From Code to Vision: Discover Ollama’s Powerful AI Models

Artificial Intelligence (AI) models are necessary in various applicationsand Ollama offers easy access to a diverse range of AI models. Each model has a unique function, addressing different needs and use cases. This guide provides more information about the different AI models available for use with Ollama, detailing their specific features, applications, and differences.

Ollam’s Artificial Intelligence Models

Embedding models

Creating embedding models vector embeddingnumerical representations of data. These vectors are key to tasks such as natural language processing and recommendation systems. Embedding models work with vector stores to efficiently store and retrieve these vectors. By converting words or phrases into vectors, embedding models enable machines to understand and process human language more efficiently. Key benefits of embedding models include:

• Better natural language understanding
• Efficient storage and retrieval of data representations
• Better performance on tasks like text classification and similarity analysis

Exploring the different types of AI models available for Ollam

Below you will find a selection of other articles from our extensive library of content that may interest you on the subject of Ollama:

Source models

Source models, also known as general models, are trained on large datasets and serve as the basis of many AI applications. These models include text models and base models that perfectly predict word sequences. While they generate coherent text, they cannot always provide direct answers to specific questions. Source models are essential for tasks that require understanding and generating human-like text, making them versatile tools in the AI ​​toolbox. Some key applications of source models include:

• Generating text
• Language translation
• Sentiment analysis

Tuned models

The models are refined specialized versions of general modelsdesigned to respond to specific inputs. These models include chat models and instruct models. Chat models support casual conversations by enabling more natural and interactive dialogues. Instruct models execute specific instructions, often based on a single message. These models are fine-tuned to perform specific tasks, making them highly effective for targeted applications. Fine-tuned models offer several advantages:

• Improved performance for specific tasks
• More natural and engaging conversational experiences
• The ability to follow precise instructions to achieve intended results

Code Models

Code Models generate code based on the given syntax. These models are similar to tools like GitHub Copilot and can be driven by comments to generate specific code. By understanding the context and requirements of the code, these models help in writing, debugging, and optimizing code. Code models are invaluable to developers because they streamline the coding process and increase productivity. Key benefits of code models include:

• Accelerated code creation
• Improved code quality and consistency
• Debugging and optimization help

Vision models

Vision models are multimodal models that accept both text and images as input. These models can describe aspects of the provided images, making them useful for tasks such as image captioning and visual question answering. Vision models have the potential to accept other modalities in the future, such as video. By integrating multiple types of data, these models offer a more comprehensive understanding of the input, enabling more advanced AI applications. Vision models offer several advantages:

• The ability to process and understand visual information
• Better performance on tasks that require understanding both text and images
• Possibility of future expansion with other data processing methods

In addition to the models currently supported by Ollama, there are other potential models that could be integrated in the future. These include speech-to-text and text-to-speech models. Speech-to-text models convert spoken language to written text, while text-to-speech models do the opposite. These models have a wide range of applications, from virtual assistants to accessibility tools, and could further enhance Ollama’s AI offering. Here is a selection of available AI models, also head over to the official GitHub repository for more information:

• All MiniLM:Embedding models trained on large sentence-level datasets.
• Age 23:A family of multilingual models supporting 23 languages, available in sizes 8B and 35B.
• BakLLaVA:A multimodal model combining the Mistral 7B and LLaVA architectures.
• CodeGeeX4:A model for creating AI software, available in 9B.
• CodeGemma:Lightweight models for coding tasks such as code generation and instruction execution.
• CodeLlama:A large code generation language model, available in 7B, 13B, 34B and 70B versions.
• Code:The first Mistral AI code generation model, 22B.
• R Command:LLM optimized for conversational interaction, available in 35B.
• R+ Command:Scalable LLM for Real-World Enterprise Applications, 104B.
• DeepSeek Developer:Open Source Mixture-of-Experts code model.
• DeepSeek Coder v2:An improved version of DeepSeek, suitable for code-specific tasks.
• Dolphin-Llama 3:A dolphin model based on Llama 3, for learning and coding.
• Dolphin-Mixtral:Finely tuned Mixtral-based models for encoding tasks.
• Dolphin-Mistral:Uncensored 7B model featuring prominent coding.
• Donut:Lightweight models built by Google DeepMind, 2B and 7B.
• Gemma 2:Google’s high-performance model, available in 2B, 9B and 27B versions.
• LLaVA:Large multimodal model combining vision and language, 7B, 13B and 34B.
• LLaVA-Llama 3:Refined LLaVA model from Llama 3 Instruct.
• Llama 2Basic language models from Meta, from 7B to 70B.
• Llama 2 Uncensored:Uncensored version of Llama 2.
• Llama 3:The best publicly available LLM program from Meta, available in versions 8B and 70B.
• Llama 3.1Meta’s most modern model, available in sizes 8B, 70B and 405B.
• Mistral:Model 7B by Mistral AI.
• Mistral-Nemo:The cutting-edge 12B model, built with NVIDIA technology.
• Mistral Little:Lightweight model for translations and summarizations.
• Mixtral:A mix of Expert models with open weights, 8x7B and 8x22B.
• Nomic-Embed-Text:High-performance, open deposition model.
• Nemotron-mini:A small NVIDIA language model optimized for RPGs and RAG QA.
• We Hermes:General purpose models based on Llama and Llama 2, available in 7B and 13B versions.
• My Hermes 2:Powerful models perfect for scientific and coding tasks.
• Orca Mini:General purpose model, suitable for entry-level equipment.
• Phew:Microsoft 2.7B Language Reasoning and Comprehension Model.
• Phi 3:Lightweight Microsoft models, available in sizes 3B and 14B.
• Phi 3.5:Lightweight AI model with 3.8 billion parameters.
• Qwen-1.5:Alibaba Cloud large models, covering from 0.5B to 110B.
• Qwen 2:The new LLM series from Alibaba, available in sizes from 0.5B to 72B.
• Qwen2.5:Pre-trained on Alibaba’s extensive dataset, supporting up to 128k tokens.
• StarCoder:Code generation model, available in sizes from 1B to 15B.
• StarCoder2:Next generation LLM code generation, from 3B to 15B.
• Tiny Llama:Compact 1.1B model trained on 3 trillion tokens.
• Vicuna:General chat model based on Llama and Llama 2.
• EncoderCreator:State-of-the-art code generation model, available in 7B, 13B, 33B and 34B versions.
• Zephyr:Refined Mistral models for support tasks, 7B and 8x22B.

Ollama offers a variety of AI models, each tailored to specific functions and applications. From embedding models that create numerical representations of data to vision models that integrate text and images, these AI models offer powerful tools for a wide range of tasks. Understanding the differences and applications of these models can help you choose the right one for your needs, ensuring you are taking full advantage of the potential of AI technology. For a full list of all currently supported AI models in Ollama, go to the official Models Library website.

Source: Matt Williams

Filed under: AI, Guides

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