Free LLM Models Directory
Discover free and open-source large language models for text generation, code completion, and embeddings.
The Ultimate Free LLM Models Directory
Welcome to the most comprehensive directory of free and open-source LLM models. Whether you're a developer, researcher, or AI enthusiast, our curated collection helps you discover powerful language models that you can run locally, fine-tune, or integrate into your applications.
Our directory features models from leading providers including Meta's Llama 2, Mistral AI, Microsoft's Phi, and hundreds more on Hugging Face. All models are verified to have permissive licenses allowing commercial use.
From text generation and code completion to embeddings and multimodal models - find everything you need to build the next generation of AI applications!
Showing 481-528 of 576 models
xwinlm
LLM Models
xwinlm is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
granite3-moe
LLM Models
granite3-moe is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
yarn-llama2
LLM Models
yarn-llama2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
orca2
LLM Models
orca2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
stable-beluga
LLM Models
stable-beluga is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
reader-lm
LLM Models
reader-lm is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
shieldgemma
LLM Models
shieldgemma is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
deepcoder
LLM Models
deepcoder is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
llama-pro
LLM Models
llama-pro is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
yarn-mistral
LLM Models
yarn-mistral is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
paraphrase-multilingual
LLM Models
paraphrase-multilingual is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
nexusraven
LLM Models
nexusraven is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
devstral-small-2
LLM Models
devstral-small-2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
bakllava
LLM Models
bakllava is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
wizardlm
LLM Models
wizardlm is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
command-r-plus
LLM Models
command-r-plus is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mistral-small3.1
LLM Models
mistral-small3.1 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
exaone-deep
LLM Models
exaone-deep is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
meditron
LLM Models
meditron is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
deepseek-v3.1
LLM Models
deepseek-v3.1 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
tinydolphin
LLM Models
tinydolphin is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
nemotron-mini
LLM Models
nemotron-mini is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
codegeex4
LLM Models
codegeex4 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mistral-openorca
LLM Models
mistral-openorca is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
wizardlm-uncensored
LLM Models
wizardlm-uncensored is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
nemotron3
LLM Models
nemotron3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
opencoder
LLM Models
opencoder is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
reflection
LLM Models
reflection is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
nemotron
LLM Models
nemotron is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
codeup
LLM Models
codeup is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
athene-v2
LLM Models
athene-v2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
nous-hermes2-mixtral
LLM Models
nous-hermes2-mixtral is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen3-next
LLM Models
qwen3-next is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
megadolphin
LLM Models
megadolphin is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
medllama2
LLM Models
medllama2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
everythinglm
LLM Models
everythinglm is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
solar-pro
LLM Models
solar-pro is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
magicoder
LLM Models
magicoder is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mathstral
LLM Models
mathstral is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
notus
LLM Models
notus is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
notux
LLM Models
notux is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
falcon2
LLM Models
falcon2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
stablelm-zephyr
LLM Models
stablelm-zephyr is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
nuextract
LLM Models
nuextract is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
exaone3.5
LLM Models
exaone3.5 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
duckdb-nsql
LLM Models
duckdb-nsql is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
bespoke-minicheck
LLM Models
bespoke-minicheck is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mistrallite
LLM Models
mistrallite is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
Frequently Asked Questions About LLM Models
What are LLM models and how do they work?
Large Language Models (LLMs) are AI models trained on vast amounts of text data to understand and generate human-like text. They use transformer architecture and can perform tasks like text generation, translation, summarization, and code completion.
Which LLM models are free to use?
Many LLM models are freely available including Llama 2, Mistral, Mixtral, Phi-2, and various models on Hugging Face. Our directory features only models with permissive licenses that allow commercial use.
How do I run LLM models locally?
You can run LLMs locally using tools like Ollama, LM Studio, or text-generation-webui. Most models require significant GPU memory (8GB+ VRAM recommended). Smaller models like Phi-2 and Llama 2 7B can run on consumer hardware.
What's the difference between open-source and proprietary LLMs?
Open-source LLMs like Llama 2 and Mistral have publicly available weights and can be run locally or fine-tuned. Proprietary models like GPT-4 are only accessible via API. Open-source models offer more control and privacy.
Can I use these LLM models for commercial projects?
Most models in our directory have permissive licenses (Apache 2.0, MIT) allowing commercial use. Always check the specific model's license on Hugging Face or the provider's website before using in production.