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

Artificial Intelligence (AI) models are essential in various applications, and Ollama offers the ability to easily access a diverse range of AI models. Each model serves a unique function, catering to different needs and use cases. This guide provides more insights into the various AI models available for use with Ollama, detailing their specific functions, applications, and differences.

Ollama AI Models

Embedding Models

Embedding models create embedding vectors, numerical representations of data. These vectors are crucial for tasks like natural language processing and recommendation systems. Embedding models work with vector stores to store and retrieve these vectors efficiently. By converting words or phrases into vectors, embedding models enable machines to understand and process human language more effectively. The key benefits of embedding models include:

• Improved natural language understanding
• Efficient storage and retrieval of data representations
• Enhanced performance in tasks like text classification and similarity analysis

Exploring the Different Types of AI Models Available for Ollama

Here are a selection of other articles from our extensive library of content you may find of interest on the subject of Ollama :

Source Models

Source models, also known as general models, are trained on large datasets and serve as the foundation for many AI applications. These models include text models and base models, which excel at predicting sequences of words. While they generate coherent text, they may not 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 toolkit. Some key applications of source models include:

• Text generation
• Language translation
• Sentiment analysis

Fine-Tuned Models

Fine-tuned models are specialized versions of general models, designed to respond to specific inputs. These models include chat models and instruct models. Chat models assist free-form conversations, allowing for more natural and interactive dialogues. Instruct models follow specific instructions, often based on a single prompt. These models are fine-tuned to perform particular tasks, making them highly effective for targeted applications. Fine-tuned models offer several advantages:

• Improved performance on specific tasks
• More natural and engaging conversational experiences
• Ability to follow precise instructions for targeted outputs

Code Models

Code models generate code based on provided syntax. These models are similar to tools like GitHub Copilot and can be directed by comments to generate specific code. By understanding the context and requirements of the code, these models assist in writing, debugging, and optimizing code. Code models are invaluable for developers, as they streamline the coding process and enhance productivity. Key benefits of code models include:

• Accelerated code development
• Improved code quality and consistency
• Assistance in debugging and optimization

Vision Models

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

• Ability to process and understand visual information
• Enhanced performance on tasks that require both text and image understanding
• Potential for future expansion to other data modalities

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 into written text, while text-to-speech models do the reverse. These models have a wide range of applications, from virtual assistants to accessibility tools, and could further enhance the capabilities of Ollama’s AI offerings. Here is a selection of the AI models available also jump over to the official GitHub repository for more info:

• All-MiniLM: Embedding models trained on large sentence-level datasets.
• Aya 23: Multilingual model family supporting 23 languages, available in 8B and 35B sizes.
• BakLLaVA: Multimodal model combining Mistral 7B and LLaVA architecture.
• CodeGeeX4: Model for AI software development, available in 9B.
• CodeGemma: Lightweight models for coding tasks like code generation and instruction following.
• CodeLlama: Large language model for code generation, available in 7B, 13B, 34B, and 70B.
• Codestral: Mistral AI’s first code model for code generation, 22B.
• Command R: LLM optimized for conversational interaction, available in 35B.
• Command R+: Scalable LLM for real-world enterprise use, 104B.
• DeepSeek Coder: Open-source Mixture-of-Experts code model.
• DeepSeek Coder v2: Enhanced version of DeepSeek, excels in code-specific tasks.
• Dolphin-Llama 3: Dolphin model based on Llama 3, for instruction and coding.
• Dolphin-Mixtral: Fine-tuned models based on Mixtral for coding tasks.
• Dolphin-Mistral: Uncensored 7B model excelling at coding.
• Gemma: Lightweight models built by Google DeepMind, 2B and 7B.
• Gemma 2: High-performing model by Google, available in 2B, 9B, and 27B.
• LLaVA: Large multimodal model combining vision and language, 7B, 13B, and 34B.
• LLaVA-Llama 3: Fine-tuned LLaVA model from Llama 3 Instruct.
• Llama 2: Foundational language models from Meta, ranging from 7B to 70B.
• Llama 2 Uncensored: Uncensored version of Llama 2.
• Llama 3: Meta’s most capable openly available LLM, available in 8B and 70B.
• Llama 3.1: Meta’s state-of-the-art model, available in 8B, 70B, and 405B sizes.
• Mistral: 7B model by Mistral AI.
• Mistral-Nemo: State-of-the-art 12B model, built with NVIDIA.
• Mistral Small: Lightweight model for translation and summarization.
• Mixtral: Mixture of Experts models with open weights, 8x7B and 8x22B.
• Nomic-Embed-Text: High-performing open embedding model.
• Nemotron-mini: Small language model by NVIDIA optimized for roleplay and RAG QA.
• Nous Hermes: General-purpose models based on Llama and Llama 2, available in 7B and 13B.
• Nous Hermes 2: Powerful models excelling at scientific and coding tasks.
• Orca Mini: General-purpose model suitable for entry-level hardware.
• Phi: Microsoft’s 2.7B reasoning and language understanding model.
• Phi 3: Lightweight models by Microsoft, available in 3B and 14B.
• Phi 3.5: Lightweight AI model with 3.8 billion parameters.
• Qwen 1.5: Large models by Alibaba Cloud, spanning 0.5B to 110B.
• Qwen 2: New LLM series by Alibaba, available in 0.5B to 72B sizes.
• Qwen 2.5: Pretrained on Alibaba’s large-scale dataset, supporting up to 128K tokens.
• StarCoder: Code generation model, available in 1B to 15B sizes.
• StarCoder2: Next-gen code LLMs for code generation, 3B to 15B.
• TinyLlama: Compact 1.1B model trained on 3 trillion tokens.
• Vicuna: General-use chat model based on Llama and Llama 2.
• WizardCoder: State-of-the-art code generation model, available in 7B, 13B, 33B, and 34B.
• Zephyr: Fine-tuned Mistral models for assistant-like tasks, 7B and 8x22B.

Ollama provides 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 that you use the full potential of AI technology. For a full list of all currently supported AI models on Ollama jump over to the official website Models Library.

Media Credit: Matt Williams

Filed Under: AI, Guides

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