible training and fine-tuning via olmocore framework","description":"OLMo provides OlmoCore, a fully open training framework enabling users to reproduce the original training runs or fine-tune models on custom data. The framework supports configuration-driven training with documented hyperparameters, data mixing strategies, and training stages (pretraining, mid-training, instruction tuning, DPO, RL). Users can access training code, training data artifacts, and training logs for complete reproducibility and modification.","intents":["Reproduce the original OLMo training run to verify model development and understand training dynamics","Fine-tune OLMo on custom domain-specific data using the documented training pipeline","Experiment with different data mixing ratios, training schedules, and post-training techniques","Conduct research on how training data composition and training stages affect model capabilities"],"best_for":["Research teams with GPU clusters studying language model training","Organizations building domain-specific models by fine-tuning OLMo","ML engineers implementing reproducible training pipelines"],"limitations":["OlmoCore documentation and API reference not provided — specific configuration schema and training options unknown","Distributed training setup requirements not specified — multi-GPU/multi-node configuration complexity unknown","Training cost and time estimates not provided — computational requirements for reproducing full training unknown","Mid-training and post-training data artifacts downloadable but composition details sparse — exact data mixing percentages and sources not fully documented","RL training implementation details minimal — reward model training, RL algorithm specifics, and convergence criteria not documented"],"requires":["Python 3.9+ with PyTorch or similar deep learning framework","GPU cluster with sufficient VRAM for distributed training (specific requirements unknown)","OlmoCore framework installed and configured","Training data in documented format (format specifications not provided)","Optional: wandb or similar logging infrastructure for training monitoring"],"input_types":["training data in text or tokenized format","configuration files specifying training hyperparameters","data mixing specifications","custom instruction/preference data for post-training"],"output_types":["trained model checkpoints","training logs and metrics","evaluation results on benchmark tasks","fine-tuned model weights"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_4","uri":"capability://data.processing.analysis.large.scale.data.deduplication.and.cleaning.via.duplodocus.and.datamap.rs","name":"large-scale data deduplication and cleaning via duplodocus and datamap-rs","description":"OLMo provides Duplodocus, a fuzzy deduplication tool, and Datamap-rs, a large-scale data cleaning utility, as open-source components used in the training pipeline. These tools enable users to preprocess training data at scale, removing duplicates and low-quality examples before training. The tools are designed for web-scale datasets and are fully reproducible, allowing researchers to understand and audit data quality decisions.","intents":["Remove duplicate documents from large web-scale training datasets before model training","Identify and filter low-quality or noisy training examples using data quality metrics","Reproduce the data cleaning steps used in OLMo training for transparency","Apply the same data cleaning methodology to custom training datasets"],"best_for":["Data engineers preparing large-scale training datasets","Researchers studying the impact of data quality on model performance","Teams building custom language models with rigorous data curation"],"limitations":["Duplodocus fuzzy deduplication algorithm details not documented — similarity threshold, hashing approach, and computational complexity unknown","Datamap-rs quality metrics not specified — what constitutes 'low-quality' and how scores are computed unknown","Scalability limits not documented — maximum dataset size and processing time not specified","Integration with OlmoCore training pipeline not detailed — how cleaning results feed into training unknown","No benchmark results showing impact of deduplication/cleaning on model performance"],"requires":["Duplodocus and Datamap-rs tools installed (installation method and dependencies not documented)","Training data in text format (specific format requirements unknown)","Sufficient disk space for processing large datasets","Optional: distributed computing infrastructure for large-scale processing"],"input_types":["raw text documents","web-crawled data","mixed-quality training corpora"],"output_types":["deduplicated document set","quality-filtered training data","data quality metrics and statistics","cleaned datasets ready for training"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_5","uri":"capability://memory.knowledge.training.data.attribution.and.tracing.via.olmotrace","name":"training data attribution and tracing via olmotrace","description":"OLMo provides OlmoTrace, a tool for attributing model outputs and behaviors to specific training examples or data sources. This enables users to trace which training documents influenced particular model predictions, supporting interpretability research and data auditing. The tool works by analyzing model attention patterns and gradient information to identify influential training examples, providing transparency into model decision-making.","intents":["Identify which training documents influenced a specific model prediction or behavior","Audit model training data to understand sources of bias or problematic outputs","Conduct interpretability research on how training data shapes model behavior","Remove or modify specific training examples and measure impact on model outputs"],"best_for":["Interpretability researchers studying model decision-making","Teams auditing models for bias or problematic training data","Organizations requiring data provenance and traceability for compliance"],"limitations":["OlmoTrace methodology not detailed — attribution algorithm (influence functions, attention-based, gradient-based) not specified","Computational cost of attribution not documented — inference overhead and memory requirements unknown","Attribution accuracy not benchmarked — how well traces correlate with actual model behavior unknown","Scalability to full training dataset not documented — maximum dataset size for tracing unknown","Integration with OlmoCore and inference frameworks not detailed"],"requires":["OlmoTrace tool installed (installation method and dependencies not documented)","Trained OLMo model checkpoint with attention/gradient information preserved","Access to original training data for attribution matching","Sufficient computational resources for attribution analysis (GPU recommended, specifics unknown)"],"input_types":["model predictions or outputs","model checkpoints with intermediate representations","training dataset","queries or prompts to trace"],"output_types":["ranked list of influential training examples","attribution scores or weights","data source provenance information","influence analysis reports"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_6","uri":"capability://data.processing.analysis.reproducible.evaluation.via.olmes.benchmark.suite","name":"reproducible evaluation via olmes benchmark suite","description":"OLMo provides OLMES, a reproducible evaluation utility for assessing model performance on standardized benchmarks. OLMES enables users to evaluate OLMo models (or other models) on consistent, documented evaluation protocols, supporting research reproducibility and fair model comparison. The evaluation framework is fully open-source and includes benchmark datasets, evaluation scripts, and metric computation.","intents":["Evaluate OLMo model performance on standard benchmarks in a reproducible manner","Compare OLMo variants (7B, 32B, base, instruct, think) on consistent metrics","Benchmark custom fine-tuned models using the same evaluation protocol","Conduct research on how training data and training techniques affect benchmark performance"],"best_for":["Researchers comparing language models on standardized benchmarks","Teams evaluating fine-tuned OLMo variants before deployment","Organizations requiring reproducible evaluation protocols for model selection"],"limitations":["Specific benchmarks included in OLMES not detailed — which tasks/datasets are evaluated unknown","Evaluation metrics and scoring methodology not documented — how performance is computed unknown","Benchmark results for OLMo variants not provided in public documentation — no published leaderboard or comparison table","Evaluation cost and time estimates not provided — computational requirements for full evaluation unknown","No comparison to other evaluation frameworks (lm-eval-harness, HELM, etc.)"],"requires":["OLMES evaluation framework installed","OLMo model checkpoint or other model to evaluate","Benchmark datasets (included with OLMES or downloaded separately)","Inference capability for the model being evaluated","Optional: GPU for faster evaluation (specific requirements unknown)"],"input_types":["trained model checkpoints","benchmark task specifications","evaluation configuration files"],"output_types":["benchmark scores and metrics","performance reports","comparative analysis across model variants","detailed evaluation logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_7","uri":"capability://data.processing.analysis.test.set.contamination.detection.via.decon","name":"test set contamination detection via decon","description":"OLMo provides Decon, a tool for detecting and removing test set contamination from training data. This tool identifies training examples that overlap with evaluation benchmarks, preventing inflated performance metrics and ensuring fair model evaluation. Decon enables users to audit training data for benchmark contamination and remove problematic examples before training.","intents":["Detect whether training data contains examples from standard evaluation benchmarks","Remove test set contamination from training data to ensure fair evaluation","Audit custom training datasets for overlap with public benchmarks","Verify that model performance improvements are genuine and not due to data contamination"],"best_for":["Researchers ensuring evaluation integrity and reproducibility","Teams preparing training data for publication or peer review","Organizations building models with strict evaluation standards"],"limitations":["Decon detection methodology not documented — similarity matching approach and contamination threshold unknown","Supported benchmark datasets not specified — which evaluation sets can be checked unknown","Detection accuracy not benchmarked — false positive/negative rates unknown","Scalability to large training datasets not documented","No published analysis of contamination found in OLMo training data"],"requires":["Decon tool installed (installation method and dependencies not documented)","Training data in text format","Benchmark datasets to check against (included with Decon or provided separately)","Sufficient disk space for contamination analysis"],"input_types":["training dataset","benchmark evaluation sets","contamination detection configuration"],"output_types":["contamination detection report","list of contaminated training examples","cleaned training dataset with contamination removed","contamination statistics and analysis"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_8","uri":"capability://automation.workflow.collaborative.distributed.training.via.flexolmo.paradigm","name":"collaborative distributed training via flexolmo paradigm","description":"OLMo provides FlexOlmo, a collaborative training paradigm enabling distributed training across multiple organizations or compute providers. FlexOlmo allows participants to contribute compute resources and data to jointly train models, with transparent accounting of contributions and fair reward distribution. This approach enables resource-constrained teams to participate in large-scale model training.","intents":["Participate in collaborative model training without owning a full GPU cluster","Contribute compute resources to shared training runs and receive model access","Build models with data from multiple organizations while maintaining privacy","Research distributed training and incentive mechanisms for collaborative AI development"],"best_for":["Organizations with spare compute capacity wanting to contribute to model training","Research teams studying collaborative and federated learning approaches","Communities building models through distributed contribution"],"limitations":["FlexOlmo implementation details not documented — architecture, communication protocol, and contribution accounting unknown","Incentive mechanism and reward distribution not specified — how contributions are valued and compensated unknown","Privacy guarantees not documented — data privacy and security during collaborative training unknown","Scalability and fault tolerance not detailed — handling of node failures and network issues unknown","No published results or case studies of FlexOlmo deployments"],"requires":["FlexOlmo framework installed and configured","GPU or compute resources to contribute (specifications unknown)","Network connectivity to collaborative training infrastructure","Optional: data to contribute to training (format and privacy requirements unknown)"],"input_types":["compute resource specifications","training data (optional)","collaborative training configuration"],"output_types":["trained model access","contribution credits or rewards","training progress reports","model checkpoints from collaborative training"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__cap_9","uri":"capability://text.generation.language.web.based.chat.interface.for.model.interaction","name":"web-based chat interface for model interaction","description":"OLMo provides a web-based chat interface ('Chat with Olmo') enabling users to interact with OLMo models through a browser without local setup or API keys. The interface supports multi-turn conversation, streaming responses, and real-time interaction. This provides an accessible entry point for non-technical users and researchers to explore model capabilities.","intents":["Explore OLMo model capabilities through an interactive chat interface","Test model behavior on custom prompts without local setup","Conduct qualitative evaluation of instruction-following and reasoning abilities","Share model interactions with collaborators through shareable chat links"],"best_for":["Non-technical users exploring language model capabilities","Researchers conducting qualitative model evaluation","Teams demonstrating model capabilities to stakeholders"],"limitations":["Chat interface hosting and infrastructure not documented — availability, uptime, and rate limits unknown","Model selection not specified — which OLMo variants (7B, 32B, base, instruct, think) are available unknown","Response latency and throughput not documented","Privacy and data retention policies not specified — whether conversations are logged or used for training unknown","No API access documented — interface appears to be web-only without programmatic access"],"requires":["Web browser with internet connectivity","No API key or local setup required","Optional: account creation (requirements unknown)"],"input_types":["natural language prompts","multi-turn conversation history","follow-up questions and clarifications"],"output_types":["natural language responses","streaming text generation","conversation history"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"olmo__headline","uri":"capability://text.generation.language.open.language.model.for.reproducible.research","name":"open language model for reproducible research","description":"OLMo is a fully open language model designed to advance open science in language modeling, providing complete training data, code, and weights for transparent and reproducible research.","intents":["best open language model","open language model for research","fully open language model","language model with complete training data","reproducible research language model"],"best_for":["research purposes","academic use","open-source projects"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Model weights downloaded from Hugging Face or direct source (32B base ~64GB disk space, 7B ~14GB)","Compatible inference framework (vLLM, llama.cpp, or similar — specific compatibility not documented)","GPU with sufficient VRAM for chosen variant or CPU-only inference capability","Python 3.9+ for training/fine-tuning with OlmoCore framework","OLMo-32B-Instruct or 7B-Instruct model weights downloaded","Inference framework supporting chat template formatting (specific template format not documented)","Open Instruct framework for reproducing or modifying instruction-tuning pipeline","Optional: preference data in DPO format for continued fine-tuning","Model weights downloaded from Hugging Face or direct source (32B ~64GB, 7B ~14GB disk space)","Compatible inference framework (vLLM, llama.cpp, Hugging Face transformers, or similar)"],"failure_modes":["Context window length not specified in documentation — maximum sequence length unknown","No quantization formats (GGUF, int8, int4) explicitly documented, limiting deployment on resource-constrained devices","Benchmark performance metrics not provided in public documentation — relative capability vs other open models unclear","Hardware requirements not specified — GPU VRAM and CPU requirements for inference unknown","Inference speed benchmarks unavailable — latency and throughput characteristics not documented","Tool-use capability not formally specified — schema format, function registry design, and error handling behavior unknown","No benchmark results comparing instruction-following quality to GPT-4, Claude, or other instruction-tuned models","Multi-turn context handling limits not documented — maximum conversation history length unknown","Preference optimization (DPO) training data composition not detailed — unclear which preference signals were prioritized","Model format not specified — whether weights are in safetensors, PyTorch, or other formats unknown","builder identity is not verified yet","no observed match outcomes 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