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 433-480 of 576 models
granite4
LLM Models
granite4 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.
lfm2.5-thinking
LLM Models
lfm2.5-thinking 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.
zephyr
LLM Models
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.
mistral-large
LLM Models
mistral-large 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.
wizard-vicuna-uncensored
LLM Models
wizard-vicuna-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.
ministral-3
LLM Models
ministral-3 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.
phi4-mini
LLM Models
phi4-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.
openthinker
LLM Models
openthinker 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.
glm4
LLM Models
glm4 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.
wizardlm2
LLM Models
wizardlm2 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.
starcoder
LLM Models
starcoder 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-v2
LLM Models
deepseek-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-hermes
LLM Models
nous-hermes 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-llm
LLM Models
deepseek-llm 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.
lfm2
LLM Models
lfm2 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.
openchat
LLM Models
openchat 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.
falcon
LLM Models
falcon 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.
vicuna
LLM Models
vicuna 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.
codeqwen
LLM Models
codeqwen 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.
openhermes
LLM Models
openhermes 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.
qwen2-math
LLM Models
qwen2-math 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.
aya
LLM Models
aya 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.
granite3.3
LLM Models
granite3.3 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.
neural-chat
LLM Models
neural-chat 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.
llama2-chinese
LLM Models
llama2-chinese 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-code
LLM Models
stable-code 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
LLM Models
nous-hermes2 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.
sqlcoder
LLM Models
sqlcoder 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.
wizardcoder
LLM Models
wizardcoder 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.
yi-coder
LLM Models
yi-coder 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.
stablelm2
LLM Models
stablelm2 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-dense
LLM Models
granite3-dense 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.
llama3-chatqa
LLM Models
llama3-chatqa 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.
granite3.1-dense
LLM Models
granite3.1-dense 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-guard3
LLM Models
llama-guard3 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.
dolphincoder
LLM Models
dolphincoder 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
LLM Models
devstral 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.
wizard-math
LLM Models
wizard-math 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.
llama3-gradient
LLM Models
llama3-gradient 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.
phi3.5
LLM Models
phi3.5 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.
samantha-mistral
LLM Models
samantha-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.
llama3-groq-tool-use
LLM Models
llama3-groq-tool-use 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.
internlm2
LLM Models
internlm2 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.
aya-expanse
LLM Models
aya-expanse 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.
starling-lm
LLM Models
starling-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.
solar
LLM Models
solar 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.
granite3.2-vision
LLM Models
granite3.2-vision 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.
phind-codellama
LLM Models
phind-codellama 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.