Ollama success

 

🔧 Core Capabilities

  1. Run LLMs Locally
    You can run models like:

    • LLaMA (Meta)

    • Mistral / Mixtral

    • Gemma (Google)

    • Code LLaMA (for programming help)

    • Tiny models for low-resource devices (like llama2:7b or phi)

  2. Interact via Command Line or API

    • Use a simple terminal interface (ollama run llama2)

    • Or use the REST API in your own apps (like building a chatbot, coding assistant, or offline research tool)

  3. Customize Models

    • Fine-tune or create modelfiles, which are lightweight prompt-based customizations (similar to Dockerfiles for LLMs)

  4. Pull + Cache Models

    • Models are downloaded and cached locally

    • You can swap or remove them easily

🧠 Use Cases

  • Offline Chatbots or Agents

  • Private Coding Assistants (via Code LLaMA or other code models)

  • Running Custom Prompts or Role-based Agents

  • Embedding in Apps (with fast local inference)

  • Experiments with prompt engineering or tuning

  • Fast prototyping of AI tools (especially for developers)

💡 Bonus: Integrations & Projects

  • Ollama can work with tools like:

    • LangChain (for building multi-step agents or chains)

    • LM Studio (GUI frontend for local models)

    • Flowise / Autogen / OpenDevin (for building agent frameworks)

    • VS Code (via extensions for local LLM assistance)

⚙️ System Requirements

  • Best performance on machines with a GPU and enough VRAM (e.g., 8–16 GB for 7B models)

  • But can also run quantized models on CPU (slower)

Would you like help setting up a specific model or integrating Ollama with a tool like VS Code or Notion?

You said:
please list all the models
ChatGPT said:

Ollama offers a diverse and extensive library of models tailored for various tasks, including general-purpose language understanding, code generation, vision-language processing, embeddings, and tool integration. Below is an overview of the available models, categorized by their primary functionalities:Ollama

🧠 General-Purpose Language Models

  • LLaMA Series: Includes LLaMA 2 and LLaMA 3 models, ranging from 7B to 70B parameters, suitable for a variety of natural language processing tasks.

  • Mistral Series: Features models like Mistral 7B and Mistral Large 2 (123B), known for their capabilities in code generation, mathematics, and reasoning.

  • Gemma Series: Offers models such as Gemma 2B and Gemma 7B, designed for efficient performance across multiple languages.

  • Phi Series: Includes Phi-2.7B and Phi-4 models, optimized for reasoning tasks.

  • DeepSeek-R1: A model family with sizes up to 671B parameters, combining general and coding abilities.GitHub+3Ollama+3Ollama+3Ollama Operator+1Ollama+1

💻 Code Generation Models

  • Code LLaMA: A family of models (7B, 13B, 34B, and 70B) specialized in code generation, including Python-specific and instruction-following variants.

  • WizardCoder: A 33B parameter model focused on code generation tasks.

  • DeepCoder: Available in 1.5B and 14B sizes, designed for coding applications.

  • Stable Code: A 3B parameter model offering code completion capabilities.Ollama+2arXiv+2Ollama+2Ollama

🖼️ Vision-Language Models

  • LLaVA: Combines a vision encoder with language models like Vicuna for visual and language understanding.

  • LLaVA-LLaMA3: A fine-tuned version of LLaVA based on LLaMA 3.

  • Gemma3 Vision: A series of models (1B to 27B) capable of processing visual information.

  • Qwen2.5-VL: Vision-language models from the Qwen series, ranging from 3B to 72B parameters.

  • Moondream 2: A lightweight 1.8B parameter model designed for edge devices.Ollama Operator+4Ollama+4Ollama+4GitHubGitHub+4Ollama+4Ollama+4

🔧 Tool-Enabled Models

These models support tool calling, enabling them to interact with external functions and APIs:Ollama

  • LLaMA 3.1

  • Mistral Nemo

  • Firefunction v2

  • Command-R+

Tool support allows these models to perform complex tasks by integrating with functions like web browsing, code execution, and more. Ollama

📊 Embedding Models

Embedding models are used to convert text into vector representations for tasks like semantic search and clustering:Ollama

  • BGE Series: Includes BGE-Large and BGE-M3 models.

  • Nomic-Embed-Text: A high-performing open embedding model with a large token context window.

  • Snowflake Arctic Embed: A suite of text embedding models optimized for performance.

  • Granite Embedding: IBM's embedding models available in 30M and 278M sizes.Ollama+2Ollama+2Ollama+2Ollama+1Ollama+1

🧪 Specialized Models

These models are fine-tuned for specific domains or tasks:

  • MedLLaMA2: Adapted from LLaMA 2 for the medical domain.

  • SQLCoder: Fine-tuned for SQL generation tasks.

  • WizardMath: Focused on math and logic problems.

  • Reflection: Trained to detect and correct reasoning mistakes.Ollama

🔍 Accessing the Full Model Library

For a comprehensive list of all available models, including their sizes, capabilities, and tags, you can visit the Ollama Model Library. This resource provides detailed information and allows you to search for models based on specific criteria.GitHub+2GitHub+2Ollama+2

If you're interested in models tailored for a particular application—such as chatbots, code assistance, or vision tasks—feel free to ask, and I can provide more targeted recommendations.

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