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.github/workflows/test.yaml
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.github/workflows/test.yaml
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@ -281,7 +281,7 @@ jobs:
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shell: bash
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- uses: golangci/golangci-lint-action@v6
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with:
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args: --timeout 10m0s -v
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args: --timeout 8m0s -v
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test:
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strategy:
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matrix:
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108
README.md
108
README.md
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@ -47,28 +47,26 @@ Ollama supports a list of models available on [ollama.com/library](https://ollam
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Here are some example models that can be downloaded:
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| Model | Parameters | Size | Download |
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| ------------------ | ---------- | ----- | -------------------------------- |
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| Llama 3.2 | 3B | 2.0GB | `ollama run llama3.2` |
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| Llama 3.2 | 1B | 1.3GB | `ollama run llama3.2:1b` |
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| Llama 3.2 Vision | 11B | 7.9GB | `ollama run llama3.2-vision` |
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| Llama 3.2 Vision | 90B | 55GB | `ollama run llama3.2-vision:90b` |
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| Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` |
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| Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` |
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| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` |
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| Phi 3 Mini | 3.8B | 2.3GB | `ollama run phi3` |
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| Phi 3 Medium | 14B | 7.9GB | `ollama run phi3:medium` |
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| Gemma 2 | 2B | 1.6GB | `ollama run gemma2:2b` |
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| Gemma 2 | 9B | 5.5GB | `ollama run gemma2` |
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| Gemma 2 | 27B | 16GB | `ollama run gemma2:27b` |
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| Mistral | 7B | 4.1GB | `ollama run mistral` |
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| Moondream 2 | 1.4B | 829MB | `ollama run moondream` |
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| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` |
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| Starling | 7B | 4.1GB | `ollama run starling-lm` |
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| Code Llama | 7B | 3.8GB | `ollama run codellama` |
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| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
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| LLaVA | 7B | 4.5GB | `ollama run llava` |
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| Solar | 10.7B | 6.1GB | `ollama run solar` |
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| Model | Parameters | Size | Download |
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| ------------------ | ---------- | ----- | ------------------------------ |
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| Llama 3.2 | 3B | 2.0GB | `ollama run llama3.2` |
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| Llama 3.2 | 1B | 1.3GB | `ollama run llama3.2:1b` |
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| Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` |
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| Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` |
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| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` |
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| Phi 3 Mini | 3.8B | 2.3GB | `ollama run phi3` |
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| Phi 3 Medium | 14B | 7.9GB | `ollama run phi3:medium` |
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| Gemma 2 | 2B | 1.6GB | `ollama run gemma2:2b` |
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| Gemma 2 | 9B | 5.5GB | `ollama run gemma2` |
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| Gemma 2 | 27B | 16GB | `ollama run gemma2:27b` |
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| Mistral | 7B | 4.1GB | `ollama run mistral` |
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| Moondream 2 | 1.4B | 829MB | `ollama run moondream` |
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| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` |
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| Starling | 7B | 4.1GB | `ollama run starling-lm` |
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| Code Llama | 7B | 3.8GB | `ollama run codellama` |
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| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
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| LLaVA | 7B | 4.5GB | `ollama run llava` |
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| Solar | 10.7B | 6.1GB | `ollama run solar` |
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> [!NOTE]
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> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
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@ -298,7 +296,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
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- [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
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- [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
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- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
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- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Chat with Code Repository)
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- [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
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- [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
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- [RAGFlow](https://github.com/infiniflow/ragflow) (Open-source Retrieval-Augmented Generation engine based on deep document understanding)
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@ -308,17 +306,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG)
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- [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation)
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- [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
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- [RWKV-Runner](https://github.com/josStorer/RWKV-Runner) (RWKV offline LLM deployment tool, also usable as a client for ChatGPT and Ollama)
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- [Ollama Grid Search](https://github.com/dezoito/ollama-grid-search) (app to evaluate and compare models)
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- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
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- [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
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- [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
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- [Shinkai Desktop](https://github.com/dcSpark/shinkai-apps) (Two click install Local AI using Ollama + Files + RAG)
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- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in discord )
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- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
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- [R2R](https://github.com/SciPhi-AI/R2R) (Open-source RAG engine)
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- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy to use GUI with sample custom LLM for Drivers Education)
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- [OpenGPA](https://opengpa.org) (Open-source offline-first Enterprise Agentic Application)
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- [Painting Droid](https://github.com/mateuszmigas/painting-droid) (Painting app with AI integrations)
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- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
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- [AI Studio](https://github.com/MindWorkAI/AI-Studio)
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@ -326,8 +318,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
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- [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
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- [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
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- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
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- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
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- [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
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- [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
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- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
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@ -337,34 +327,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
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- [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
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- [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
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- [Tkinter-based client](https://github.com/chyok/ollama-gui) (Python tkinter-based Client for Ollama)
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- [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
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- [Local Multimodal AI Chat](https://github.com/Leon-Sander/Local-Multimodal-AI-Chat) (Ollama-based LLM Chat with support for multiple features, including PDF RAG, voice chat, image-based interactions, and integration with OpenAI.)
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- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG on Mac/Windows/Linux)
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- [OrionChat](https://github.com/EliasPereirah/OrionChat) - OrionChat is a web interface for chatting with different AI providers
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- [G1](https://github.com/bklieger-groq/g1) (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
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- [Web management](https://github.com/lemonit-eric-mao/ollama-web-management) (Web management page)
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- [Promptery](https://github.com/promptery/promptery) (desktop client for Ollama.)
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- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
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- [SpaceLlama](https://github.com/tcsenpai/spacellama) (Firefox and Chrome extension to quickly summarize web pages with ollama in a sidebar)
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- [YouLama](https://github.com/tcsenpai/youlama) (Webapp to quickly summarize any YouTube video, supporting Invidious as well)
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- [DualMind](https://github.com/tcsenpai/dualmind) (Experimental app allowing two models to talk to each other in the terminal or in a web interface)
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- [ollamarama-matrix](https://github.com/h1ddenpr0cess20/ollamarama-matrix) (Ollama chatbot for the Matrix chat protocol)
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- [ollama-chat-app](https://github.com/anan1213095357/ollama-chat-app) (Flutter-based chat app)
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- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard and said in the meetings)
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- [Hexabot](https://github.com/hexastack/hexabot) (A conversational AI builder)
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- [Reddit Rate](https://github.com/rapidarchitect/reddit_analyzer) (Search and Rate Reddit topics with a weighted summation)
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- [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
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- [VT](https://github.com/vinhnx/vt.ai) (A minimal multimodal AI chat app, with dynamic conversation routing. Supports local models via Ollama)
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- [Nosia](https://github.com/nosia-ai/nosia) (Easy to install and use RAG platform based on Ollama)
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- [Witsy](https://github.com/nbonamy/witsy) (An AI Desktop application avaiable for Mac/Windows/Linux)
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- [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
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### Cloud
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- [Google Cloud](https://cloud.google.com/run/docs/tutorials/gpu-gemma2-with-ollama)
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- [Fly.io](https://fly.io/docs/python/do-more/add-ollama/)
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- [Koyeb](https://www.koyeb.com/deploy/ollama)
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- [Reddit Rate]((https://github.com/rapidarchitect/reddit_analyzer)) (Search and Rate Reddit topics with a weighted summation)
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### Terminal
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@ -380,7 +348,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [Oatmeal](https://github.com/dustinblackman/oatmeal)
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- [cmdh](https://github.com/pgibler/cmdh)
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- [ooo](https://github.com/npahlfer/ooo)
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- [shell-pilot](https://github.com/reid41/shell-pilot)(Interact with models via pure shell scripts on Linux or macOS)
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- [shell-pilot](https://github.com/reid41/shell-pilot)
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- [tenere](https://github.com/pythops/tenere)
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- [llm-ollama](https://github.com/taketwo/llm-ollama) for [Datasette's LLM CLI](https://llm.datasette.io/en/stable/).
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- [typechat-cli](https://github.com/anaisbetts/typechat-cli)
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@ -388,19 +356,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [tlm](https://github.com/yusufcanb/tlm)
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- [podman-ollama](https://github.com/ericcurtin/podman-ollama)
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- [gollama](https://github.com/sammcj/gollama)
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- [ParLlama](https://github.com/paulrobello/parllama)
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- [Ollama eBook Summary](https://github.com/cognitivetech/ollama-ebook-summary/)
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- [Ollama Mixture of Experts (MOE) in 50 lines of code](https://github.com/rapidarchitect/ollama_moe)
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- [vim-intelligence-bridge](https://github.com/pepo-ec/vim-intelligence-bridge) Simple interaction of "Ollama" with the Vim editor
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- [x-cmd ollama](https://x-cmd.com/mod/ollama)
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- [bb7](https://github.com/drunkwcodes/bb7)
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- [SwollamaCLI](https://github.com/marcusziade/Swollama) bundled with the Swollama Swift package. [Demo](https://github.com/marcusziade/Swollama?tab=readme-ov-file#cli-usage)
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- [aichat](https://github.com/sigoden/aichat) All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI tools & agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
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- [PowershAI](https://github.com/rrg92/powershai) PowerShell module that brings AI to terminal on Windows, including support for Ollama
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- [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
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### Apple Vision Pro
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- [Enchanted](https://github.com/AugustDev/enchanted)
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### Database
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@ -422,11 +382,9 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
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- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
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- [crewAI](https://github.com/crewAIInc/crewAI)
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- [Spring AI](https://github.com/spring-projects/spring-ai) with [reference](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html) and [example](https://github.com/tzolov/ollama-tools)
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- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
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- [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
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- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
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- [LLPhant](https://github.com/theodo-group/LLPhant?tab=readme-ov-file#ollama)
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- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
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- [LiteLLM](https://github.com/BerriAI/litellm)
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- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
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@ -451,20 +409,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama)
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- [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
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- [LlamaScript](https://github.com/Project-Llama/llamascript)
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- [llm-axe](https://github.com/emirsahin1/llm-axe) (Python Toolkit for Building LLM Powered Apps)
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- [Gollm](https://docs.gollm.co/examples/ollama-example)
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- [Gollama for Golang](https://github.com/jonathanhecl/gollama)
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- [Ollamaclient for Golang](https://github.com/xyproto/ollamaclient)
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- [High-level function abstraction in Go](https://gitlab.com/tozd/go/fun)
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- [Ollama PHP](https://github.com/ArdaGnsrn/ollama-php)
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- [Agents-Flex for Java](https://github.com/agents-flex/agents-flex) with [example](https://github.com/agents-flex/agents-flex/tree/main/agents-flex-llm/agents-flex-llm-ollama/src/test/java/com/agentsflex/llm/ollama)
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- [Parakeet](https://github.com/parakeet-nest/parakeet) is a GoLang library, made to simplify the development of small generative AI applications with Ollama.
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- [Haverscript](https://github.com/andygill/haverscript) with [examples](https://github.com/andygill/haverscript/tree/main/examples)
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- [Ollama for Swift](https://github.com/mattt/ollama-swift)
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- [Swollama for Swift](https://github.com/marcusziade/Swollama) with [DocC](https://marcusziade.github.io/Swollama/documentation/swollama/)
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- [GoLamify](https://github.com/prasad89/golamify)
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- [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
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- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
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### Mobile
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@ -478,7 +428,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama)
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- [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel)
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- [Continue](https://github.com/continuedev/continue)
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- [Vibe](https://github.com/thewh1teagle/vibe) (Transcribe and analyze meetings with Ollama)
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- [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama)
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- [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq)
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- [NotesOllama](https://github.com/andersrex/notesollama) (Apple Notes Ollama plugin)
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@ -501,24 +450,15 @@ See the [API documentation](./docs/api.md) for all endpoints.
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- [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
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- [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
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- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
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- [ChatGPTBox: All in one browser extension](https://github.com/josStorer/chatGPTBox) with [Integrating Tutorial](https://github.com/josStorer/chatGPTBox/issues/616#issuecomment-1975186467)
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- [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
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- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
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- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.)
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- [node-red-contrib-ollama](https://github.com/jakubburkiewicz/node-red-contrib-ollama)
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- [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
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- [vnc-lm](https://github.com/jake83741/vnc-lm) (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)
|
||||
- [vnc-lm](https://github.com/jk011ru/vnc-lm) (A containerized Discord bot with support for attachments and web links)
|
||||
- [LSP-AI](https://github.com/SilasMarvin/lsp-ai) (Open-source language server for AI-powered functionality)
|
||||
- [QodeAssist](https://github.com/Palm1r/QodeAssist) (AI-powered coding assistant plugin for Qt Creator)
|
||||
- [Obsidian Quiz Generator plugin](https://github.com/ECuiDev/obsidian-quiz-generator)
|
||||
- [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
|
||||
- [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
|
||||
|
||||
### Supported backends
|
||||
|
||||
- [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.
|
||||
|
||||
### Observability
|
||||
|
||||
- [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
|
||||
- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ func checkError(resp *http.Response, body []byte) error {
|
|||
|
||||
// ClientFromEnvironment creates a new [Client] using configuration from the
|
||||
// environment variable OLLAMA_HOST, which points to the network host and
|
||||
// port on which the ollama service is listening. The format of this variable
|
||||
// port on which the ollama service is listenting. The format of this variable
|
||||
// is:
|
||||
//
|
||||
// <scheme>://<host>:<port>
|
||||
|
|
|
|||
13
api/types.go
13
api/types.go
|
|
@ -12,7 +12,7 @@ import (
|
|||
"time"
|
||||
)
|
||||
|
||||
// StatusError is an error with an HTTP status code and message.
|
||||
// StatusError is an error with and HTTP status code.
|
||||
type StatusError struct {
|
||||
StatusCode int
|
||||
Status string
|
||||
|
|
@ -57,7 +57,7 @@ type GenerateRequest struct {
|
|||
Template string `json:"template"`
|
||||
|
||||
// Context is the context parameter returned from a previous call to
|
||||
// [Client.Generate]. It can be used to keep a short conversational memory.
|
||||
// Generate call. It can be used to keep a short conversational memory.
|
||||
Context []int `json:"context,omitempty"`
|
||||
|
||||
// Stream specifies whether the response is streaming; it is true by default.
|
||||
|
|
@ -90,14 +90,14 @@ type ChatRequest struct {
|
|||
// Messages is the messages of the chat - can be used to keep a chat memory.
|
||||
Messages []Message `json:"messages"`
|
||||
|
||||
// Stream enables streaming of returned responses; true by default.
|
||||
// Stream enable streaming of returned response; true by default.
|
||||
Stream *bool `json:"stream,omitempty"`
|
||||
|
||||
// Format is the format to return the response in (e.g. "json").
|
||||
Format string `json:"format"`
|
||||
|
||||
// KeepAlive controls how long the model will stay loaded into memory
|
||||
// following the request.
|
||||
// followin the request.
|
||||
KeepAlive *Duration `json:"keep_alive,omitempty"`
|
||||
|
||||
// Tools is an optional list of tools the model has access to.
|
||||
|
|
@ -146,7 +146,6 @@ type ToolCall struct {
|
|||
}
|
||||
|
||||
type ToolCallFunction struct {
|
||||
Index int `json:"index,omitempty"`
|
||||
Name string `json:"name"`
|
||||
Arguments ToolCallFunctionArguments `json:"arguments"`
|
||||
}
|
||||
|
|
@ -204,8 +203,8 @@ type Metrics struct {
|
|||
EvalDuration time.Duration `json:"eval_duration,omitempty"`
|
||||
}
|
||||
|
||||
// Options specified in [GenerateRequest]. If you add a new option here, also
|
||||
// add it to the API docs.
|
||||
// Options specified in [GenerateRequest], if you add a new option here add it
|
||||
// to the API docs also.
|
||||
type Options struct {
|
||||
Runner
|
||||
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ func initStore() {
|
|||
slog.Debug(fmt.Sprintf("unexpected error searching for store: %s", err))
|
||||
}
|
||||
slog.Debug("initializing new store")
|
||||
store.ID = uuid.NewString()
|
||||
store.ID = uuid.New().String()
|
||||
writeStore(getStorePath())
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ func (t *winTray) UpdateAvailable(ver string) error {
|
|||
if err := t.addOrUpdateMenuItem(updateAvailableMenuID, 0, updateAvailableMenuTitle, true); err != nil {
|
||||
return fmt.Errorf("unable to create menu entries %w", err)
|
||||
}
|
||||
if err := t.addOrUpdateMenuItem(updateMenuID, 0, updateMenuTitle, false); err != nil {
|
||||
if err := t.addOrUpdateMenuItem(updateMenuID, 0, updateMenutTitle, false); err != nil {
|
||||
return fmt.Errorf("unable to create menu entries %w", err)
|
||||
}
|
||||
if err := t.addSeparatorMenuItem(separatorMenuID, 0); err != nil {
|
||||
|
|
|
|||
|
|
@ -10,6 +10,6 @@ const (
|
|||
|
||||
quitMenuTitle = "Quit Ollama"
|
||||
updateAvailableMenuTitle = "An update is available"
|
||||
updateMenuTitle = "Restart to update"
|
||||
updateMenutTitle = "Restart to update"
|
||||
diagLogsMenuTitle = "View logs"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -361,7 +361,7 @@ func (t *winTray) showMenu() error {
|
|||
|
||||
boolRet, _, err = pTrackPopupMenu.Call(
|
||||
uintptr(t.menus[0]),
|
||||
TPM_BOTTOMALIGN|TPM_LEFTALIGN|TPM_RIGHTBUTTON,
|
||||
TPM_BOTTOMALIGN|TPM_LEFTALIGN,
|
||||
uintptr(p.X),
|
||||
uintptr(p.Y),
|
||||
0,
|
||||
|
|
|
|||
|
|
@ -67,7 +67,6 @@ const (
|
|||
SW_HIDE = 0
|
||||
TPM_BOTTOMALIGN = 0x0020
|
||||
TPM_LEFTALIGN = 0x0000
|
||||
TPM_RIGHTBUTTON = 0x0002
|
||||
WM_CLOSE = 0x0010
|
||||
WM_USER = 0x0400
|
||||
WS_CAPTION = 0x00C00000
|
||||
|
|
|
|||
71
cmd/cmd.go
71
cmd/cmd.go
|
|
@ -19,6 +19,7 @@ import (
|
|||
"os"
|
||||
"os/signal"
|
||||
"path/filepath"
|
||||
"regexp"
|
||||
"runtime"
|
||||
"strconv"
|
||||
"strings"
|
||||
|
|
@ -34,11 +35,13 @@ import (
|
|||
"golang.org/x/term"
|
||||
|
||||
"github.com/ollama/ollama/api"
|
||||
"github.com/ollama/ollama/auth"
|
||||
"github.com/ollama/ollama/envconfig"
|
||||
"github.com/ollama/ollama/format"
|
||||
"github.com/ollama/ollama/parser"
|
||||
"github.com/ollama/ollama/progress"
|
||||
"github.com/ollama/ollama/server"
|
||||
"github.com/ollama/ollama/types/errtypes"
|
||||
"github.com/ollama/ollama/types/model"
|
||||
"github.com/ollama/ollama/version"
|
||||
)
|
||||
|
|
@ -453,10 +456,6 @@ func RunHandler(cmd *cobra.Command, args []string) error {
|
|||
if len(prompts) > 0 {
|
||||
interactive = false
|
||||
}
|
||||
// Be quiet if we're redirecting to a pipe or file
|
||||
if !term.IsTerminal(int(os.Stdout.Fd())) {
|
||||
interactive = false
|
||||
}
|
||||
|
||||
nowrap, err := cmd.Flags().GetBool("nowordwrap")
|
||||
if err != nil {
|
||||
|
|
@ -513,6 +512,47 @@ func RunHandler(cmd *cobra.Command, args []string) error {
|
|||
return generate(cmd, opts)
|
||||
}
|
||||
|
||||
func errFromUnknownKey(unknownKeyErr error) error {
|
||||
// find SSH public key in the error message
|
||||
sshKeyPattern := `ssh-\w+ [^\s"]+`
|
||||
re := regexp.MustCompile(sshKeyPattern)
|
||||
matches := re.FindStringSubmatch(unknownKeyErr.Error())
|
||||
|
||||
if len(matches) > 0 {
|
||||
serverPubKey := matches[0]
|
||||
|
||||
localPubKey, err := auth.GetPublicKey()
|
||||
if err != nil {
|
||||
return unknownKeyErr
|
||||
}
|
||||
|
||||
if runtime.GOOS == "linux" && serverPubKey != localPubKey {
|
||||
// try the ollama service public key
|
||||
svcPubKey, err := os.ReadFile("/usr/share/ollama/.ollama/id_ed25519.pub")
|
||||
if err != nil {
|
||||
return unknownKeyErr
|
||||
}
|
||||
localPubKey = strings.TrimSpace(string(svcPubKey))
|
||||
}
|
||||
|
||||
// check if the returned public key matches the local public key, this prevents adding a remote key to the user's account
|
||||
if serverPubKey != localPubKey {
|
||||
return unknownKeyErr
|
||||
}
|
||||
|
||||
var msg strings.Builder
|
||||
msg.WriteString(unknownKeyErr.Error())
|
||||
msg.WriteString("\n\nYour ollama key is:\n")
|
||||
msg.WriteString(localPubKey)
|
||||
msg.WriteString("\nAdd your key at:\n")
|
||||
msg.WriteString("https://ollama.com/settings/keys")
|
||||
|
||||
return errors.New(msg.String())
|
||||
}
|
||||
|
||||
return unknownKeyErr
|
||||
}
|
||||
|
||||
func PushHandler(cmd *cobra.Command, args []string) error {
|
||||
client, err := api.ClientFromEnvironment()
|
||||
if err != nil {
|
||||
|
|
@ -559,8 +599,6 @@ func PushHandler(cmd *cobra.Command, args []string) error {
|
|||
}
|
||||
|
||||
request := api.PushRequest{Name: args[0], Insecure: insecure}
|
||||
|
||||
n := model.ParseName(args[0])
|
||||
if err := client.Push(cmd.Context(), &request, fn); err != nil {
|
||||
if spinner != nil {
|
||||
spinner.Stop()
|
||||
|
|
@ -568,19 +606,18 @@ func PushHandler(cmd *cobra.Command, args []string) error {
|
|||
if strings.Contains(err.Error(), "access denied") {
|
||||
return errors.New("you are not authorized to push to this namespace, create the model under a namespace you own")
|
||||
}
|
||||
host := model.ParseName(args[0]).Host
|
||||
isOllamaHost := strings.HasSuffix(host, ".ollama.ai") || strings.HasSuffix(host, ".ollama.com")
|
||||
if strings.Contains(err.Error(), errtypes.UnknownOllamaKeyErrMsg) && isOllamaHost {
|
||||
// the user has not added their ollama key to ollama.com
|
||||
// re-throw an error with a more user-friendly message
|
||||
return errFromUnknownKey(err)
|
||||
}
|
||||
|
||||
return err
|
||||
}
|
||||
|
||||
p.Stop()
|
||||
spinner.Stop()
|
||||
|
||||
destination := n.String()
|
||||
if strings.HasSuffix(n.Host, ".ollama.ai") || strings.HasSuffix(n.Host, ".ollama.com") {
|
||||
destination = "https://ollama.com/" + strings.TrimSuffix(n.DisplayShortest(), ":latest")
|
||||
}
|
||||
fmt.Printf("\nYou can find your model at:\n\n")
|
||||
fmt.Printf("\t%s\n", destination)
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
|
|
@ -763,9 +800,9 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
|
|||
case "parameters":
|
||||
fmt.Println(resp.Parameters)
|
||||
case "system":
|
||||
fmt.Print(resp.System)
|
||||
fmt.Println(resp.System)
|
||||
case "template":
|
||||
fmt.Print(resp.Template)
|
||||
fmt.Println(resp.Template)
|
||||
}
|
||||
|
||||
return nil
|
||||
|
|
|
|||
125
cmd/cmd_test.go
125
cmd/cmd_test.go
|
|
@ -4,7 +4,6 @@ import (
|
|||
"bytes"
|
||||
"context"
|
||||
"encoding/json"
|
||||
"io"
|
||||
"net/http"
|
||||
"net/http/httptest"
|
||||
"os"
|
||||
|
|
@ -370,127 +369,3 @@ func TestGetModelfileName(t *testing.T) {
|
|||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestPushHandler(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
modelName string
|
||||
serverResponse map[string]func(w http.ResponseWriter, r *http.Request)
|
||||
expectedError string
|
||||
expectedOutput string
|
||||
}{
|
||||
{
|
||||
name: "successful push",
|
||||
modelName: "test-model",
|
||||
serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
|
||||
"/api/push": func(w http.ResponseWriter, r *http.Request) {
|
||||
if r.Method != http.MethodPost {
|
||||
t.Errorf("expected POST request, got %s", r.Method)
|
||||
}
|
||||
|
||||
var req api.PushRequest
|
||||
if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
|
||||
http.Error(w, err.Error(), http.StatusBadRequest)
|
||||
return
|
||||
}
|
||||
|
||||
if req.Name != "test-model" {
|
||||
t.Errorf("expected model name 'test-model', got %s", req.Name)
|
||||
}
|
||||
|
||||
// Simulate progress updates
|
||||
responses := []api.ProgressResponse{
|
||||
{Status: "preparing manifest"},
|
||||
{Digest: "sha256:abc123456789", Total: 100, Completed: 50},
|
||||
{Digest: "sha256:abc123456789", Total: 100, Completed: 100},
|
||||
}
|
||||
|
||||
for _, resp := range responses {
|
||||
if err := json.NewEncoder(w).Encode(resp); err != nil {
|
||||
http.Error(w, err.Error(), http.StatusInternalServerError)
|
||||
return
|
||||
}
|
||||
w.(http.Flusher).Flush()
|
||||
}
|
||||
},
|
||||
},
|
||||
expectedOutput: "\nYou can find your model at:\n\n\thttps://ollama.com/test-model\n",
|
||||
},
|
||||
{
|
||||
name: "unauthorized push",
|
||||
modelName: "unauthorized-model",
|
||||
serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
|
||||
"/api/push": func(w http.ResponseWriter, r *http.Request) {
|
||||
w.Header().Set("Content-Type", "application/json")
|
||||
w.WriteHeader(http.StatusUnauthorized)
|
||||
err := json.NewEncoder(w).Encode(map[string]string{
|
||||
"error": "access denied",
|
||||
})
|
||||
if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
},
|
||||
},
|
||||
expectedError: "you are not authorized to push to this namespace, create the model under a namespace you own",
|
||||
},
|
||||
}
|
||||
|
||||
for _, tt := range tests {
|
||||
t.Run(tt.name, func(t *testing.T) {
|
||||
mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
|
||||
if handler, ok := tt.serverResponse[r.URL.Path]; ok {
|
||||
handler(w, r)
|
||||
return
|
||||
}
|
||||
http.Error(w, "not found", http.StatusNotFound)
|
||||
}))
|
||||
defer mockServer.Close()
|
||||
|
||||
t.Setenv("OLLAMA_HOST", mockServer.URL)
|
||||
|
||||
cmd := &cobra.Command{}
|
||||
cmd.Flags().Bool("insecure", false, "")
|
||||
cmd.SetContext(context.TODO())
|
||||
|
||||
// Redirect stderr to capture progress output
|
||||
oldStderr := os.Stderr
|
||||
r, w, _ := os.Pipe()
|
||||
os.Stderr = w
|
||||
|
||||
// Capture stdout for the "Model pushed" message
|
||||
oldStdout := os.Stdout
|
||||
outR, outW, _ := os.Pipe()
|
||||
os.Stdout = outW
|
||||
|
||||
err := PushHandler(cmd, []string{tt.modelName})
|
||||
|
||||
// Restore stderr
|
||||
w.Close()
|
||||
os.Stderr = oldStderr
|
||||
// drain the pipe
|
||||
if _, err := io.ReadAll(r); err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
// Restore stdout and get output
|
||||
outW.Close()
|
||||
os.Stdout = oldStdout
|
||||
stdout, _ := io.ReadAll(outR)
|
||||
|
||||
if tt.expectedError == "" {
|
||||
if err != nil {
|
||||
t.Errorf("expected no error, got %v", err)
|
||||
}
|
||||
if tt.expectedOutput != "" {
|
||||
if got := string(stdout); got != tt.expectedOutput {
|
||||
t.Errorf("expected output %q, got %q", tt.expectedOutput, got)
|
||||
}
|
||||
}
|
||||
} else {
|
||||
if err == nil || !strings.Contains(err.Error(), tt.expectedError) {
|
||||
t.Errorf("expected error containing %q, got %v", tt.expectedError, err)
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -319,6 +319,8 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
|
|||
opts.Messages = append(opts.Messages, newMessage)
|
||||
}
|
||||
fmt.Println("Set system message.")
|
||||
sb.Reset()
|
||||
|
||||
sb.Reset()
|
||||
continue
|
||||
default:
|
||||
|
|
@ -514,7 +516,7 @@ func extractFileNames(input string) []string {
|
|||
// Regex to match file paths starting with optional drive letter, / ./ \ or .\ and include escaped or unescaped spaces (\ or %20)
|
||||
// and followed by more characters and a file extension
|
||||
// This will capture non filename strings, but we'll check for file existence to remove mismatches
|
||||
regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png)\b`
|
||||
regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png|svg)\b`
|
||||
re := regexp.MustCompile(regexPattern)
|
||||
|
||||
return re.FindAllString(input, -1)
|
||||
|
|
|
|||
|
|
@ -12,45 +12,44 @@ import (
|
|||
func TestExtractFilenames(t *testing.T) {
|
||||
// Unix style paths
|
||||
input := ` some preamble
|
||||
./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2 ./1.svg
|
||||
/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG`
|
||||
./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2
|
||||
/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.svg`
|
||||
res := extractFileNames(input)
|
||||
assert.Len(t, res, 5)
|
||||
assert.Contains(t, res[0], "one.png")
|
||||
assert.Contains(t, res[1], "two.jpg")
|
||||
assert.Contains(t, res[2], "three.jpeg")
|
||||
assert.Contains(t, res[3], "four.png")
|
||||
assert.Contains(t, res[4], "five.JPG")
|
||||
assert.Contains(t, res[4], "five.svg")
|
||||
assert.NotContains(t, res[4], '"')
|
||||
assert.NotContains(t, res, "inbetween1")
|
||||
assert.NotContains(t, res, "./1.svg")
|
||||
assert.NotContains(t, res, "inbtween")
|
||||
|
||||
// Windows style paths
|
||||
input = ` some preamble
|
||||
c:/users/jdoe/one.png inbetween1 c:/program files/someplace/two.jpg inbetween2
|
||||
/absolute/nospace/three.jpeg inbetween3 /absolute/with space/four.png inbetween4
|
||||
./relative\ path/five.JPG inbetween5 "./relative with/spaces/six.png inbetween6
|
||||
d:\path with\spaces\seven.JPEG inbetween7 c:\users\jdoe\eight.png inbetween8
|
||||
d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG some ending
|
||||
./relative\ path/five.svg inbetween5 "./relative with/spaces/six.png inbetween6
|
||||
d:\path with\spaces\seven.svg inbetween7 c:\users\jdoe\eight.png inbetween8
|
||||
d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.svg some ending
|
||||
`
|
||||
res = extractFileNames(input)
|
||||
assert.Len(t, res, 10)
|
||||
assert.NotContains(t, res, "inbetween2")
|
||||
assert.NotContains(t, res, "inbtween")
|
||||
assert.Contains(t, res[0], "one.png")
|
||||
assert.Contains(t, res[0], "c:")
|
||||
assert.Contains(t, res[1], "two.jpg")
|
||||
assert.Contains(t, res[1], "c:")
|
||||
assert.Contains(t, res[2], "three.jpeg")
|
||||
assert.Contains(t, res[3], "four.png")
|
||||
assert.Contains(t, res[4], "five.JPG")
|
||||
assert.Contains(t, res[4], "five.svg")
|
||||
assert.Contains(t, res[5], "six.png")
|
||||
assert.Contains(t, res[6], "seven.JPEG")
|
||||
assert.Contains(t, res[6], "seven.svg")
|
||||
assert.Contains(t, res[6], "d:")
|
||||
assert.Contains(t, res[7], "eight.png")
|
||||
assert.Contains(t, res[7], "c:")
|
||||
assert.Contains(t, res[8], "nine.png")
|
||||
assert.Contains(t, res[8], "d:")
|
||||
assert.Contains(t, res[9], "ten.PNG")
|
||||
assert.Contains(t, res[9], "ten.svg")
|
||||
assert.Contains(t, res[9], "E:")
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -350,7 +350,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
|
|||
return nil, err
|
||||
}
|
||||
}
|
||||
gpuInfo.DependencyPath = []string{libDir}
|
||||
gpuInfo.DependencyPath = libDir
|
||||
|
||||
if gfxOverride == "" {
|
||||
// Only load supported list once
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
|
|||
UnreliableFreeMemory: true,
|
||||
|
||||
ID: strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
|
||||
DependencyPath: []string{libDir},
|
||||
DependencyPath: libDir,
|
||||
MinimumMemory: rocmMinimumMemory,
|
||||
Name: name,
|
||||
Compute: gfx,
|
||||
|
|
|
|||
|
|
@ -240,7 +240,7 @@ func GetGPUInfo() GpuInfoList {
|
|||
Library: "cpu",
|
||||
Variant: cpuCapability.String(),
|
||||
ID: "0",
|
||||
DependencyPath: []string{depPath},
|
||||
DependencyPath: depPath,
|
||||
},
|
||||
CPUs: details,
|
||||
},
|
||||
|
|
@ -293,11 +293,11 @@ func GetGPUInfo() GpuInfoList {
|
|||
gpuInfo.DriverMinor = driverMinor
|
||||
variant := cudaVariant(gpuInfo)
|
||||
if depPath != "" {
|
||||
gpuInfo.DependencyPath = []string{depPath}
|
||||
gpuInfo.DependencyPath = depPath
|
||||
// Check for variant specific directory
|
||||
if variant != "" {
|
||||
if _, err := os.Stat(filepath.Join(depPath, "cuda_"+variant)); err == nil {
|
||||
gpuInfo.DependencyPath = []string{filepath.Join(depPath, "cuda_"+variant), depPath}
|
||||
gpuInfo.DependencyPath = filepath.Join(depPath, "cuda_"+variant)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -370,7 +370,7 @@ func GetGPUInfo() GpuInfoList {
|
|||
gpuInfo.FreeMemory = uint64(memInfo.free)
|
||||
gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
|
||||
gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
|
||||
gpuInfo.DependencyPath = []string{depPath}
|
||||
gpuInfo.DependencyPath = depPath
|
||||
oneapiGPUs = append(oneapiGPUs, gpuInfo)
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ type GpuInfo struct { // TODO better name maybe "InferenceProcessor"?
|
|||
MinimumMemory uint64 `json:"-"`
|
||||
|
||||
// Any extra PATH/LD_LIBRARY_PATH dependencies required for the Library to operate properly
|
||||
DependencyPath []string `json:"lib_path,omitempty"`
|
||||
DependencyPath string `json:"lib_path,omitempty"`
|
||||
|
||||
// Extra environment variables specific to the GPU as list of [key,value]
|
||||
EnvWorkarounds [][2]string `json:"envs,omitempty"`
|
||||
|
|
|
|||
74
docs/api.md
74
docs/api.md
|
|
@ -830,30 +830,10 @@ Create a model from a [`Modelfile`](./modelfile.md). It is recommended to set `m
|
|||
|
||||
### Parameters
|
||||
|
||||
- `model`: name of the model to create
|
||||
- `name`: name of the model to create
|
||||
- `modelfile` (optional): contents of the Modelfile
|
||||
- `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
|
||||
- `path` (optional): path to the Modelfile
|
||||
- `quantize` (optional): quantize a non-quantized (e.g. float16) model
|
||||
|
||||
#### Quantization types
|
||||
|
||||
| Type | Recommended |
|
||||
| --- | :-: |
|
||||
| q2_K | |
|
||||
| q3_K_L | |
|
||||
| q3_K_M | |
|
||||
| q3_K_S | |
|
||||
| q4_0 | |
|
||||
| q4_1 | |
|
||||
| q4_K_M | * |
|
||||
| q4_K_S | |
|
||||
| q5_0 | |
|
||||
| q5_1 | |
|
||||
| q5_K_M | |
|
||||
| q5_K_S | |
|
||||
| q6_K | |
|
||||
| q8_0 | * |
|
||||
|
||||
### Examples
|
||||
|
||||
|
|
@ -865,14 +845,14 @@ Create a new model from a `Modelfile`.
|
|||
|
||||
```shell
|
||||
curl http://localhost:11434/api/create -d '{
|
||||
"model": "mario",
|
||||
"name": "mario",
|
||||
"modelfile": "FROM llama3\nSYSTEM You are mario from Super Mario Bros."
|
||||
}'
|
||||
```
|
||||
|
||||
##### Response
|
||||
|
||||
A stream of JSON objects is returned:
|
||||
A stream of JSON objects. Notice that the final JSON object shows a `"status": "success"`.
|
||||
|
||||
```json
|
||||
{"status":"reading model metadata"}
|
||||
|
|
@ -888,43 +868,13 @@ A stream of JSON objects is returned:
|
|||
{"status":"success"}
|
||||
```
|
||||
|
||||
#### Quantize a model
|
||||
|
||||
Quantize a non-quantized model.
|
||||
|
||||
##### Request
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/api/create -d '{
|
||||
"model": "llama3.1:quantized",
|
||||
"modelfile": "FROM llama3.1:8b-instruct-fp16",
|
||||
"quantize": "q4_K_M"
|
||||
}'
|
||||
```
|
||||
|
||||
##### Response
|
||||
|
||||
A stream of JSON objects is returned:
|
||||
|
||||
```
|
||||
{"status":"quantizing F16 model to Q4_K_M"}
|
||||
{"status":"creating new layer sha256:667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29"}
|
||||
{"status":"using existing layer sha256:11ce4ee3e170f6adebac9a991c22e22ab3f8530e154ee669954c4bc73061c258"}
|
||||
{"status":"using existing layer sha256:0ba8f0e314b4264dfd19df045cde9d4c394a52474bf92ed6a3de22a4ca31a177"}
|
||||
{"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
|
||||
{"status":"creating new layer sha256:455f34728c9b5dd3376378bfb809ee166c145b0b4c1f1a6feca069055066ef9a"}
|
||||
{"status":"writing manifest"}
|
||||
{"status":"success"}
|
||||
```
|
||||
|
||||
|
||||
### Check if a Blob Exists
|
||||
|
||||
```shell
|
||||
HEAD /api/blobs/:digest
|
||||
```
|
||||
|
||||
Ensures that the file blob used for a FROM or ADAPTER field exists on the server. This is checking your Ollama server and not ollama.com.
|
||||
Ensures that the file blob used for a FROM or ADAPTER field exists on the server. This is checking your Ollama server and not Ollama.ai.
|
||||
|
||||
#### Query Parameters
|
||||
|
||||
|
|
@ -1029,7 +979,7 @@ Show information about a model including details, modelfile, template, parameter
|
|||
|
||||
### Parameters
|
||||
|
||||
- `model`: name of the model to show
|
||||
- `name`: name of the model to show
|
||||
- `verbose`: (optional) if set to `true`, returns full data for verbose response fields
|
||||
|
||||
### Examples
|
||||
|
|
@ -1038,7 +988,7 @@ Show information about a model including details, modelfile, template, parameter
|
|||
|
||||
```shell
|
||||
curl http://localhost:11434/api/show -d '{
|
||||
"model": "llama3.2"
|
||||
"name": "llama3.2"
|
||||
}'
|
||||
```
|
||||
|
||||
|
|
@ -1118,7 +1068,7 @@ Delete a model and its data.
|
|||
|
||||
### Parameters
|
||||
|
||||
- `model`: model name to delete
|
||||
- `name`: model name to delete
|
||||
|
||||
### Examples
|
||||
|
||||
|
|
@ -1126,7 +1076,7 @@ Delete a model and its data.
|
|||
|
||||
```shell
|
||||
curl -X DELETE http://localhost:11434/api/delete -d '{
|
||||
"model": "llama3:13b"
|
||||
"name": "llama3:13b"
|
||||
}'
|
||||
```
|
||||
|
||||
|
|
@ -1144,7 +1094,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
|
|||
|
||||
### Parameters
|
||||
|
||||
- `model`: name of the model to pull
|
||||
- `name`: name of the model to pull
|
||||
- `insecure`: (optional) allow insecure connections to the library. Only use this if you are pulling from your own library during development.
|
||||
- `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
|
||||
|
||||
|
|
@ -1154,7 +1104,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
|
|||
|
||||
```shell
|
||||
curl http://localhost:11434/api/pull -d '{
|
||||
"model": "llama3.2"
|
||||
"name": "llama3.2"
|
||||
}'
|
||||
```
|
||||
|
||||
|
|
@ -1216,7 +1166,7 @@ Upload a model to a model library. Requires registering for ollama.ai and adding
|
|||
|
||||
### Parameters
|
||||
|
||||
- `model`: name of the model to push in the form of `<namespace>/<model>:<tag>`
|
||||
- `name`: name of the model to push in the form of `<namespace>/<model>:<tag>`
|
||||
- `insecure`: (optional) allow insecure connections to the library. Only use this if you are pushing to your library during development.
|
||||
- `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
|
||||
|
||||
|
|
@ -1226,7 +1176,7 @@ Upload a model to a model library. Requires registering for ollama.ai and adding
|
|||
|
||||
```shell
|
||||
curl http://localhost:11434/api/push -d '{
|
||||
"model": "mattw/pygmalion:latest"
|
||||
"name": "mattw/pygmalion:latest"
|
||||
}'
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -50,9 +50,6 @@ sudo systemctl restart docker
|
|||
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> If you're running on an NVIDIA JetPack system, Ollama can't automatically discover the correct JetPack version. Pass the environment variable JETSON_JETPACK=5 or JETSON_JETPACK=6 to the container to select version 5 or 6.
|
||||
|
||||
### AMD GPU
|
||||
|
||||
To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ ollama run my-model
|
|||
|
||||
Ollama supports importing adapters based on several different model architectures including:
|
||||
|
||||
* Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
|
||||
* Llama (including Llama 2, Llama 3, and Llama 3.1);
|
||||
* Mistral (including Mistral 1, Mistral 2, and Mixtral); and
|
||||
* Gemma (including Gemma 1 and Gemma 2)
|
||||
|
||||
|
|
@ -67,12 +67,14 @@ ollama run my-model
|
|||
|
||||
Ollama supports importing models for several different architectures including:
|
||||
|
||||
* Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
|
||||
* Llama (including Llama 2, Llama 3, and Llama 3.1);
|
||||
* Mistral (including Mistral 1, Mistral 2, and Mixtral);
|
||||
* Gemma (including Gemma 1 and Gemma 2); and
|
||||
* Phi3
|
||||
|
||||
This includes importing foundation models as well as any fine tuned models which have been _fused_ with a foundation model.
|
||||
This includes importing foundation models as well as any fine tuned models which which have been _fused_ with a foundation model.
|
||||
|
||||
|
||||
## Importing a GGUF based model or adapter
|
||||
|
||||
If you have a GGUF based model or adapter it is possible to import it into Ollama. You can obtain a GGUF model or adapter by:
|
||||
|
|
@ -81,7 +83,7 @@ If you have a GGUF based model or adapter it is possible to import it into Ollam
|
|||
* converting a Safetensors adapter with the `convert_lora_to_gguf.py` from Llama.cpp; or
|
||||
* downloading a model or adapter from a place such as HuggingFace
|
||||
|
||||
To import a GGUF model, create a `Modelfile` containing:
|
||||
To import a GGUF model, create a `Modelfile` containg:
|
||||
|
||||
```dockerfile
|
||||
FROM /path/to/file.gguf
|
||||
|
|
|
|||
|
|
@ -112,21 +112,6 @@ sudo systemctl status ollama
|
|||
> https://www.amd.com/en/support/linux-drivers for best support of your Radeon
|
||||
> GPU.
|
||||
|
||||
## Customizing
|
||||
|
||||
To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
|
||||
|
||||
```
|
||||
sudo systemctl edit ollama
|
||||
```
|
||||
|
||||
Alternatively, create an override file manually in `/etc/systemd/system/ollama.service.d/override.conf`:
|
||||
|
||||
```ini
|
||||
[Service]
|
||||
Environment="OLLAMA_DEBUG=1"
|
||||
```
|
||||
|
||||
## Updating
|
||||
|
||||
Update Ollama by running the install script again:
|
||||
|
|
|
|||
|
|
@ -120,7 +120,7 @@ FROM <model directory>
|
|||
The model directory should contain the Safetensors weights for a supported architecture.
|
||||
|
||||
Currently supported model architectures:
|
||||
* Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2)
|
||||
* Llama (including Llama 2, Llama 3, and Llama 3.1)
|
||||
* Mistral (including Mistral 1, Mistral 2, and Mixtral)
|
||||
* Gemma (including Gemma 1 and Gemma 2)
|
||||
* Phi3
|
||||
|
|
|
|||
|
|
@ -95,21 +95,13 @@ If none of those resolve the problem, gather additional information and file an
|
|||
|
||||
On linux, AMD GPU access typically requires `video` and/or `render` group membership to access the `/dev/kfd` device. If permissions are not set up correctly, Ollama will detect this and report an error in the server log.
|
||||
|
||||
When running in a container, in some Linux distributions and container runtimes, the ollama process may be unable to access the GPU. Use `ls -lnd /dev/kfd /dev/dri /dev/dri/*` on the host system to determine the **numeric** group IDs on your system, and pass additional `--group-add ...` arguments to the container so it can access the required devices. For example, in the following output `crw-rw---- 1 0 44 226, 0 Sep 16 16:55 /dev/dri/card0` the group ID column is `44`
|
||||
|
||||
If Ollama initially works on the GPU in a docker container, but then switches to running on CPU after some period of time with errors in the server log reporting GPU discovery failures, this can be resolved by disabling systemd cgroup management in Docker. Edit `/etc/docker/daemon.json` on the host and add `"exec-opts": ["native.cgroupdriver=cgroupfs"]` to the docker configuration.
|
||||
When running in a container, in some Linux distributions and container runtimes, the ollama process may be unable to access the GPU. Use `ls -ld /dev/kfd /dev/dri /dev/dri/*` on the host system to determine the group assignments on your system, and pass additional `--group-add ...` arguments to the container so it can access the required devices.
|
||||
|
||||
If you are experiencing problems getting Ollama to correctly discover or use your GPU for inference, the following may help isolate the failure.
|
||||
- `AMD_LOG_LEVEL=3` Enable info log levels in the AMD HIP/ROCm libraries. This can help show more detailed error codes that can help troubleshoot problems
|
||||
- `OLLAMA_DEBUG=1` During GPU discovery additional information will be reported
|
||||
- Check dmesg for any errors from amdgpu or kfd drivers `sudo dmesg | grep -i amdgpu` and `sudo dmesg | grep -i kfd`
|
||||
|
||||
## Multiple AMD GPUs
|
||||
|
||||
If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.
|
||||
|
||||
- https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/mgpu.html#mgpu-known-issues-and-limitations
|
||||
|
||||
## Windows Terminal Errors
|
||||
|
||||
Older versions of Windows 10 (e.g., 21H1) are known to have a bug where the standard terminal program does not display control characters correctly. This can result in a long string of strings like `←[?25h←[?25l` being displayed, sometimes erroring with `The parameter is incorrect` To resolve this problem, please update to Win 10 22H1 or newer.
|
||||
|
|
|
|||
83
docs/tutorials/fly-gpu.md
Normal file
83
docs/tutorials/fly-gpu.md
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
# Running Ollama on Fly.io GPU Instances
|
||||
|
||||
Ollama runs with little to no configuration on [Fly.io GPU instances](https://fly.io/docs/gpus/gpu-quickstart/). If you don't have access to GPUs yet, you'll need to [apply for access](https://fly.io/gpu/) on the waitlist. Once you're accepted, you'll get an email with instructions on how to get started.
|
||||
|
||||
Create a new app with `fly apps create`:
|
||||
|
||||
```bash
|
||||
fly apps create
|
||||
```
|
||||
|
||||
Then create a `fly.toml` file in a new folder that looks like this:
|
||||
|
||||
```toml
|
||||
app = "sparkling-violet-709"
|
||||
primary_region = "ord"
|
||||
vm.size = "a100-40gb" # see https://fly.io/docs/gpus/gpu-quickstart/ for more info
|
||||
|
||||
[build]
|
||||
image = "ollama/ollama"
|
||||
|
||||
[http_service]
|
||||
internal_port = 11434
|
||||
force_https = false
|
||||
auto_stop_machines = true
|
||||
auto_start_machines = true
|
||||
min_machines_running = 0
|
||||
processes = ["app"]
|
||||
|
||||
[mounts]
|
||||
source = "models"
|
||||
destination = "/root/.ollama"
|
||||
initial_size = "100gb"
|
||||
```
|
||||
|
||||
Then create a [new private IPv6 address](https://fly.io/docs/reference/private-networking/#flycast-private-load-balancing) for your app:
|
||||
|
||||
```bash
|
||||
fly ips allocate-v6 --private
|
||||
```
|
||||
|
||||
Then deploy your app:
|
||||
|
||||
```bash
|
||||
fly deploy
|
||||
```
|
||||
|
||||
And finally you can access it interactively with a new Fly.io Machine:
|
||||
|
||||
```
|
||||
fly machine run -e OLLAMA_HOST=http://your-app-name.flycast --shell ollama/ollama
|
||||
```
|
||||
|
||||
```bash
|
||||
$ ollama run openchat:7b-v3.5-fp16
|
||||
>>> How do I bake chocolate chip cookies?
|
||||
To bake chocolate chip cookies, follow these steps:
|
||||
|
||||
1. Preheat the oven to 375°F (190°C) and line a baking sheet with parchment paper or silicone baking mat.
|
||||
|
||||
2. In a large bowl, mix together 1 cup of unsalted butter (softened), 3/4 cup granulated sugar, and 3/4
|
||||
cup packed brown sugar until light and fluffy.
|
||||
|
||||
3. Add 2 large eggs, one at a time, to the butter mixture, beating well after each addition. Stir in 1
|
||||
teaspoon of pure vanilla extract.
|
||||
|
||||
4. In a separate bowl, whisk together 2 cups all-purpose flour, 1/2 teaspoon baking soda, and 1/2 teaspoon
|
||||
salt. Gradually add the dry ingredients to the wet ingredients, stirring until just combined.
|
||||
|
||||
5. Fold in 2 cups of chocolate chips (or chunks) into the dough.
|
||||
|
||||
6. Drop rounded tablespoons of dough onto the prepared baking sheet, spacing them about 2 inches apart.
|
||||
|
||||
7. Bake for 10-12 minutes, or until the edges are golden brown. The centers should still be slightly soft.
|
||||
|
||||
8. Allow the cookies to cool on the baking sheet for a few minutes before transferring them to a wire rack
|
||||
to cool completely.
|
||||
|
||||
Enjoy your homemade chocolate chip cookies!
|
||||
```
|
||||
|
||||
When you set it up like this, it will automatically turn off when you're done using it. Then when you access it again, it will automatically turn back on. This is a great way to save money on GPU instances when you're not using them. If you want a persistent wake-on-use connection to your Ollama instance, you can set up a [connection to your Fly network using WireGuard](https://fly.io/docs/reference/private-networking/#discovering-apps-through-dns-on-a-wireguard-connection). Then you can access your Ollama instance at `http://your-app-name.flycast`.
|
||||
|
||||
And that's it!
|
||||
77
docs/tutorials/langchainjs.md
Normal file
77
docs/tutorials/langchainjs.md
Normal file
|
|
@ -0,0 +1,77 @@
|
|||
# Using LangChain with Ollama using JavaScript
|
||||
|
||||
In this tutorial, we are going to use JavaScript with LangChain and Ollama to learn about something just a touch more recent. In August 2023, there was a series of wildfires on Maui. There is no way an LLM trained before that time can know about this, since their training data would not include anything as recent as that. So we can find the [Wikipedia article about the fires](https://en.wikipedia.org/wiki/2023_Hawaii_wildfires) and ask questions about the contents.
|
||||
|
||||
To get started, let's just use **LangChain** to ask a simple question to a model. To do this with JavaScript, we need to install **LangChain**:
|
||||
|
||||
```bash
|
||||
npm install @langchain/community
|
||||
```
|
||||
|
||||
Now we can start building out our JavaScript:
|
||||
|
||||
```javascript
|
||||
import { Ollama } from "@langchain/community/llms/ollama";
|
||||
|
||||
const ollama = new Ollama({
|
||||
baseUrl: "http://localhost:11434",
|
||||
model: "llama3.2",
|
||||
});
|
||||
|
||||
const answer = await ollama.invoke(`why is the sky blue?`);
|
||||
|
||||
console.log(answer);
|
||||
```
|
||||
|
||||
That will get us the same thing as if we ran `ollama run llama3.2 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
|
||||
|
||||
```bash
|
||||
npm install cheerio
|
||||
```
|
||||
|
||||
```javascript
|
||||
import { CheerioWebBaseLoader } from "langchain/document_loaders/web/cheerio";
|
||||
|
||||
const loader = new CheerioWebBaseLoader("https://en.wikipedia.org/wiki/2023_Hawaii_wildfires");
|
||||
const data = await loader.load();
|
||||
```
|
||||
|
||||
That will load the document. Although this page is smaller than the Odyssey, it is certainly bigger than the context size for most LLMs. So we are going to need to split into smaller pieces, and then select just the pieces relevant to our question. This is a great use for a vector datastore. In this example, we will use the **MemoryVectorStore** that is part of **LangChain**. But there is one more thing we need to get the content into the datastore. We have to run an embeddings process that converts the tokens in the text into a series of vectors. And for that, we are going to use **Tensorflow**. There is a lot of stuff going on in this one. First, install the **Tensorflow** components that we need.
|
||||
|
||||
```javascript
|
||||
npm install @tensorflow/tfjs-core@3.6.0 @tensorflow/tfjs-converter@3.6.0 @tensorflow-models/universal-sentence-encoder@1.3.3 @tensorflow/tfjs-node@4.10.0
|
||||
```
|
||||
|
||||
If you just install those components without the version numbers, it will install the latest versions, but there are conflicts within **Tensorflow**, so you need to install the compatible versions.
|
||||
|
||||
```javascript
|
||||
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter"
|
||||
import { MemoryVectorStore } from "langchain/vectorstores/memory";
|
||||
import "@tensorflow/tfjs-node";
|
||||
import { TensorFlowEmbeddings } from "langchain/embeddings/tensorflow";
|
||||
|
||||
// Split the text into 500 character chunks. And overlap each chunk by 20 characters
|
||||
const textSplitter = new RecursiveCharacterTextSplitter({
|
||||
chunkSize: 500,
|
||||
chunkOverlap: 20
|
||||
});
|
||||
const splitDocs = await textSplitter.splitDocuments(data);
|
||||
|
||||
// Then use the TensorFlow Embedding to store these chunks in the datastore
|
||||
const vectorStore = await MemoryVectorStore.fromDocuments(splitDocs, new TensorFlowEmbeddings());
|
||||
```
|
||||
|
||||
To connect the datastore to a question asked to a LLM, we need to use the concept at the heart of **LangChain**: the chain. Chains are a way to connect a number of activities together to accomplish a particular tasks. There are a number of chain types available, but for this tutorial we are using the **RetrievalQAChain**.
|
||||
|
||||
```javascript
|
||||
import { RetrievalQAChain } from "langchain/chains";
|
||||
|
||||
const retriever = vectorStore.asRetriever();
|
||||
const chain = RetrievalQAChain.fromLLM(ollama, retriever);
|
||||
const result = await chain.call({query: "When was Hawaii's request for a major disaster declaration approved?"});
|
||||
console.log(result.text)
|
||||
```
|
||||
|
||||
So we created a retriever, which is a way to return the chunks that match a query from a datastore. And then connect the retriever and the model via a chain. Finally, we send a query to the chain, which results in an answer using our document as a source. The answer it returned was correct, August 10, 2023.
|
||||
|
||||
And that is a simple introduction to what you can do with **LangChain** and **Ollama.**
|
||||
85
docs/tutorials/langchainpy.md
Normal file
85
docs/tutorials/langchainpy.md
Normal file
|
|
@ -0,0 +1,85 @@
|
|||
# Using LangChain with Ollama in Python
|
||||
|
||||
Let's imagine we are studying the classics, such as **the Odyssey** by **Homer**. We might have a question about Neleus and his family. If you ask llama2 for that info, you may get something like:
|
||||
|
||||
> I apologize, but I'm a large language model, I cannot provide information on individuals or families that do not exist in reality. Neleus is not a real person or character, and therefore does not have a family or any other personal details. My apologies for any confusion. Is there anything else I can help you with?
|
||||
|
||||
This sounds like a typical censored response, but even llama2-uncensored gives a mediocre answer:
|
||||
|
||||
> Neleus was a legendary king of Pylos and the father of Nestor, one of the Argonauts. His mother was Clymene, a sea nymph, while his father was Neptune, the god of the sea.
|
||||
|
||||
So let's figure out how we can use **LangChain** with Ollama to ask our question to the actual document, the Odyssey by Homer, using Python.
|
||||
|
||||
Let's start by asking a simple question that we can get an answer to from the **Llama3** model using **Ollama**. First, we need to install the **LangChain** package:
|
||||
|
||||
`pip install langchain_community`
|
||||
|
||||
Then we can create a model and ask the question:
|
||||
|
||||
```python
|
||||
from langchain_community.llms import Ollama
|
||||
ollama = Ollama(
|
||||
base_url='http://localhost:11434',
|
||||
model="llama3"
|
||||
)
|
||||
print(ollama.invoke("why is the sky blue"))
|
||||
```
|
||||
|
||||
Notice that we are defining the model and the base URL for Ollama.
|
||||
|
||||
Now let's load a document to ask questions against. I'll load up the Odyssey by Homer, which you can find at Project Gutenberg. We will need **WebBaseLoader** which is part of **LangChain** and loads text from any webpage. On my machine, I also needed to install **bs4** to get that to work, so run `pip install bs4`.
|
||||
|
||||
```python
|
||||
from langchain.document_loaders import WebBaseLoader
|
||||
loader = WebBaseLoader("https://www.gutenberg.org/files/1727/1727-h/1727-h.htm")
|
||||
data = loader.load()
|
||||
```
|
||||
|
||||
This file is pretty big. Just the preface is 3000 tokens. Which means the full document won't fit into the context for the model. So we need to split it up into smaller pieces.
|
||||
|
||||
```python
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
||||
|
||||
text_splitter=RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
|
||||
all_splits = text_splitter.split_documents(data)
|
||||
```
|
||||
|
||||
It's split up, but we have to find the relevant splits and then submit those to the model. We can do this by creating embeddings and storing them in a vector database. We can use Ollama directly to instantiate an embedding model. We will use ChromaDB in this example for a vector database. `pip install chromadb`
|
||||
We also need to pull embedding model: `ollama pull nomic-embed-text`
|
||||
```python
|
||||
from langchain.embeddings import OllamaEmbeddings
|
||||
from langchain.vectorstores import Chroma
|
||||
oembed = OllamaEmbeddings(base_url="http://localhost:11434", model="nomic-embed-text")
|
||||
vectorstore = Chroma.from_documents(documents=all_splits, embedding=oembed)
|
||||
```
|
||||
|
||||
Now let's ask a question from the document. **Who was Neleus, and who is in his family?** Neleus is a character in the Odyssey, and the answer can be found in our text.
|
||||
|
||||
```python
|
||||
question="Who is Neleus and who is in Neleus' family?"
|
||||
docs = vectorstore.similarity_search(question)
|
||||
len(docs)
|
||||
```
|
||||
|
||||
This will output the number of matches for chunks of data similar to the search.
|
||||
|
||||
The next thing is to send the question and the relevant parts of the docs to the model to see if we can get a good answer. But we are stitching two parts of the process together, and that is called a chain. This means we need to define a chain:
|
||||
|
||||
```python
|
||||
from langchain.chains import RetrievalQA
|
||||
qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
|
||||
res = qachain.invoke({"query": question})
|
||||
print(res['result'])
|
||||
```
|
||||
|
||||
The answer received from this chain was:
|
||||
|
||||
> Neleus is a character in Homer's "Odyssey" and is mentioned in the context of Penelope's suitors. Neleus is the father of Chloris, who is married to Neleus and bears him several children, including Nestor, Chromius, Periclymenus, and Pero. Amphinomus, the son of Nisus, is also mentioned as a suitor of Penelope and is known for his good natural disposition and agreeable conversation.
|
||||
|
||||
It's not a perfect answer, as it implies Neleus married his daughter when actually Chloris "was the youngest daughter to Amphion son of Iasus and king of Minyan Orchomenus, and was Queen in Pylos".
|
||||
|
||||
I updated the chunk_overlap for the text splitter to 20 and tried again and got a much better answer:
|
||||
|
||||
> Neleus is a character in Homer's epic poem "The Odyssey." He is the husband of Chloris, who is the youngest daughter of Amphion son of Iasus and king of Minyan Orchomenus. Neleus has several children with Chloris, including Nestor, Chromius, Periclymenus, and Pero.
|
||||
|
||||
And that is a much better answer.
|
||||
15
docs/tutorials/nvidia-jetson.md
Normal file
15
docs/tutorials/nvidia-jetson.md
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
# Running Ollama on NVIDIA Jetson Devices
|
||||
|
||||
Ollama runs well on [NVIDIA Jetson Devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) and should run out of the box with the standard installation instructions.
|
||||
|
||||
The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack), but should also work on JetPack 6.0.
|
||||
|
||||
- Install Ollama via standard Linux command (ignore the 404 error): `curl https://ollama.com/install.sh | sh`
|
||||
- Pull the model you want to use (e.g. mistral): `ollama pull mistral`
|
||||
- Start an interactive session: `ollama run mistral`
|
||||
|
||||
And that's it!
|
||||
|
||||
# Running Ollama in Docker
|
||||
|
||||
When running GPU accelerated applications in Docker, it is highly recommended to use [dusty-nv jetson-containers repo](https://github.com/dusty-nv/jetson-containers).
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from langchain.llms import Ollama
|
||||
|
||||
input = input("What is your question?\n> ")
|
||||
input = input("What is your question?")
|
||||
llm = Ollama(model="llama3.2")
|
||||
res = llm.invoke(input)
|
||||
res = llm.predict(input)
|
||||
print (res)
|
||||
|
|
|
|||
10
go.mod
10
go.mod
|
|
@ -7,12 +7,12 @@ require (
|
|||
github.com/emirpasic/gods v1.18.1
|
||||
github.com/gin-gonic/gin v1.10.0
|
||||
github.com/golang/protobuf v1.5.4 // indirect
|
||||
github.com/google/uuid v1.6.0
|
||||
github.com/google/uuid v1.1.2
|
||||
github.com/olekukonko/tablewriter v0.0.5
|
||||
github.com/spf13/cobra v1.7.0
|
||||
github.com/stretchr/testify v1.9.0
|
||||
github.com/x448/float16 v0.8.4
|
||||
golang.org/x/sync v0.9.0
|
||||
golang.org/x/sync v0.3.0
|
||||
)
|
||||
|
||||
require (
|
||||
|
|
@ -22,14 +22,14 @@ require (
|
|||
github.com/mattn/go-runewidth v0.0.14
|
||||
github.com/nlpodyssey/gopickle v0.3.0
|
||||
github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
|
||||
golang.org/x/image v0.22.0
|
||||
golang.org/x/image v0.14.0
|
||||
)
|
||||
|
||||
require (
|
||||
github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40 // indirect
|
||||
github.com/bytedance/sonic/loader v0.1.1 // indirect
|
||||
github.com/chewxy/hm v1.0.0 // indirect
|
||||
github.com/chewxy/math32 v1.11.0 // indirect
|
||||
github.com/chewxy/math32 v1.10.1 // indirect
|
||||
github.com/cloudwego/base64x v0.1.4 // indirect
|
||||
github.com/cloudwego/iasm v0.2.0 // indirect
|
||||
github.com/davecgh/go-spew v1.1.1 // indirect
|
||||
|
|
@ -73,7 +73,7 @@ require (
|
|||
golang.org/x/net v0.25.0 // indirect
|
||||
golang.org/x/sys v0.20.0
|
||||
golang.org/x/term v0.20.0
|
||||
golang.org/x/text v0.20.0
|
||||
golang.org/x/text v0.15.0
|
||||
google.golang.org/protobuf v1.34.1
|
||||
gopkg.in/yaml.v3 v3.0.1 // indirect
|
||||
)
|
||||
|
|
|
|||
19
go.sum
19
go.sum
|
|
@ -21,8 +21,8 @@ github.com/census-instrumentation/opencensus-proto v0.2.1/go.mod h1:f6KPmirojxKA
|
|||
github.com/chewxy/hm v1.0.0 h1:zy/TSv3LV2nD3dwUEQL2VhXeoXbb9QkpmdRAVUFiA6k=
|
||||
github.com/chewxy/hm v1.0.0/go.mod h1:qg9YI4q6Fkj/whwHR1D+bOGeF7SniIP40VweVepLjg0=
|
||||
github.com/chewxy/math32 v1.0.0/go.mod h1:Miac6hA1ohdDUTagnvJy/q+aNnEk16qWUdb8ZVhvCN0=
|
||||
github.com/chewxy/math32 v1.11.0 h1:8sek2JWqeaKkVnHa7bPVqCEOUPbARo4SGxs6toKyAOo=
|
||||
github.com/chewxy/math32 v1.11.0/go.mod h1:dOB2rcuFrCn6UHrze36WSLVPKtzPMRAQvBvUwkSsLqs=
|
||||
github.com/chewxy/math32 v1.10.1 h1:LFpeY0SLJXeaiej/eIp2L40VYfscTvKh/FSEZ68uMkU=
|
||||
github.com/chewxy/math32 v1.10.1/go.mod h1:dOB2rcuFrCn6UHrze36WSLVPKtzPMRAQvBvUwkSsLqs=
|
||||
github.com/client9/misspell v0.3.4/go.mod h1:qj6jICC3Q7zFZvVWo7KLAzC3yx5G7kyvSDkc90ppPyw=
|
||||
github.com/cloudwego/base64x v0.1.4 h1:jwCgWpFanWmN8xoIUHa2rtzmkd5J2plF/dnLS6Xd/0Y=
|
||||
github.com/cloudwego/base64x v0.1.4/go.mod h1:0zlkT4Wn5C6NdauXdJRhSKRlJvmclQ1hhJgA0rcu/8w=
|
||||
|
|
@ -113,9 +113,8 @@ github.com/google/go-cmp v0.5.6/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/
|
|||
github.com/google/go-cmp v0.6.0 h1:ofyhxvXcZhMsU5ulbFiLKl/XBFqE1GSq7atu8tAmTRI=
|
||||
github.com/google/go-cmp v0.6.0/go.mod h1:17dUlkBOakJ0+DkrSSNjCkIjxS6bF9zb3elmeNGIjoY=
|
||||
github.com/google/gofuzz v1.0.0/go.mod h1:dBl0BpW6vV/+mYPU4Po3pmUjxk6FQPldtuIdl/M65Eg=
|
||||
github.com/google/uuid v1.1.2 h1:EVhdT+1Kseyi1/pUmXKaFxYsDNy9RQYkMWRH68J/W7Y=
|
||||
github.com/google/uuid v1.1.2/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
|
||||
github.com/google/uuid v1.6.0 h1:NIvaJDMOsjHA8n1jAhLSgzrAzy1Hgr+hNrb57e+94F0=
|
||||
github.com/google/uuid v1.6.0/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
|
||||
github.com/grpc-ecosystem/grpc-gateway v1.16.0/go.mod h1:BDjrQk3hbvj6Nolgz8mAMFbcEtjT1g+wF4CSlocrBnw=
|
||||
github.com/inconshreveable/mousetrap v1.1.0 h1:wN+x4NVGpMsO7ErUn/mUI3vEoE6Jt13X2s0bqwp9tc8=
|
||||
github.com/inconshreveable/mousetrap v1.1.0/go.mod h1:vpF70FUmC8bwa3OWnCshd2FqLfsEA9PFc4w1p2J65bw=
|
||||
|
|
@ -231,8 +230,8 @@ golang.org/x/image v0.0.0-20200430140353-33d19683fad8/go.mod h1:FeLwcggjj3mMvU+o
|
|||
golang.org/x/image v0.0.0-20200618115811-c13761719519/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
|
||||
golang.org/x/image v0.0.0-20201208152932-35266b937fa6/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
|
||||
golang.org/x/image v0.0.0-20210216034530-4410531fe030/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
|
||||
golang.org/x/image v0.22.0 h1:UtK5yLUzilVrkjMAZAZ34DXGpASN8i8pj8g+O+yd10g=
|
||||
golang.org/x/image v0.22.0/go.mod h1:9hPFhljd4zZ1GNSIZJ49sqbp45GKK9t6w+iXvGqZUz4=
|
||||
golang.org/x/image v0.14.0 h1:tNgSxAFe3jC4uYqvZdTr84SZoM1KfwdC9SKIFrLjFn4=
|
||||
golang.org/x/image v0.14.0/go.mod h1:HUYqC05R2ZcZ3ejNQsIHQDQiwWM4JBqmm6MKANTp4LE=
|
||||
golang.org/x/lint v0.0.0-20181026193005-c67002cb31c3/go.mod h1:UVdnD1Gm6xHRNCYTkRU2/jEulfH38KcIWyp/GAMgvoE=
|
||||
golang.org/x/lint v0.0.0-20190227174305-5b3e6a55c961/go.mod h1:wehouNa3lNwaWXcvxsM5YxQ5yQlVC4a0KAMCusXpPoU=
|
||||
golang.org/x/lint v0.0.0-20190313153728-d0100b6bd8b3/go.mod h1:6SW0HCj/g11FgYtHlgUYUwCkIfeOF89ocIRzGO/8vkc=
|
||||
|
|
@ -266,8 +265,8 @@ golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJ
|
|||
golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
|
||||
golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
|
||||
golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
|
||||
golang.org/x/sync v0.9.0 h1:fEo0HyrW1GIgZdpbhCRO0PkJajUS5H9IFUztCgEo2jQ=
|
||||
golang.org/x/sync v0.9.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
|
||||
golang.org/x/sync v0.3.0 h1:ftCYgMx6zT/asHUrPw8BLLscYtGznsLAnjq5RH9P66E=
|
||||
golang.org/x/sync v0.3.0/go.mod h1:FU7BRWz2tNW+3quACPkgCx/L+uEAv1htQ0V83Z9Rj+Y=
|
||||
golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
|
||||
golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
|
||||
golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
|
||||
|
|
@ -292,8 +291,8 @@ golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
|
|||
golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
|
||||
golang.org/x/text v0.3.5/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
|
||||
golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
|
||||
golang.org/x/text v0.20.0 h1:gK/Kv2otX8gz+wn7Rmb3vT96ZwuoxnQlY+HlJVj7Qug=
|
||||
golang.org/x/text v0.20.0/go.mod h1:D4IsuqiFMhST5bX19pQ9ikHC2GsaKyk/oF+pn3ducp4=
|
||||
golang.org/x/text v0.15.0 h1:h1V/4gjBv8v9cjcR6+AR5+/cIYK5N/WAgiv4xlsEtAk=
|
||||
golang.org/x/text v0.15.0/go.mod h1:18ZOQIKpY8NJVqYksKHtTdi31H5itFRjB5/qKTNYzSU=
|
||||
golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
|
||||
golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
|
||||
golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
|
||||
|
|
|
|||
|
|
@ -10,38 +10,7 @@ import (
|
|||
"github.com/ollama/ollama/api"
|
||||
)
|
||||
|
||||
func TestLongInputContext(t *testing.T) {
|
||||
// Setting NUM_PARALLEL to 1 ensures the allocated context is exactly what
|
||||
// we asked for and there is nothing extra that we could spill over into
|
||||
t.Setenv("OLLAMA_NUM_PARALLEL", "1")
|
||||
|
||||
// Longer needed for small footprint GPUs
|
||||
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
|
||||
defer cancel()
|
||||
// Set up the test data
|
||||
req := api.GenerateRequest{
|
||||
Model: "llama2",
|
||||
Prompt: "Oh, don’t speak to me of Austria. Perhaps I don’t understand things, but Austria never has wished, and does not wish, for war. She is betraying us! Russia alone must save Europe. Our gracious sovereign recognizes his high vocation and will be true to it. That is the one thing I have faith in! Our good and wonderful sovereign has to perform the noblest role on earth, and he is so virtuous and noble that God will not forsake him. He will fulfill his vocation and crush the hydra of revolution, which has become more terrible than ever in the person of this murderer and villain! We alone must avenge the blood of the just one.... Whom, I ask you, can we rely on?... England with her commercial spirit will not and cannot understand the Emperor Alexander’s loftiness of soul. She has refused to evacuate Malta. She wanted to find, and still seeks, some secret motive in our actions. What answer did Novosíltsev get? None. The English have not understood and cannot understand the self-abnegation of our Emperor who wants nothing for himself, but only desires the good of mankind. And what have they promised? Nothing! And what little they have promised they will not perform! Prussia has always declared that Buonaparte is invincible, and that all Europe is powerless before him.... And I don’t believe a word that Hardenburg says, or Haugwitz either. This famous Prussian neutrality is just a trap. I have faith only in God and the lofty destiny of our adored monarch. He will save Europe! What country is this referring to?",
|
||||
Stream: &stream,
|
||||
Options: map[string]interface{}{
|
||||
"temperature": 0,
|
||||
"seed": 123,
|
||||
"num_ctx": 128,
|
||||
},
|
||||
}
|
||||
client, _, cleanup := InitServerConnection(ctx, t)
|
||||
defer cleanup()
|
||||
if err := PullIfMissing(ctx, client, req.Model); err != nil {
|
||||
t.Fatalf("PullIfMissing failed: %v", err)
|
||||
}
|
||||
DoGenerate(ctx, t, client, req, []string{"russia", "germany", "france", "england", "austria", "prussia"}, 120*time.Second, 10*time.Second)
|
||||
}
|
||||
|
||||
func TestContextExhaustion(t *testing.T) {
|
||||
// Setting NUM_PARALLEL to 1 ensures the allocated context is exactly what
|
||||
// we asked for and there is nothing extra that we could spill over into
|
||||
t.Setenv("OLLAMA_NUM_PARALLEL", "1")
|
||||
|
||||
// Longer needed for small footprint GPUs
|
||||
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
|
||||
defer cancel()
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ import (
|
|||
"github.com/stretchr/testify/require"
|
||||
|
||||
"github.com/ollama/ollama/api"
|
||||
"github.com/ollama/ollama/envconfig"
|
||||
)
|
||||
|
||||
func TestMaxQueue(t *testing.T) {
|
||||
|
|
@ -26,8 +27,12 @@ func TestMaxQueue(t *testing.T) {
|
|||
|
||||
// Note: This test can be quite slow when running in CPU mode, so keep the threadCount low unless your on GPU
|
||||
// Also note that by default Darwin can't sustain > ~128 connections without adjusting limits
|
||||
threadCount := 16
|
||||
t.Setenv("OLLAMA_MAX_QUEUE", strconv.Itoa(threadCount))
|
||||
threadCount := 32
|
||||
if maxQueue := envconfig.MaxQueue(); maxQueue != 0 {
|
||||
threadCount = int(maxQueue)
|
||||
} else {
|
||||
t.Setenv("OLLAMA_MAX_QUEUE", strconv.Itoa(threadCount))
|
||||
}
|
||||
|
||||
req := api.GenerateRequest{
|
||||
Model: "orca-mini",
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ go build -tags avx,cuda .
|
|||
|
||||
### ROCm
|
||||
|
||||
Install [ROCm](https://rocm.docs.amd.com/en/latest/).
|
||||
Install the [CUDA toolkit v11.3.1](https://developer.nvidia.com/cuda-11-3-1-download-archive):
|
||||
|
||||
```shell
|
||||
make ggml_hipblas.so
|
||||
|
|
@ -77,7 +77,7 @@ go build -tags avx,cuda .
|
|||
|
||||
### ROCm
|
||||
|
||||
Install [ROCm](https://rocm.docs.amd.com/en/latest/).
|
||||
Install [ROCm 5.7.1](https://rocm.docs.amd.com/en/docs-5.7.1/).
|
||||
|
||||
```shell
|
||||
make ggml_hipblas.dll
|
||||
|
|
@ -93,7 +93,7 @@ make -j
|
|||
|
||||
## Vendoring
|
||||
|
||||
Ollama currently vendors [llama.cpp](https://github.com/ggerganov/llama.cpp/) and [ggml](https://github.com/ggerganov/ggml) through a vendoring model. While we generally strive to contribute changes back upstream to avoid drift, we cary a small set of patches which are applied to the tracking commit. A set of make targets are available to aid developers in updating to a newer tracking commit, or to work on changes.
|
||||
Ollama currently vendors [llama.cpp](https://github.com/ggerganov/llama.cpp/) and [ggml](https://github.com/ggerganov/ggml) through a vendoring model. While we generally strive to contribute changes back upstream to avoid drift, we cary a small set of patches which are applied to the tracking commit. A set of make targets are available to aid developers in updating to a newer tracking commit, or to work on changes.
|
||||
|
||||
If you update the vendoring code, start by running the following command to establish the tracking llama.cpp repo in the `./vendor/` directory.
|
||||
|
||||
|
|
@ -105,35 +105,35 @@ make apply-patches
|
|||
|
||||
**Pin to new base commit**
|
||||
|
||||
To update to a newer base commit, select the upstream git tag or commit and update `llama/vendoring`
|
||||
To update to a newer base commit, select the upstream git tag or commit and update `llama/vendoring.env`
|
||||
|
||||
#### Applying patches
|
||||
|
||||
When updating to a newer base commit, the existing patches may not apply cleanly and require manual merge resolution.
|
||||
|
||||
Start by applying the patches. If any of the patches have conflicts, the `git am` will stop at the first failure.
|
||||
Start by applying the patches. If any of the patches have conflicts, the `git am` will stop at the first failure.
|
||||
|
||||
```
|
||||
make apply-patches
|
||||
```
|
||||
|
||||
If you see an error message about a conflict, go into the `./vendor/` directory, and perform merge resolution using your preferred tool to the patch commit which failed. Save the file(s) and continue the patch series with `git am --continue` . If any additional patches fail, follow the same pattern until the full patch series is applied. Once finished, run a final `create-patches` and `sync` target to ensure everything is updated.
|
||||
If you see an error message about a conflict, go into the `./vendor/` directory, and perform merge resolution using your preferred tool to the patch commit which failed. Save the file(s) and continue the patch series with `git am --continue` . If any additional patches fail, follow the same pattern until the full patch series is applied. Once finished, run a final `create-patches` and `sync` target to ensure everything is updated.
|
||||
|
||||
```
|
||||
make create-patches sync
|
||||
```
|
||||
|
||||
Build and test Ollama, and make any necessary changes to the Go code based on the new base commit. Submit your PR to the Ollama repo.
|
||||
Build and test Ollama, and make any necessary changes to the Go code based on the new base commit. Submit your PR to the Ollama repo.
|
||||
|
||||
### Generating Patches
|
||||
|
||||
When working on new fixes or features that impact vendored code, use the following model. First get a clean tracking repo with all current patches applied:
|
||||
When working on new fixes or features that impact vendored code, use the following model. First get a clean tracking repo with all current patches applied:
|
||||
|
||||
```
|
||||
make apply-patches
|
||||
```
|
||||
|
||||
Now edit the upstream native code in the `./vendor/` directory. You do not need to commit every change in order to build, a dirty working tree in the tracking repo is OK while developing. Simply save in your editor, and run the following to refresh the vendored code with your changes, build the backend(s) and build ollama:
|
||||
Now edit the upstream native code in the `./vendor/` directory. You do not need to commit every change in order to build, a dirty working tree in the tracking repo is OK while developing. Simply save in your editor, and run the following to refresh the vendored code with your changes, build the backend(s) and build ollama:
|
||||
|
||||
```
|
||||
make sync
|
||||
|
|
@ -142,9 +142,9 @@ go build .
|
|||
```
|
||||
|
||||
> [!IMPORTANT]
|
||||
> Do **NOT** run `apply-patches` while you're iterating as that will reset the tracking repo. It will detect a dirty tree and abort, but if your tree is clean and you accidentally ran this target, use `git reflog` to recover your commit(s).
|
||||
> Do **NOT** run `apply-patches` while you're iterating as that will reset the tracking repo. It will detect a dirty tree and abort, but if your tree is clean and you accidentally ran this target, use `git reflog` to recover your commit(s).
|
||||
|
||||
Iterate until you're ready to submit PRs. Once your code is ready, commit a change in the `./vendor/` directory, then generate the patches for ollama with
|
||||
Iterate until you're ready to submit PRs. Once your code is ready, commit a change in the `./vendor/` directory, then generate the patches for ollama with
|
||||
|
||||
```
|
||||
make create-patches
|
||||
|
|
|
|||
|
|
@ -21,8 +21,6 @@ package llama
|
|||
#cgo cuda CFLAGS: -fPIE -DGGML_USE_CUDA -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_CUDA_MMV_Y=1 -DGGML_BUILD=1
|
||||
#cgo cuda CXXFLAGS: -DGGML_USE_CUDA -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_CUDA_MMV_Y=1 -DGGML_BUILD=1
|
||||
#cgo cuda CXXFLAGS: -DGGML_USE_CUDA -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_CUDA_MMV_Y=1 -DGGML_BUILD=1
|
||||
#cgo cuda_jetpack5 LDFLAGS: -lggml_cuda_jetpack5 -L/usr/local/cuda-11/lib64
|
||||
#cgo cuda_jetpack6 LDFLAGS: -lggml_cuda_jetpack6 -L/usr/local/cuda-12/lib64
|
||||
#cgo cuda_v11 LDFLAGS: -lggml_cuda_v11 -L/usr/local/cuda-11/lib64
|
||||
#cgo cuda_v12 LDFLAGS: -lggml_cuda_v12 -L/usr/local/cuda-12/lib64
|
||||
#cgo darwin,amd64 CFLAGS: -Wno-incompatible-pointer-types-discards-qualifiers
|
||||
|
|
@ -38,8 +36,8 @@ package llama
|
|||
#cgo linux CXXFLAGS: -D_GNU_SOURCE
|
||||
#cgo linux,amd64 LDFLAGS: -L${SRCDIR}/build/Linux/amd64
|
||||
#cgo linux,amd64 LDFLAGS: -L${SRCDIR}/build/Linux/amd64
|
||||
#cgo linux,arm64 CFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA
|
||||
#cgo linux,arm64 CXXFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA
|
||||
#cgo linux,arm64 CFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA -D__ARM_FEATURE_MATMUL_INT8
|
||||
#cgo linux,arm64 CXXFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA -D__ARM_FEATURE_MATMUL_INT8
|
||||
#cgo linux,arm64 LDFLAGS: -L${SRCDIR}/build/Linux/arm64
|
||||
#cgo linux,arm64,sve CFLAGS: -march=armv8.6-a+sve
|
||||
#cgo linux,arm64,sve CXXFLAGS: -march=armv8.6-a+sve
|
||||
|
|
@ -157,7 +155,9 @@ type Context struct {
|
|||
numThreads int
|
||||
}
|
||||
|
||||
var ErrKvCacheFull = errors.New("could not find a kv cache slot")
|
||||
func (c *Context) KvCacheClear() {
|
||||
C.llama_kv_cache_clear(c.c)
|
||||
}
|
||||
|
||||
func (c *Context) Decode(batch *Batch) error {
|
||||
// Positive return values does not mean a fatal error, but rather a warning.
|
||||
|
|
@ -171,7 +171,7 @@ func (c *Context) Decode(batch *Batch) error {
|
|||
}
|
||||
|
||||
if code > 0 {
|
||||
return ErrKvCacheFull
|
||||
return fmt.Errorf("could not find a KV slot for the batch - try reducing the size of the batch or increase the context. code: %d", code)
|
||||
}
|
||||
|
||||
return nil
|
||||
|
|
@ -193,14 +193,6 @@ func (c *Context) KvCacheSeqCp(srcSeqId int, dstSeqId int, p0 int, p1 int) {
|
|||
C.llama_kv_cache_seq_cp(c.c, C.int(srcSeqId), C.int(dstSeqId), C.int(p0), C.int(p1))
|
||||
}
|
||||
|
||||
func (c *Context) KvCacheClear() {
|
||||
C.llama_kv_cache_clear(c.c)
|
||||
}
|
||||
|
||||
func (c *Context) KvCacheDefrag() {
|
||||
C.llama_kv_cache_defrag(c.c)
|
||||
}
|
||||
|
||||
// Get the embeddings for a sequence id
|
||||
func (c *Context) GetEmbeddingsSeq(seqId int) []float32 {
|
||||
embeddings := unsafe.Pointer(C.llama_get_embeddings_seq(c.c, C.int(seqId)))
|
||||
|
|
@ -390,8 +382,6 @@ func (b *Batch) Add(token int, embed []float32, pos int, logits bool, seqIds ...
|
|||
|
||||
if logits {
|
||||
unsafe.Slice(b.c.logits, b.allocSize())[b.c.n_tokens] = 1
|
||||
} else {
|
||||
unsafe.Slice(b.c.logits, b.allocSize())[b.c.n_tokens] = 0
|
||||
}
|
||||
|
||||
b.c.n_tokens += 1
|
||||
|
|
@ -608,10 +598,6 @@ func (c *Context) SetCrossAttention(state bool) {
|
|||
C.llama_set_cross_attention(c.c, C.bool(state))
|
||||
}
|
||||
|
||||
func (c *Context) Synchronize() {
|
||||
C.llama_synchronize(c.c)
|
||||
}
|
||||
|
||||
// sampling
|
||||
// TODO: this is a temporary wrapper to allow calling C++ code from CGo
|
||||
type SamplingContext struct {
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ GPU_COMPILER_CFLAGS_LINUX = $(CFLAGS) -Xcompiler -fPIC -D_GNU_SOURCE
|
|||
GPU_COMPILER_CXXFLAGS_WIN = $(CXXFLAGS) -D_WIN32_WINNT=0x602
|
||||
GPU_COMPILER_CXXFLAGS_LINUX = $(CXXFLAGS) -Xcompiler -fPIC -D_GNU_SOURCE
|
||||
GPU_LIBS = $(sort $(wildcard $(addsuffix *.$(SHARED_EXT)*,$(addprefix $(GPU_LIB_DIR)/$(SHARED_PREFIX),$(GPU_RUNNER_LIBS_SHORT)))))
|
||||
GPU_DIST_DEPS_LIBS= $(sort $(addprefix $(DIST_GPU_RUNNER_DEPS_DIR)/,$(notdir $(GPU_LIBS))))
|
||||
GPU_DIST_DEPS_LIBS= $(sort $(addprefix $(DIST_LIB_DIR)/,$(notdir $(GPU_LIBS))))
|
||||
|
||||
ifeq ($(OS),linux)
|
||||
CUDA_PATH?=/usr/local/cuda
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@ package main
|
|||
|
||||
import (
|
||||
"errors"
|
||||
"fmt"
|
||||
"log/slog"
|
||||
"reflect"
|
||||
"time"
|
||||
|
|
@ -23,11 +22,7 @@ type InputCache struct {
|
|||
lc *llama.Context
|
||||
}
|
||||
|
||||
func NewInputCache(lc *llama.Context, kvSize int, numSlots int, multiUserCache bool) (*InputCache, error) {
|
||||
if kvSize/numSlots < 1 {
|
||||
return nil, fmt.Errorf("must have at least one kv cache entry per parallel sequence (kv: %v parallel: %v)", kvSize, numSlots)
|
||||
}
|
||||
|
||||
func NewInputCache(lc *llama.Context, kvSize int, numSlots int, multiUserCache bool) *InputCache {
|
||||
slots := make([]InputCacheSlot, numSlots)
|
||||
|
||||
for i := range slots {
|
||||
|
|
@ -42,7 +37,7 @@ func NewInputCache(lc *llama.Context, kvSize int, numSlots int, multiUserCache b
|
|||
slots: slots,
|
||||
multiUserCache: multiUserCache,
|
||||
lc: lc,
|
||||
}, nil
|
||||
}
|
||||
}
|
||||
|
||||
// Locking: Operations on InputCacheSlot (including finding one
|
||||
|
|
@ -63,7 +58,7 @@ type InputCacheSlot struct {
|
|||
lastUsed time.Time
|
||||
}
|
||||
|
||||
func (c *InputCache) LoadCacheSlot(prompt []input, cachePrompt bool) (*InputCacheSlot, []input, error) {
|
||||
func (c *InputCache) LoadCacheSlot(prompt []input, cachePrompt bool) (*InputCacheSlot, []input, int, error) {
|
||||
var slot *InputCacheSlot
|
||||
var numPast int
|
||||
var err error
|
||||
|
|
@ -80,7 +75,7 @@ func (c *InputCache) LoadCacheSlot(prompt []input, cachePrompt bool) (*InputCach
|
|||
slot, numPast, err = c.findBestCacheSlot(prompt)
|
||||
}
|
||||
if err != nil {
|
||||
return nil, nil, err
|
||||
return nil, nil, 0, err
|
||||
}
|
||||
|
||||
if !cachePrompt {
|
||||
|
|
@ -107,7 +102,7 @@ func (c *InputCache) LoadCacheSlot(prompt []input, cachePrompt bool) (*InputCach
|
|||
prompt = prompt[numPast:]
|
||||
slot.Inputs = slot.Inputs[:numPast]
|
||||
|
||||
return slot, prompt, nil
|
||||
return slot, prompt, numPast, nil
|
||||
}
|
||||
|
||||
func (c *InputCache) findLongestCacheSlot(prompt []input) (*InputCacheSlot, int, error) {
|
||||
|
|
@ -199,48 +194,14 @@ func countCommonPrefix(a []input, b []input) int {
|
|||
return count
|
||||
}
|
||||
|
||||
func (c *InputCache) ShiftDiscard(inputLen int, numKeep int) int {
|
||||
targetFree := (c.numCtx - numKeep) / 2
|
||||
targetFree = max(targetFree, 1)
|
||||
|
||||
currentFree := c.numCtx - inputLen
|
||||
discard := targetFree - currentFree
|
||||
|
||||
if discard < 0 {
|
||||
discard = 0
|
||||
}
|
||||
|
||||
return discard
|
||||
}
|
||||
|
||||
// Frees up space in the KV cache by deleting the oldest half of history and shifting
|
||||
// the newest half into that space (saving numKeep inputs at the beginning).
|
||||
//
|
||||
// Assumes that at least 1 entry can be freed up by shifting (i.e. numKeep < numCtx)
|
||||
func (c *InputCache) ShiftCacheSlot(slot *InputCacheSlot, numKeep int) error {
|
||||
if numKeep >= c.numCtx {
|
||||
return fmt.Errorf("unable to shift context - keep exceeds context (keep: %v context: %v)", numKeep, c.numCtx)
|
||||
}
|
||||
|
||||
discard := c.ShiftDiscard(len(slot.Inputs), numKeep)
|
||||
|
||||
if discard <= 0 {
|
||||
return nil
|
||||
}
|
||||
|
||||
slog.Debug("context limit hit - shifting", "id", slot.Id, "limit", c.numCtx, "input", len(slot.Inputs),
|
||||
"keep", numKeep, "discard", discard)
|
||||
|
||||
func (c *InputCache) ShiftCacheSlot(slot *InputCacheSlot, numKeep int, numDiscard int, numPast int) {
|
||||
// TODO (jessegross): KV cache removal can fail for certain types of models
|
||||
if !c.lc.KvCacheSeqRm(slot.Id, numKeep, numKeep+discard) {
|
||||
return fmt.Errorf("unable to remove old kv cache entries (id: %v, keep: %v discard: %v)", slot.Id, numKeep, discard)
|
||||
}
|
||||
c.lc.KvCacheSeqAdd(slot.Id, numKeep+discard, len(slot.Inputs), -discard)
|
||||
// server.cpp doesn't handle this, though we can be more graceful
|
||||
c.lc.KvCacheSeqRm(slot.Id, numKeep, numKeep+numDiscard)
|
||||
c.lc.KvCacheSeqAdd(slot.Id, numKeep+numDiscard, numPast, -numDiscard)
|
||||
|
||||
for i := numKeep + discard; i < len(slot.Inputs); i++ {
|
||||
slot.Inputs[i-discard] = slot.Inputs[i]
|
||||
for i := numKeep + numDiscard; i < len(slot.Inputs); i++ {
|
||||
slot.Inputs[i-numDiscard] = slot.Inputs[i]
|
||||
}
|
||||
slot.Inputs = slot.Inputs[:len(slot.Inputs)-discard]
|
||||
|
||||
return nil
|
||||
slot.Inputs = slot.Inputs[:len(slot.Inputs)-numDiscard]
|
||||
}
|
||||
|
|
|
|||
|
|
@ -227,66 +227,3 @@ func TestFindCacheSlot(t *testing.T) {
|
|||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestShiftDiscard(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
numCtx int
|
||||
numKeep int
|
||||
inputLen int
|
||||
expected int
|
||||
}{
|
||||
{
|
||||
name: "Shift",
|
||||
numCtx: 2048,
|
||||
numKeep: 5,
|
||||
inputLen: 2048,
|
||||
expected: 1021,
|
||||
},
|
||||
{
|
||||
name: "Max Keep",
|
||||
numCtx: 2048,
|
||||
numKeep: 2047,
|
||||
inputLen: 2048,
|
||||
expected: 1,
|
||||
},
|
||||
{
|
||||
name: "No Keep",
|
||||
numCtx: 2048,
|
||||
numKeep: 0,
|
||||
inputLen: 2048,
|
||||
expected: 1024,
|
||||
},
|
||||
{
|
||||
name: "Truncate",
|
||||
numCtx: 2048,
|
||||
numKeep: 5,
|
||||
inputLen: 5000,
|
||||
expected: 3973,
|
||||
},
|
||||
{
|
||||
name: "Truncate Keep",
|
||||
numCtx: 2048,
|
||||
numKeep: 2047,
|
||||
inputLen: 5000,
|
||||
expected: 2953,
|
||||
},
|
||||
{
|
||||
name: "No Op",
|
||||
numCtx: 2048,
|
||||
numKeep: 5,
|
||||
inputLen: 512,
|
||||
expected: 0,
|
||||
},
|
||||
}
|
||||
|
||||
for _, tt := range tests {
|
||||
t.Run(tt.name, func(t *testing.T) {
|
||||
c := InputCache{numCtx: tt.numCtx}
|
||||
result := c.ShiftDiscard(tt.inputLen, tt.numKeep)
|
||||
if result != tt.expected {
|
||||
t.Errorf("shiftDiscard(ctx: %v, keep: %v input: %v): have %v; want %v", tt.numCtx, tt.numKeep, tt.inputLen, result, tt.expected)
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -20,8 +20,6 @@ import (
|
|||
"time"
|
||||
"unicode/utf8"
|
||||
|
||||
"golang.org/x/sync/semaphore"
|
||||
|
||||
"github.com/ollama/ollama/api"
|
||||
"github.com/ollama/ollama/llama"
|
||||
)
|
||||
|
|
@ -36,6 +34,9 @@ type input struct {
|
|||
}
|
||||
|
||||
type Sequence struct {
|
||||
// number of inputs evaluated
|
||||
numPast int
|
||||
|
||||
// batch index
|
||||
iBatch int
|
||||
|
||||
|
|
@ -45,9 +46,6 @@ type Sequence struct {
|
|||
// prompt inputs left to evaluate
|
||||
inputs []input
|
||||
|
||||
// inputs that have been added to a batch but not yet submitted to Decode
|
||||
pendingInputs []input
|
||||
|
||||
// tokens that have been generated but not returned yet (e.g. for stop sequences)
|
||||
pendingResponses []string
|
||||
|
||||
|
|
@ -114,19 +112,20 @@ func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequen
|
|||
params.numKeep = len(inputs)
|
||||
}
|
||||
|
||||
if s.model.AddBOSToken() {
|
||||
params.numKeep += 1
|
||||
if !params.embedding {
|
||||
// Subtracting 4 ensures that at least 1 input can be discarded during shift
|
||||
params.numKeep = min(params.numKeep, s.cache.numCtx-4)
|
||||
params.numKeep += s.bosToken
|
||||
} else {
|
||||
// Embeddings are 1 shot - just truncate to the context window, without ever shifting
|
||||
params.numKeep = min(params.numKeep, s.cache.numCtx)
|
||||
}
|
||||
|
||||
// Ensure that at least 1 input can be discarded during shift
|
||||
params.numKeep = min(params.numKeep, s.cache.numCtx-1)
|
||||
|
||||
// truncate to fit in context window
|
||||
if len(inputs) > s.cache.numCtx {
|
||||
discard := len(inputs) - s.cache.numCtx
|
||||
slog.Warn("truncating input prompt", "limit", s.cache.numCtx, "prompt", len(inputs), "numKeep", params.numKeep)
|
||||
newInputs := inputs[:params.numKeep]
|
||||
newInputs = append(newInputs, inputs[params.numKeep+discard:]...)
|
||||
|
||||
slog.Warn("truncating input prompt", "limit", s.cache.numCtx, "prompt", len(inputs), "keep", params.numKeep, "new", len(newInputs))
|
||||
newInputs = append(newInputs, inputs[len(inputs)-s.cache.numCtx+params.numKeep:]...)
|
||||
inputs = newInputs
|
||||
}
|
||||
|
||||
|
|
@ -164,26 +163,22 @@ func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequen
|
|||
// generating image embeddings for each image
|
||||
func (s *Server) inputs(prompt string, images []ImageData) ([]input, error) {
|
||||
var inputs []input
|
||||
var parts []string
|
||||
var matches [][]string
|
||||
|
||||
if s.image != nil {
|
||||
re := regexp.MustCompile(`\[img-(\d+)\]`)
|
||||
parts = re.Split(prompt, -1)
|
||||
matches = re.FindAllStringSubmatch(prompt, -1)
|
||||
} else {
|
||||
parts = []string{prompt}
|
||||
}
|
||||
re := regexp.MustCompile(`\[img-(\d+)\]`)
|
||||
parts := re.Split(prompt, -1)
|
||||
matches := re.FindAllStringSubmatch(prompt, -1)
|
||||
|
||||
for i, part := range parts {
|
||||
// text - tokenize
|
||||
tokens, err := s.lc.Model().Tokenize(part, i == 0, true)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if strings.TrimSpace(part) != "" {
|
||||
tokens, err := s.lc.Model().Tokenize(part, i == 0, true)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
for _, t := range tokens {
|
||||
inputs = append(inputs, input{token: t})
|
||||
for _, t := range tokens {
|
||||
inputs = append(inputs, input{token: t})
|
||||
}
|
||||
}
|
||||
|
||||
// image - generate image embedding
|
||||
|
|
@ -217,51 +212,41 @@ func (s *Server) inputs(prompt string, images []ImageData) ([]input, error) {
|
|||
}
|
||||
|
||||
type Server struct {
|
||||
// is the server ready to process requests?
|
||||
// protects access to model and image
|
||||
ready sync.WaitGroup
|
||||
|
||||
// loaded model
|
||||
model *llama.Model
|
||||
lc *llama.Context
|
||||
|
||||
// image model context for multi-modal models
|
||||
// required for image embeddings
|
||||
image *ImageContext
|
||||
|
||||
// status for external health reporting - loading, ready to serve, etc.
|
||||
status ServerStatus
|
||||
|
||||
// current progress on loading the model
|
||||
progress float32
|
||||
|
||||
// number of simultaneous requests to handle
|
||||
parallel int
|
||||
|
||||
// maximum number of elements in a batch (per sequence)
|
||||
// TODO (jmorganca): make this n_batch
|
||||
batchSize int
|
||||
|
||||
// protects access to everything below this line
|
||||
// this is context state needed for decoding
|
||||
mu sync.Mutex
|
||||
// parallel is the number of parallel requests to handle
|
||||
parallel int
|
||||
|
||||
// indicates that data is ready for processing
|
||||
cond *sync.Cond
|
||||
|
||||
// decoding state
|
||||
lc *llama.Context
|
||||
|
||||
// the list of simultaneous sequences being evaluated
|
||||
// seqs is the list of parallel sequences being evaluated
|
||||
// TODO (jmorganca): this can probably be moved into run()
|
||||
seqs []*Sequence
|
||||
|
||||
// seqs can have a maximum of parallel entries, which
|
||||
// is enfoced by seqSem
|
||||
seqsSem *semaphore.Weighted
|
||||
|
||||
// KV cache
|
||||
cache *InputCache
|
||||
|
||||
// does this model require a beginning of sequence token?
|
||||
bosToken int
|
||||
|
||||
// next sequence for prompt processing to avoid starvation
|
||||
nextSeq int
|
||||
|
||||
// is the server ready to process requests?
|
||||
ready sync.WaitGroup
|
||||
|
||||
mu sync.Mutex
|
||||
|
||||
cond *sync.Cond
|
||||
|
||||
progress float32
|
||||
|
||||
status ServerStatus
|
||||
}
|
||||
|
||||
func (s *Server) allNil() bool {
|
||||
|
|
@ -273,6 +258,18 @@ func (s *Server) allNil() bool {
|
|||
return true
|
||||
}
|
||||
|
||||
func (s *Server) shiftContext(seq *Sequence) {
|
||||
numLeft := seq.numPast - seq.numKeep
|
||||
numDiscard := numLeft / 2
|
||||
|
||||
slog.Debug("context limit hit - shifting", "limit", s.cache.numCtx, "numPast", seq.numPast,
|
||||
"numKeep", seq.numKeep, "numLeft", numLeft, "numDiscard", numDiscard)
|
||||
|
||||
s.cache.ShiftCacheSlot(seq.cache, seq.numKeep, numDiscard, seq.numPast)
|
||||
|
||||
seq.numPast -= numDiscard
|
||||
}
|
||||
|
||||
func flushPending(seq *Sequence) bool {
|
||||
joined := strings.Join(seq.pendingResponses, "")
|
||||
seq.pendingResponses = []string{}
|
||||
|
|
@ -308,7 +305,6 @@ func (s *Server) removeSequence(seqIndex int, reason string) {
|
|||
close(seq.embedding)
|
||||
seq.cache.InUse = false
|
||||
s.seqs[seqIndex] = nil
|
||||
s.seqsSem.Release(1)
|
||||
}
|
||||
|
||||
func (s *Server) run(ctx context.Context) {
|
||||
|
|
@ -339,11 +335,7 @@ func (s *Server) run(ctx context.Context) {
|
|||
case <-ctx.Done():
|
||||
return
|
||||
default:
|
||||
err := s.processBatch(tokenBatch, embedBatch)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
s.processBatch(tokenBatch, embedBatch)
|
||||
tokenBatch.Clear()
|
||||
embedBatch.Clear()
|
||||
}
|
||||
|
|
@ -357,7 +349,7 @@ func (s *Server) run(ctx context.Context) {
|
|||
// these should instead be handled by the handlers
|
||||
// it should only be responsible for accepting tokens or embeddings and
|
||||
// processing batches as fast as possible
|
||||
func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch) error {
|
||||
func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch) {
|
||||
s.mu.Lock()
|
||||
for s.allNil() {
|
||||
s.cond.Wait() // Wait until an item is added
|
||||
|
|
@ -377,23 +369,17 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
|
|||
}
|
||||
|
||||
// if past the num predict limit
|
||||
if seq.numPredict > 0 && seq.numPredicted >= seq.numPredict {
|
||||
if seq.numPredict > 0 && seq.numPredicted > seq.numPredict {
|
||||
s.removeSequence(seqIdx, "limit")
|
||||
continue
|
||||
}
|
||||
|
||||
for i, input := range seq.inputs {
|
||||
if len(seq.cache.Inputs)+len(seq.pendingInputs)+1 > s.cache.numCtx {
|
||||
if len(seq.pendingInputs) == 0 {
|
||||
err := s.cache.ShiftCacheSlot(seq.cache, seq.numKeep)
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
} else {
|
||||
break
|
||||
}
|
||||
}
|
||||
if seq.numPast+len(seq.inputs) > s.cache.numCtx {
|
||||
s.shiftContext(seq)
|
||||
}
|
||||
|
||||
var numInputsProcessed int
|
||||
for i, input := range seq.inputs {
|
||||
embedding := input.embed != nil
|
||||
|
||||
// If we don't currently have a batch, use one of the correct type and
|
||||
|
|
@ -417,37 +403,28 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
|
|||
}
|
||||
|
||||
crossAttention = seq.crossAttention
|
||||
batch.Add(input.token, input.embed, len(seq.cache.Inputs)+len(seq.pendingInputs), i+1 == len(seq.inputs), seq.cache.Id)
|
||||
seq.pendingInputs = append(seq.pendingInputs, input)
|
||||
seq.iBatch = batch.NumTokens() - 1
|
||||
batch.Add(input.token, input.embed, seq.numPast, numInputsProcessed+1 == len(seq.inputs), seq.cache.Id)
|
||||
seq.numPast++
|
||||
numInputsProcessed++
|
||||
}
|
||||
|
||||
seq.inputs = seq.inputs[len(seq.pendingInputs):]
|
||||
if numInputsProcessed > 0 {
|
||||
seq.cache.Inputs = append(seq.cache.Inputs, seq.inputs[:numInputsProcessed]...)
|
||||
seq.inputs = seq.inputs[numInputsProcessed:]
|
||||
seq.iBatch = batch.NumTokens() - 1
|
||||
}
|
||||
}
|
||||
|
||||
if batch == nil || batch.NumTokens() == 0 {
|
||||
return nil
|
||||
return
|
||||
}
|
||||
|
||||
s.lc.SetCrossAttention(crossAttention)
|
||||
|
||||
err := s.lc.Decode(batch)
|
||||
if err != nil {
|
||||
if errors.Is(err, llama.ErrKvCacheFull) {
|
||||
slog.Debug("defragmenting kv cache")
|
||||
s.cache.lc.KvCacheDefrag()
|
||||
err = s.lc.Decode(batch)
|
||||
}
|
||||
if err != nil {
|
||||
return fmt.Errorf("failed to decode batch: %w", err)
|
||||
}
|
||||
}
|
||||
|
||||
if crossAttention {
|
||||
// synchronize state to ensure the cross attention batch is complete.
|
||||
// needed specifically for multi-GPU systems otherwise an inflight
|
||||
// task may be incorrectly invalidated causing a crash
|
||||
s.lc.Synchronize()
|
||||
slog.Error("failed to decode batch", "error", err)
|
||||
return
|
||||
}
|
||||
|
||||
for i, seq := range s.seqs {
|
||||
|
|
@ -455,12 +432,6 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
|
|||
continue
|
||||
}
|
||||
|
||||
// After calling Decode, pending inputs are now in the cache
|
||||
if len(seq.pendingInputs) > 0 {
|
||||
seq.cache.Inputs = append(seq.cache.Inputs, seq.pendingInputs...)
|
||||
seq.pendingInputs = []input{}
|
||||
}
|
||||
|
||||
// don't sample prompt processing
|
||||
if len(seq.inputs) != 0 {
|
||||
continue
|
||||
|
|
@ -473,7 +444,7 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
|
|||
|
||||
// if done processing the prompt, generate an embedding and return
|
||||
if seq.embeddingOnly {
|
||||
embed := s.lc.GetEmbeddingsSeq(seq.cache.Id)
|
||||
embed := s.lc.GetEmbeddingsSeq(i)
|
||||
if embed == nil {
|
||||
embed = s.lc.GetEmbeddingsIth(seq.iBatch)
|
||||
}
|
||||
|
|
@ -543,8 +514,6 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
|
|||
s.removeSequence(i, "connection")
|
||||
}
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
// TODO (jmorganca): use structs from the api package to avoid duplication
|
||||
|
|
@ -658,21 +627,12 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
|
|||
return
|
||||
}
|
||||
|
||||
// Ensure there is a place to put the sequence, released when removed from s.seqs
|
||||
if err := s.seqsSem.Acquire(r.Context(), 1); err != nil {
|
||||
if errors.Is(err, context.Canceled) {
|
||||
slog.Info("aborting completion request due to client closing the connection")
|
||||
} else {
|
||||
slog.Error("Failed to acquire semaphore", "error", err)
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// TODO (jmorganca): add to sequence queue instead of
|
||||
// failing if a slot isn't available
|
||||
s.mu.Lock()
|
||||
found := false
|
||||
for i, sq := range s.seqs {
|
||||
if sq == nil {
|
||||
seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
|
||||
seq.cache, seq.inputs, seq.numPast, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
|
||||
if err != nil {
|
||||
s.mu.Unlock()
|
||||
http.Error(w, fmt.Sprintf("Failed to load cache: %v", err), http.StatusInternalServerError)
|
||||
|
|
@ -683,17 +643,11 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
|
|||
|
||||
s.seqs[i] = seq
|
||||
s.cond.Signal()
|
||||
found = true
|
||||
break
|
||||
}
|
||||
}
|
||||
s.mu.Unlock()
|
||||
|
||||
if !found {
|
||||
http.Error(w, "could not find an available sequence", http.StatusInternalServerError)
|
||||
return
|
||||
}
|
||||
|
||||
for {
|
||||
select {
|
||||
case <-r.Context().Done():
|
||||
|
|
@ -757,21 +711,11 @@ func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
|
|||
return
|
||||
}
|
||||
|
||||
// Ensure there is a place to put the sequence, released when removed from s.seqs
|
||||
if err := s.seqsSem.Acquire(r.Context(), 1); err != nil {
|
||||
if errors.Is(err, context.Canceled) {
|
||||
slog.Info("aborting embeddings request due to client closing the connection")
|
||||
} else {
|
||||
slog.Error("Failed to acquire semaphore", "error", err)
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// TODO (jessegross): Wait for a free slot instead of failing and blocking forever
|
||||
s.mu.Lock()
|
||||
found := false
|
||||
for i, sq := range s.seqs {
|
||||
if sq == nil {
|
||||
seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
|
||||
seq.cache, seq.inputs, seq.numPast, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
|
||||
if err != nil {
|
||||
s.mu.Unlock()
|
||||
http.Error(w, fmt.Sprintf("Failed to load cache: %v", err), http.StatusInternalServerError)
|
||||
|
|
@ -779,17 +723,11 @@ func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
|
|||
}
|
||||
s.seqs[i] = seq
|
||||
s.cond.Signal()
|
||||
found = true
|
||||
break
|
||||
}
|
||||
}
|
||||
s.mu.Unlock()
|
||||
|
||||
if !found {
|
||||
http.Error(w, "could not find an available sequence", http.StatusInternalServerError)
|
||||
return
|
||||
}
|
||||
|
||||
embedding := <-seq.embedding
|
||||
|
||||
if err := json.NewEncoder(w).Encode(&EmbeddingResponse{
|
||||
|
|
@ -833,21 +771,10 @@ func (s *Server) health(w http.ResponseWriter, r *http.Request) {
|
|||
}
|
||||
}
|
||||
|
||||
type multiLPath []string
|
||||
|
||||
func (m *multiLPath) Set(value string) error {
|
||||
*m = append(*m, value)
|
||||
return nil
|
||||
}
|
||||
|
||||
func (m *multiLPath) String() string {
|
||||
return strings.Join(*m, ", ")
|
||||
}
|
||||
|
||||
func (s *Server) loadModel(
|
||||
params llama.ModelParams,
|
||||
mpath string,
|
||||
lpath multiLPath,
|
||||
lpath string,
|
||||
ppath string,
|
||||
kvSize int,
|
||||
flashAttention bool,
|
||||
|
|
@ -868,15 +795,17 @@ func (s *Server) loadModel(
|
|||
panic(err)
|
||||
}
|
||||
|
||||
if lpath.String() != "" {
|
||||
for _, path := range lpath {
|
||||
err := s.model.ApplyLoraFromFile(s.lc, path, 1.0, threads)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
if lpath != "" {
|
||||
err := s.model.ApplyLoraFromFile(s.lc, lpath, 1.0, threads)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
}
|
||||
|
||||
if s.model.AddBOSToken() {
|
||||
s.bosToken = 1
|
||||
}
|
||||
|
||||
if ppath != "" {
|
||||
var err error
|
||||
s.image, err = NewImageContext(s.lc, ppath)
|
||||
|
|
@ -885,10 +814,7 @@ func (s *Server) loadModel(
|
|||
}
|
||||
}
|
||||
|
||||
s.cache, err = NewInputCache(s.lc, kvSize, s.parallel, multiUserCache)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
s.cache = NewInputCache(s.lc, kvSize, s.parallel, multiUserCache)
|
||||
|
||||
s.status = ServerStatusReady
|
||||
s.ready.Done()
|
||||
|
|
@ -903,6 +829,7 @@ func main() {
|
|||
mainGpu := flag.Int("main-gpu", 0, "Main GPU")
|
||||
flashAttention := flag.Bool("flash-attn", false, "Enable flash attention")
|
||||
kvSize := flag.Int("ctx-size", 2048, "Context (or KV cache) size")
|
||||
lpath := flag.String("lora", "", "Path to lora layer file")
|
||||
port := flag.Int("port", 8080, "Port to expose the server on")
|
||||
threads := flag.Int("threads", runtime.NumCPU(), "Number of threads to use during generation")
|
||||
verbose := flag.Bool("verbose", false, "verbose output (default: disabled)")
|
||||
|
|
@ -912,9 +839,6 @@ func main() {
|
|||
multiUserCache := flag.Bool("multiuser-cache", false, "optimize input cache algorithm for multiple users")
|
||||
requirements := flag.Bool("requirements", false, "print json requirement information")
|
||||
|
||||
var lpaths multiLPath
|
||||
flag.Var(&lpaths, "lora", "Path to lora layer file (can be specified multiple times)")
|
||||
|
||||
flag.Parse()
|
||||
if *requirements {
|
||||
printRequirements(os.Stdout)
|
||||
|
|
@ -943,7 +867,6 @@ func main() {
|
|||
batchSize: *batchSize,
|
||||
parallel: *parallel,
|
||||
seqs: make([]*Sequence, *parallel),
|
||||
seqsSem: semaphore.NewWeighted(int64(*parallel)),
|
||||
status: ServerStatusLoadingModel,
|
||||
}
|
||||
|
||||
|
|
@ -961,7 +884,7 @@ func main() {
|
|||
params := llama.ModelParams{
|
||||
NumGpuLayers: *nGpuLayers,
|
||||
MainGpu: *mainGpu,
|
||||
UseMmap: !*noMmap && lpaths.String() == "",
|
||||
UseMmap: !*noMmap && *lpath == "",
|
||||
UseMlock: *mlock,
|
||||
TensorSplit: tensorSplitFloats,
|
||||
Progress: func(progress float32) {
|
||||
|
|
@ -970,7 +893,7 @@ func main() {
|
|||
}
|
||||
|
||||
server.ready.Add(1)
|
||||
go server.loadModel(params, *mpath, lpaths, *ppath, *kvSize, *flashAttention, *threads, *multiUserCache)
|
||||
go server.loadModel(params, *mpath, *lpath, *ppath, *kvSize, *flashAttention, *threads, *multiUserCache)
|
||||
|
||||
server.cond = sync.NewCond(&server.mu)
|
||||
|
||||
|
|
|
|||
|
|
@ -32,10 +32,9 @@ const (
|
|||
fileTypeIQ1_S
|
||||
fileTypeIQ4_NL
|
||||
fileTypeIQ3_S
|
||||
fileTypeIQ3_M
|
||||
fileTypeIQ2_S
|
||||
fileTypeIQ2_M
|
||||
fileTypeIQ4_XS
|
||||
fileTypeIQ2_M
|
||||
fileTypeIQ1_M
|
||||
fileTypeBF16
|
||||
|
||||
|
|
@ -94,8 +93,6 @@ func ParseFileType(s string) (fileType, error) {
|
|||
return fileTypeIQ4_NL, nil
|
||||
case "IQ3_S":
|
||||
return fileTypeIQ3_S, nil
|
||||
case "IQ3_M":
|
||||
return fileTypeIQ3_M, nil
|
||||
case "IQ2_S":
|
||||
return fileTypeIQ2_S, nil
|
||||
case "IQ4_XS":
|
||||
|
|
@ -163,8 +160,6 @@ func (t fileType) String() string {
|
|||
return "IQ4_NL"
|
||||
case fileTypeIQ3_S:
|
||||
return "IQ3_S"
|
||||
case fileTypeIQ3_M:
|
||||
return "IQ3_M"
|
||||
case fileTypeIQ2_S:
|
||||
return "IQ2_S"
|
||||
case fileTypeIQ4_XS:
|
||||
|
|
|
|||
|
|
@ -144,6 +144,10 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
|
|||
// Loop through potential servers
|
||||
finalErr := errors.New("no suitable llama servers found")
|
||||
|
||||
if len(adapters) > 1 {
|
||||
return nil, errors.New("ollama supports only one lora adapter, but multiple were provided")
|
||||
}
|
||||
|
||||
rDir, err := runners.Refresh(build.EmbedFS)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
|
|
@ -197,9 +201,8 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
|
|||
}
|
||||
|
||||
if len(adapters) > 0 {
|
||||
for _, adapter := range adapters {
|
||||
params = append(params, "--lora", adapter)
|
||||
}
|
||||
// TODO: applying multiple adapters is not supported by the llama.cpp server yet
|
||||
params = append(params, "--lora", adapters[0])
|
||||
}
|
||||
|
||||
if len(projectors) > 0 {
|
||||
|
|
@ -303,9 +306,9 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
|
|||
|
||||
// Note: we always put the dependency path first
|
||||
// since this was the exact version we compiled/linked against
|
||||
if gpus[0].DependencyPath != nil {
|
||||
if gpus[0].DependencyPath != "" {
|
||||
// assume gpus from the same library have the same dependency path
|
||||
libraryPaths = append(gpus[0].DependencyPath, libraryPaths...)
|
||||
libraryPaths = append([]string{gpus[0].DependencyPath}, libraryPaths...)
|
||||
}
|
||||
|
||||
server := filepath.Join(dir, "ollama_llama_server")
|
||||
|
|
@ -684,11 +687,7 @@ type CompletionResponse struct {
|
|||
|
||||
func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {
|
||||
if err := s.sem.Acquire(ctx, 1); err != nil {
|
||||
if errors.Is(err, context.Canceled) {
|
||||
slog.Info("aborting completion request due to client closing the connection")
|
||||
} else {
|
||||
slog.Error("Failed to acquire semaphore", "error", err)
|
||||
}
|
||||
slog.Error("Failed to acquire semaphore", "error", err)
|
||||
return err
|
||||
}
|
||||
defer s.sem.Release(1)
|
||||
|
|
@ -839,15 +838,13 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
|
|||
}
|
||||
|
||||
if err := scanner.Err(); err != nil {
|
||||
if strings.Contains(err.Error(), "unexpected EOF") || strings.Contains(err.Error(), "forcibly closed") {
|
||||
if strings.Contains(err.Error(), "unexpected EOF") {
|
||||
s.Close()
|
||||
var msg string
|
||||
msg := ""
|
||||
if s.status != nil && s.status.LastErrMsg != "" {
|
||||
msg = s.status.LastErrMsg
|
||||
} else {
|
||||
msg = err.Error()
|
||||
}
|
||||
return fmt.Errorf("an error was encountered while running the model: %s", msg)
|
||||
return fmt.Errorf("an unknown error was encountered while running the model %s", msg)
|
||||
}
|
||||
|
||||
return fmt.Errorf("error reading llm response: %v", err)
|
||||
|
|
@ -866,11 +863,7 @@ type EmbeddingResponse struct {
|
|||
|
||||
func (s *llmServer) Embedding(ctx context.Context, input string) ([]float32, error) {
|
||||
if err := s.sem.Acquire(ctx, 1); err != nil {
|
||||
if errors.Is(err, context.Canceled) {
|
||||
slog.Info("aborting embedding request due to client closing the connection")
|
||||
} else {
|
||||
slog.Error("Failed to acquire semaphore", "error", err)
|
||||
}
|
||||
slog.Error("Failed to acquire semaphore", "error", err)
|
||||
return nil, err
|
||||
}
|
||||
defer s.sem.Release(1)
|
||||
|
|
@ -1099,9 +1092,7 @@ func (s *llmServer) EstimatedTotal() uint64 {
|
|||
func (s *llmServer) EstimatedVRAMByGPU(gpuID string) uint64 {
|
||||
for i, gpu := range s.gpus {
|
||||
if gpu.ID == gpuID {
|
||||
if i < len(s.estimate.GPUSizes) {
|
||||
return s.estimate.GPUSizes[i]
|
||||
}
|
||||
return s.estimate.GPUSizes[i]
|
||||
}
|
||||
}
|
||||
return 0
|
||||
|
|
|
|||
|
|
@ -27,7 +27,6 @@ var errorPrefixes = []string{
|
|||
"\"ERR\"",
|
||||
"error loading model",
|
||||
"GGML_ASSERT",
|
||||
"Deepseek2 does not support K-shift",
|
||||
}
|
||||
|
||||
func (w *StatusWriter) Write(b []byte) (int, error) {
|
||||
|
|
|
|||
|
|
@ -140,7 +140,6 @@ type CompletionChunk struct {
|
|||
|
||||
type ToolCall struct {
|
||||
ID string `json:"id"`
|
||||
Index int `json:"index"`
|
||||
Type string `json:"type"`
|
||||
Function struct {
|
||||
Name string `json:"name"`
|
||||
|
|
@ -201,13 +200,12 @@ func toolCallId() string {
|
|||
return "call_" + strings.ToLower(string(b))
|
||||
}
|
||||
|
||||
func toToolCalls(tc []api.ToolCall) []ToolCall {
|
||||
toolCalls := make([]ToolCall, len(tc))
|
||||
for i, tc := range tc {
|
||||
func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
|
||||
toolCalls := make([]ToolCall, len(r.Message.ToolCalls))
|
||||
for i, tc := range r.Message.ToolCalls {
|
||||
toolCalls[i].ID = toolCallId()
|
||||
toolCalls[i].Type = "function"
|
||||
toolCalls[i].Function.Name = tc.Function.Name
|
||||
toolCalls[i].Index = tc.Function.Index
|
||||
|
||||
args, err := json.Marshal(tc.Function.Arguments)
|
||||
if err != nil {
|
||||
|
|
@ -217,11 +215,7 @@ func toToolCalls(tc []api.ToolCall) []ToolCall {
|
|||
|
||||
toolCalls[i].Function.Arguments = string(args)
|
||||
}
|
||||
return toolCalls
|
||||
}
|
||||
|
||||
func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
|
||||
toolCalls := toToolCalls(r.Message.ToolCalls)
|
||||
return ChatCompletion{
|
||||
Id: id,
|
||||
Object: "chat.completion",
|
||||
|
|
@ -250,7 +244,6 @@ func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
|
|||
}
|
||||
|
||||
func toChunk(id string, r api.ChatResponse) ChatCompletionChunk {
|
||||
toolCalls := toToolCalls(r.Message.ToolCalls)
|
||||
return ChatCompletionChunk{
|
||||
Id: id,
|
||||
Object: "chat.completion.chunk",
|
||||
|
|
@ -259,7 +252,7 @@ func toChunk(id string, r api.ChatResponse) ChatCompletionChunk {
|
|||
SystemFingerprint: "fp_ollama",
|
||||
Choices: []ChunkChoice{{
|
||||
Index: 0,
|
||||
Delta: Message{Role: "assistant", Content: r.Message.Content, ToolCalls: toolCalls},
|
||||
Delta: Message{Role: "assistant", Content: r.Message.Content},
|
||||
FinishReason: func(reason string) *string {
|
||||
if len(reason) > 0 {
|
||||
return &reason
|
||||
|
|
@ -578,7 +571,7 @@ type EmbedWriter struct {
|
|||
model string
|
||||
}
|
||||
|
||||
func (w *BaseWriter) writeError(data []byte) (int, error) {
|
||||
func (w *BaseWriter) writeError(code int, data []byte) (int, error) {
|
||||
var serr api.StatusError
|
||||
err := json.Unmarshal(data, &serr)
|
||||
if err != nil {
|
||||
|
|
@ -637,7 +630,7 @@ func (w *ChatWriter) writeResponse(data []byte) (int, error) {
|
|||
func (w *ChatWriter) Write(data []byte) (int, error) {
|
||||
code := w.ResponseWriter.Status()
|
||||
if code != http.StatusOK {
|
||||
return w.writeError(data)
|
||||
return w.writeError(code, data)
|
||||
}
|
||||
|
||||
return w.writeResponse(data)
|
||||
|
|
@ -686,7 +679,7 @@ func (w *CompleteWriter) writeResponse(data []byte) (int, error) {
|
|||
func (w *CompleteWriter) Write(data []byte) (int, error) {
|
||||
code := w.ResponseWriter.Status()
|
||||
if code != http.StatusOK {
|
||||
return w.writeError(data)
|
||||
return w.writeError(code, data)
|
||||
}
|
||||
|
||||
return w.writeResponse(data)
|
||||
|
|
@ -711,7 +704,7 @@ func (w *ListWriter) writeResponse(data []byte) (int, error) {
|
|||
func (w *ListWriter) Write(data []byte) (int, error) {
|
||||
code := w.ResponseWriter.Status()
|
||||
if code != http.StatusOK {
|
||||
return w.writeError(data)
|
||||
return w.writeError(code, data)
|
||||
}
|
||||
|
||||
return w.writeResponse(data)
|
||||
|
|
@ -737,7 +730,7 @@ func (w *RetrieveWriter) writeResponse(data []byte) (int, error) {
|
|||
func (w *RetrieveWriter) Write(data []byte) (int, error) {
|
||||
code := w.ResponseWriter.Status()
|
||||
if code != http.StatusOK {
|
||||
return w.writeError(data)
|
||||
return w.writeError(code, data)
|
||||
}
|
||||
|
||||
return w.writeResponse(data)
|
||||
|
|
@ -762,7 +755,7 @@ func (w *EmbedWriter) writeResponse(data []byte) (int, error) {
|
|||
func (w *EmbedWriter) Write(data []byte) (int, error) {
|
||||
code := w.ResponseWriter.Status()
|
||||
if code != http.StatusOK {
|
||||
return w.writeError(data)
|
||||
return w.writeError(code, data)
|
||||
}
|
||||
|
||||
return w.writeResponse(data)
|
||||
|
|
|
|||
|
|
@ -195,86 +195,7 @@ func TestChatMiddleware(t *testing.T) {
|
|||
Stream: &False,
|
||||
},
|
||||
},
|
||||
{
|
||||
name: "chat handler with streaming tools",
|
||||
body: `{
|
||||
"model": "test-model",
|
||||
"messages": [
|
||||
{"role": "user", "content": "What's the weather like in Paris?"}
|
||||
],
|
||||
"stream": true,
|
||||
"tools": [{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"required": ["location"],
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state"
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}]
|
||||
}`,
|
||||
req: api.ChatRequest{
|
||||
Model: "test-model",
|
||||
Messages: []api.Message{
|
||||
{
|
||||
Role: "user",
|
||||
Content: "What's the weather like in Paris?",
|
||||
},
|
||||
},
|
||||
Tools: []api.Tool{
|
||||
{
|
||||
Type: "function",
|
||||
Function: api.ToolFunction{
|
||||
Name: "get_weather",
|
||||
Description: "Get the current weather",
|
||||
Parameters: struct {
|
||||
Type string `json:"type"`
|
||||
Required []string `json:"required"`
|
||||
Properties map[string]struct {
|
||||
Type string `json:"type"`
|
||||
Description string `json:"description"`
|
||||
Enum []string `json:"enum,omitempty"`
|
||||
} `json:"properties"`
|
||||
}{
|
||||
Type: "object",
|
||||
Required: []string{"location"},
|
||||
Properties: map[string]struct {
|
||||
Type string `json:"type"`
|
||||
Description string `json:"description"`
|
||||
Enum []string `json:"enum,omitempty"`
|
||||
}{
|
||||
"location": {
|
||||
Type: "string",
|
||||
Description: "The city and state",
|
||||
},
|
||||
"unit": {
|
||||
Type: "string",
|
||||
Enum: []string{"celsius", "fahrenheit"},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
Options: map[string]any{
|
||||
"temperature": 1.0,
|
||||
"top_p": 1.0,
|
||||
},
|
||||
Stream: &True,
|
||||
},
|
||||
},
|
||||
|
||||
{
|
||||
name: "chat handler error forwarding",
|
||||
body: `{
|
||||
|
|
|
|||
|
|
@ -65,22 +65,9 @@ var (
|
|||
errInvalidCommand = errors.New("command must be one of \"from\", \"license\", \"template\", \"system\", \"adapter\", \"parameter\", or \"message\"")
|
||||
)
|
||||
|
||||
type ParserError struct {
|
||||
LineNumber int
|
||||
Msg string
|
||||
}
|
||||
|
||||
func (e *ParserError) Error() string {
|
||||
if e.LineNumber > 0 {
|
||||
return fmt.Sprintf("(line %d): %s", e.LineNumber, e.Msg)
|
||||
}
|
||||
return e.Msg
|
||||
}
|
||||
|
||||
func ParseFile(r io.Reader) (*File, error) {
|
||||
var cmd Command
|
||||
var curr state
|
||||
var currLine int = 1
|
||||
var b bytes.Buffer
|
||||
var role string
|
||||
|
||||
|
|
@ -97,18 +84,11 @@ func ParseFile(r io.Reader) (*File, error) {
|
|||
return nil, err
|
||||
}
|
||||
|
||||
if isNewline(r) {
|
||||
currLine++
|
||||
}
|
||||
|
||||
next, r, err := parseRuneForState(r, curr)
|
||||
if errors.Is(err, io.ErrUnexpectedEOF) {
|
||||
return nil, fmt.Errorf("%w: %s", err, b.String())
|
||||
} else if err != nil {
|
||||
return nil, &ParserError{
|
||||
LineNumber: currLine,
|
||||
Msg: err.Error(),
|
||||
}
|
||||
return nil, err
|
||||
}
|
||||
|
||||
// process the state transition, some transitions need to be intercepted and redirected
|
||||
|
|
@ -116,10 +96,7 @@ func ParseFile(r io.Reader) (*File, error) {
|
|||
switch curr {
|
||||
case stateName:
|
||||
if !isValidCommand(b.String()) {
|
||||
return nil, &ParserError{
|
||||
LineNumber: currLine,
|
||||
Msg: errInvalidCommand.Error(),
|
||||
}
|
||||
return nil, errInvalidCommand
|
||||
}
|
||||
|
||||
// next state sometimes depends on the current buffer value
|
||||
|
|
@ -140,10 +117,7 @@ func ParseFile(r io.Reader) (*File, error) {
|
|||
cmd.Name = b.String()
|
||||
case stateMessage:
|
||||
if !isValidMessageRole(b.String()) {
|
||||
return nil, &ParserError{
|
||||
LineNumber: currLine,
|
||||
Msg: errInvalidMessageRole.Error(),
|
||||
}
|
||||
return nil, errInvalidMessageRole
|
||||
}
|
||||
|
||||
role = b.String()
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ package parser
|
|||
import (
|
||||
"bytes"
|
||||
"encoding/binary"
|
||||
"errors"
|
||||
"fmt"
|
||||
"io"
|
||||
"strings"
|
||||
|
|
@ -181,15 +180,8 @@ func TestParseFileBadCommand(t *testing.T) {
|
|||
FROM foo
|
||||
BADCOMMAND param1 value1
|
||||
`
|
||||
parserError := &ParserError{
|
||||
LineNumber: 3,
|
||||
Msg: errInvalidCommand.Error(),
|
||||
}
|
||||
|
||||
_, err := ParseFile(strings.NewReader(input))
|
||||
if !errors.As(err, &parserError) {
|
||||
t.Errorf("unexpected error: expected: %s, actual: %s", parserError.Error(), err.Error())
|
||||
}
|
||||
require.ErrorIs(t, err, errInvalidCommand)
|
||||
}
|
||||
|
||||
func TestParseFileMessages(t *testing.T) {
|
||||
|
|
@ -253,10 +245,7 @@ FROM foo
|
|||
MESSAGE badguy I'm a bad guy!
|
||||
`,
|
||||
nil,
|
||||
&ParserError{
|
||||
LineNumber: 3,
|
||||
Msg: errInvalidMessageRole.Error(),
|
||||
},
|
||||
errInvalidMessageRole,
|
||||
},
|
||||
{
|
||||
`
|
||||
|
|
@ -275,35 +264,13 @@ MESSAGE system`,
|
|||
},
|
||||
}
|
||||
|
||||
for _, tt := range cases {
|
||||
for _, c := range cases {
|
||||
t.Run("", func(t *testing.T) {
|
||||
modelfile, err := ParseFile(strings.NewReader(tt.input))
|
||||
|
||||
modelfile, err := ParseFile(strings.NewReader(c.input))
|
||||
require.ErrorIs(t, err, c.err)
|
||||
if modelfile != nil {
|
||||
assert.Equal(t, tt.expected, modelfile.Commands)
|
||||
assert.Equal(t, c.expected, modelfile.Commands)
|
||||
}
|
||||
|
||||
if tt.err == nil {
|
||||
if err != nil {
|
||||
t.Fatalf("expected no error, but got %v", err)
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
switch tt.err.(type) {
|
||||
case *ParserError:
|
||||
var pErr *ParserError
|
||||
if errors.As(err, &pErr) {
|
||||
// got the correct type of error
|
||||
return
|
||||
}
|
||||
}
|
||||
|
||||
if errors.Is(err, tt.err) {
|
||||
return
|
||||
}
|
||||
|
||||
t.Fatalf("unexpected error: expected: %v, actual: %v", tt.err, err)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ export GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=$V
|
|||
# TODO - consider `docker buildx ls --format=json` to autodiscover platform capability
|
||||
PLATFORM=${PLATFORM:-"linux/arm64,linux/amd64"}
|
||||
DOCKER_ORG=${DOCKER_ORG:-"ollama"}
|
||||
RELEASE_IMAGE_REPO=${RELEASE_IMAGE_REPO:-"${DOCKER_ORG}/release"}
|
||||
FINAL_IMAGE_REPO=${FINAL_IMAGE_REPO:-"${DOCKER_ORG}/ollama"}
|
||||
OLLAMA_COMMON_BUILD_ARGS="--build-arg=VERSION \
|
||||
--build-arg=GOFLAGS \
|
||||
|
|
|
|||
|
|
@ -4,12 +4,9 @@
|
|||
|
||||
set -eu
|
||||
|
||||
red="$( (/usr/bin/tput bold || :; /usr/bin/tput setaf 1 || :) 2>&-)"
|
||||
plain="$( (/usr/bin/tput sgr0 || :) 2>&-)"
|
||||
|
||||
status() { echo ">>> $*" >&2; }
|
||||
error() { echo "${red}ERROR:${plain} $*"; exit 1; }
|
||||
warning() { echo "${red}WARNING:${plain} $*"; }
|
||||
error() { echo "ERROR $*"; exit 1; }
|
||||
warning() { echo "WARNING: $*"; }
|
||||
|
||||
TEMP_DIR=$(mktemp -d)
|
||||
cleanup() { rm -rf $TEMP_DIR; }
|
||||
|
|
@ -96,22 +93,6 @@ else
|
|||
fi
|
||||
fi
|
||||
|
||||
# Check for NVIDIA JetPack systems with additional downloads
|
||||
if [ -f /etc/nv_tegra_release ] ; then
|
||||
if grep R36 /etc/nv_tegra_release > /dev/null ; then
|
||||
status "Downloading JetPack 6 components"
|
||||
curl --fail --show-error --location --progress-bar \
|
||||
"https://ollama.com/download/ollama-linux-${ARCH}-jetpack6.tgz${VER_PARAM}" | \
|
||||
$SUDO tar -xzf - -C "$OLLAMA_INSTALL_DIR"
|
||||
elif grep R35 /etc/nv_tegra_release > /dev/null ; then
|
||||
status "Downloading JetPack 5 components"
|
||||
curl --fail --show-error --location --progress-bar \
|
||||
"https://ollama.com/download/ollama-linux-${ARCH}-jetpack5.tgz${VER_PARAM}" | \
|
||||
$SUDO tar -xzf - -C "$OLLAMA_INSTALL_DIR"
|
||||
else
|
||||
warning "Unsupported JetPack version detected. GPU may not be supported"
|
||||
fi
|
||||
fi
|
||||
|
||||
install_success() {
|
||||
status 'The Ollama API is now available at 127.0.0.1:11434.'
|
||||
|
|
@ -165,12 +146,6 @@ EOF
|
|||
start_service() { $SUDO systemctl restart ollama; }
|
||||
trap start_service EXIT
|
||||
;;
|
||||
*)
|
||||
warning "systemd is not running"
|
||||
if [ "$IS_WSL2" = true ]; then
|
||||
warning "see https://learn.microsoft.com/en-us/windows/wsl/systemd#how-to-enable-systemd to enable it"
|
||||
fi
|
||||
;;
|
||||
esac
|
||||
}
|
||||
|
||||
|
|
@ -188,13 +163,6 @@ if [ "$IS_WSL2" = true ]; then
|
|||
exit 0
|
||||
fi
|
||||
|
||||
# Don't attempt to install drivers on Jetson systems
|
||||
if [ -f /etc/nv_tegra_release ] ; then
|
||||
status "NVIDIA JetPack ready."
|
||||
install_success
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Install GPU dependencies on Linux
|
||||
if ! available lspci && ! available lshw; then
|
||||
warning "Unable to detect NVIDIA/AMD GPU. Install lspci or lshw to automatically detect and install GPU dependencies."
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import (
|
|||
"cmp"
|
||||
"context"
|
||||
"crypto/sha256"
|
||||
"encoding/base64"
|
||||
"encoding/hex"
|
||||
"encoding/json"
|
||||
"errors"
|
||||
|
|
@ -12,7 +13,6 @@ import (
|
|||
"io"
|
||||
"log"
|
||||
"log/slog"
|
||||
"net"
|
||||
"net/http"
|
||||
"net/url"
|
||||
"os"
|
||||
|
|
@ -23,12 +23,14 @@ import (
|
|||
"strings"
|
||||
|
||||
"github.com/ollama/ollama/api"
|
||||
"github.com/ollama/ollama/auth"
|
||||
"github.com/ollama/ollama/envconfig"
|
||||
"github.com/ollama/ollama/format"
|
||||
"github.com/ollama/ollama/llama"
|
||||
"github.com/ollama/ollama/llm"
|
||||
"github.com/ollama/ollama/parser"
|
||||
"github.com/ollama/ollama/template"
|
||||
"github.com/ollama/ollama/types/errtypes"
|
||||
"github.com/ollama/ollama/types/model"
|
||||
"github.com/ollama/ollama/version"
|
||||
)
|
||||
|
|
@ -982,7 +984,37 @@ func GetSHA256Digest(r io.Reader) (string, int64) {
|
|||
|
||||
var errUnauthorized = errors.New("unauthorized: access denied")
|
||||
|
||||
// getTokenSubject returns the subject of a JWT token, it does not validate the token
|
||||
func getTokenSubject(token string) string {
|
||||
parts := strings.Split(token, ".")
|
||||
if len(parts) != 3 {
|
||||
return ""
|
||||
}
|
||||
|
||||
payload := parts[1]
|
||||
payloadBytes, err := base64.RawURLEncoding.DecodeString(payload)
|
||||
if err != nil {
|
||||
slog.Error(fmt.Sprintf("failed to decode jwt payload: %v", err))
|
||||
return ""
|
||||
}
|
||||
|
||||
var payloadMap map[string]interface{}
|
||||
if err := json.Unmarshal(payloadBytes, &payloadMap); err != nil {
|
||||
slog.Error(fmt.Sprintf("failed to unmarshal payload JSON: %v", err))
|
||||
return ""
|
||||
}
|
||||
|
||||
sub, ok := payloadMap["sub"]
|
||||
if !ok {
|
||||
slog.Error("jwt does not contain 'sub' field")
|
||||
return ""
|
||||
}
|
||||
|
||||
return fmt.Sprintf("%s", sub)
|
||||
}
|
||||
|
||||
func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.URL, headers http.Header, body io.ReadSeeker, regOpts *registryOptions) (*http.Response, error) {
|
||||
anonymous := true // access will default to anonymous if no user is found associated with the public key
|
||||
for range 2 {
|
||||
resp, err := makeRequest(ctx, method, requestURL, headers, body, regOpts)
|
||||
if err != nil {
|
||||
|
|
@ -1003,6 +1035,7 @@ func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.UR
|
|||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
anonymous = getTokenSubject(token) == "anonymous"
|
||||
regOpts.Token = token
|
||||
if body != nil {
|
||||
_, err = body.Seek(0, io.SeekStart)
|
||||
|
|
@ -1025,24 +1058,19 @@ func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.UR
|
|||
}
|
||||
}
|
||||
|
||||
if anonymous {
|
||||
// no user is associated with the public key, and the request requires non-anonymous access
|
||||
pubKey, nestedErr := auth.GetPublicKey()
|
||||
if nestedErr != nil {
|
||||
slog.Error(fmt.Sprintf("couldn't get public key: %v", nestedErr))
|
||||
return nil, errUnauthorized
|
||||
}
|
||||
return nil, &errtypes.UnknownOllamaKey{Key: pubKey}
|
||||
}
|
||||
// user is associated with the public key, but is not authorized to make the request
|
||||
return nil, errUnauthorized
|
||||
}
|
||||
|
||||
// testMakeRequestDialContext specifies the dial function for the http client in
|
||||
// makeRequest. It can be used to resolve hosts in model names to local
|
||||
// addresses for testing. For example, the model name ("example.com/my/model")
|
||||
// can be directed to push/pull from "127.0.0.1:1234".
|
||||
//
|
||||
// This is not safe to set across goroutines. It should be set in
|
||||
// the main test goroutine, and not by tests marked to run in parallel with
|
||||
// t.Parallel().
|
||||
//
|
||||
// It should be cleared after use, otherwise it will affect other tests.
|
||||
//
|
||||
// Ideally we would have some set this up the stack, but the code is not
|
||||
// structured in a way that makes this easy, so this will have to do for now.
|
||||
var testMakeRequestDialContext func(ctx context.Context, network, addr string) (net.Conn, error)
|
||||
|
||||
func makeRequest(ctx context.Context, method string, requestURL *url.URL, headers http.Header, body io.Reader, regOpts *registryOptions) (*http.Response, error) {
|
||||
if requestURL.Scheme != "http" && regOpts != nil && regOpts.Insecure {
|
||||
requestURL.Scheme = "http"
|
||||
|
|
@ -1076,15 +1104,14 @@ func makeRequest(ctx context.Context, method string, requestURL *url.URL, header
|
|||
req.ContentLength = contentLength
|
||||
}
|
||||
|
||||
c := &http.Client{
|
||||
resp, err := (&http.Client{
|
||||
CheckRedirect: regOpts.CheckRedirect,
|
||||
}).Do(req)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if testMakeRequestDialContext != nil {
|
||||
tr := http.DefaultTransport.(*http.Transport).Clone()
|
||||
tr.DialContext = testMakeRequestDialContext
|
||||
c.Transport = tr
|
||||
}
|
||||
return c.Do(req)
|
||||
|
||||
return resp, nil
|
||||
}
|
||||
|
||||
func getValue(header, key string) string {
|
||||
|
|
|
|||
|
|
@ -39,7 +39,6 @@ func TestExecuteWithTools(t *testing.T) {
|
|||
{"mistral", `[TOOL_CALLS] [{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]
|
||||
|
||||
The temperature in San Francisco, CA is 70°F and in Toronto, Canada is 20°C.`, true},
|
||||
{"mistral", `[TOOL_CALLS] [{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"To }]`, false},
|
||||
{"mistral", `I'm not aware of that information. However, I can suggest searching for the weather using the "get_current_weather" function:
|
||||
|
||||
[{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]`, true},
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ func TestChatPrompt(t *testing.T) {
|
|||
mllamaModel := Model{Template: tmpl, ProjectorPaths: []string{"vision"}, Config: ConfigV2{ModelFamilies: []string{"mllama"}}}
|
||||
|
||||
createImg := func(width, height int) ([]byte, error) {
|
||||
img := image.NewRGBA(image.Rect(0, 0, width, height))
|
||||
img := image.NewRGBA(image.Rect(0, 0, 5, 5))
|
||||
var buf bytes.Buffer
|
||||
|
||||
if err := png.Encode(&buf, img); err != nil {
|
||||
|
|
|
|||
|
|
@ -507,7 +507,7 @@ func (s *Server) EmbeddingsHandler(c *gin.Context) {
|
|||
embedding, err := r.Embedding(c.Request.Context(), req.Prompt)
|
||||
if err != nil {
|
||||
slog.Info(fmt.Sprintf("embedding generation failed: %v", err))
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": fmt.Errorf("failed to generate embedding: %v", err)})
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": "failed to generate embedding"})
|
||||
return
|
||||
}
|
||||
|
||||
|
|
@ -540,8 +540,7 @@ func (s *Server) PullHandler(c *gin.Context) {
|
|||
return
|
||||
}
|
||||
|
||||
name, err = getExistingName(name)
|
||||
if err != nil {
|
||||
if err := checkNameExists(name); err != nil {
|
||||
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
|
|
@ -622,20 +621,19 @@ func (s *Server) PushHandler(c *gin.Context) {
|
|||
streamResponse(c, ch)
|
||||
}
|
||||
|
||||
// getExistingName returns the original, on disk name if the input name is a
|
||||
// case-insensitive match, otherwise it returns the input name.
|
||||
func getExistingName(n model.Name) (model.Name, error) {
|
||||
var zero model.Name
|
||||
existing, err := Manifests(true)
|
||||
func checkNameExists(name model.Name) error {
|
||||
names, err := Manifests(true)
|
||||
if err != nil {
|
||||
return zero, err
|
||||
return err
|
||||
}
|
||||
for e := range existing {
|
||||
if n.EqualFold(e) {
|
||||
return e, nil
|
||||
|
||||
for n := range names {
|
||||
if strings.EqualFold(n.Filepath(), name.Filepath()) && n != name {
|
||||
return errors.New("a model with that name already exists")
|
||||
}
|
||||
}
|
||||
return n, nil
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (s *Server) CreateHandler(c *gin.Context) {
|
||||
|
|
@ -654,8 +652,7 @@ func (s *Server) CreateHandler(c *gin.Context) {
|
|||
return
|
||||
}
|
||||
|
||||
name, err := getExistingName(name)
|
||||
if err != nil {
|
||||
if err := checkNameExists(name); err != nil {
|
||||
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
|
|
@ -961,19 +958,14 @@ func (s *Server) CopyHandler(c *gin.Context) {
|
|||
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("source %q is invalid", r.Source)})
|
||||
return
|
||||
}
|
||||
src, err := getExistingName(src)
|
||||
if err != nil {
|
||||
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
|
||||
dst := model.ParseName(r.Destination)
|
||||
if !dst.IsValid() {
|
||||
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("destination %q is invalid", r.Destination)})
|
||||
return
|
||||
}
|
||||
dst, err = getExistingName(dst)
|
||||
if err != nil {
|
||||
|
||||
if err := checkNameExists(dst); err != nil {
|
||||
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
|
|
@ -1141,7 +1133,7 @@ func (s *Server) GenerateRoutes() http.Handler {
|
|||
config.AllowWildcard = true
|
||||
config.AllowBrowserExtensions = true
|
||||
config.AllowHeaders = []string{"Authorization", "Content-Type", "User-Agent", "Accept", "X-Requested-With"}
|
||||
openAIProperties := []string{"lang", "package-version", "os", "arch", "retry-count", "runtime", "runtime-version", "async"}
|
||||
openAIProperties := []string{"lang", "package-version", "os", "arch", "runtime", "runtime-version", "async"}
|
||||
for _, prop := range openAIProperties {
|
||||
config.AllowHeaders = append(config.AllowHeaders, "x-stainless-"+prop)
|
||||
}
|
||||
|
|
@ -1458,7 +1450,6 @@ func (s *Server) ChatHandler(c *gin.Context) {
|
|||
|
||||
prompt, images, err := chatPrompt(c.Request.Context(), m, r.Tokenize, opts, msgs, req.Tools)
|
||||
if err != nil {
|
||||
slog.Error("chat prompt error", "error", err)
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
|
|
@ -1468,8 +1459,6 @@ func (s *Server) ChatHandler(c *gin.Context) {
|
|||
ch := make(chan any)
|
||||
go func() {
|
||||
defer close(ch)
|
||||
var sb strings.Builder
|
||||
var toolCallIndex int = 0
|
||||
if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
|
||||
Prompt: prompt,
|
||||
Images: images,
|
||||
|
|
@ -1495,37 +1484,7 @@ func (s *Server) ChatHandler(c *gin.Context) {
|
|||
res.LoadDuration = checkpointLoaded.Sub(checkpointStart)
|
||||
}
|
||||
|
||||
// TODO: tool call checking and filtering should be moved outside of this callback once streaming
|
||||
// however this was a simple change for now without reworking streaming logic of this (and other)
|
||||
// handlers
|
||||
if req.Stream != nil && !*req.Stream || len(req.Tools) == 0 {
|
||||
ch <- res
|
||||
return
|
||||
}
|
||||
|
||||
// Streaming tool calls:
|
||||
// If tools are recognized, use a flag to track the sending of a tool downstream
|
||||
// This ensures that content is cleared from the message on the last chunk sent
|
||||
sb.WriteString(r.Content)
|
||||
if toolCalls, ok := m.parseToolCalls(sb.String()); ok {
|
||||
res.Message.ToolCalls = toolCalls
|
||||
for i := range toolCalls {
|
||||
toolCalls[i].Function.Index = toolCallIndex
|
||||
toolCallIndex++
|
||||
}
|
||||
res.Message.Content = ""
|
||||
sb.Reset()
|
||||
ch <- res
|
||||
return
|
||||
}
|
||||
|
||||
if r.Done {
|
||||
// Send any remaining content if no tool calls were detected
|
||||
if toolCallIndex == 0 {
|
||||
res.Message.Content = sb.String()
|
||||
}
|
||||
ch <- res
|
||||
}
|
||||
ch <- res
|
||||
}); err != nil {
|
||||
ch <- gin.H{"error": err.Error()}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ import (
|
|||
"io"
|
||||
"net/http"
|
||||
"strings"
|
||||
"sync"
|
||||
"testing"
|
||||
"time"
|
||||
|
||||
|
|
@ -26,14 +25,10 @@ type mockRunner struct {
|
|||
// CompletionRequest is only valid until the next call to Completion
|
||||
llm.CompletionRequest
|
||||
llm.CompletionResponse
|
||||
CompletionFn func(context.Context, llm.CompletionRequest, func(llm.CompletionResponse)) error
|
||||
}
|
||||
|
||||
func (m *mockRunner) Completion(ctx context.Context, r llm.CompletionRequest, fn func(r llm.CompletionResponse)) error {
|
||||
func (m *mockRunner) Completion(_ context.Context, r llm.CompletionRequest, fn func(r llm.CompletionResponse)) error {
|
||||
m.CompletionRequest = r
|
||||
if m.CompletionFn != nil {
|
||||
return m.CompletionFn(ctx, r, fn)
|
||||
}
|
||||
fn(m.CompletionResponse)
|
||||
return nil
|
||||
}
|
||||
|
|
@ -93,14 +88,9 @@ func TestGenerateChat(t *testing.T) {
|
|||
Model: "test",
|
||||
Modelfile: fmt.Sprintf(`FROM %s
|
||||
TEMPLATE """
|
||||
{{- if .Tools }}
|
||||
{{ .Tools }}
|
||||
{{ end }}
|
||||
{{- range .Messages }}
|
||||
{{- .Role }}: {{ .Content }}
|
||||
{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
|
||||
{{- end }}
|
||||
{{ end }}"""
|
||||
{{- if .System }}System: {{ .System }} {{ end }}
|
||||
{{- if .Prompt }}User: {{ .Prompt }} {{ end }}
|
||||
{{- if .Response }}Assistant: {{ .Response }} {{ end }}"""
|
||||
`, createBinFile(t, llm.KV{
|
||||
"general.architecture": "llama",
|
||||
"llama.block_count": uint32(1),
|
||||
|
|
@ -273,7 +263,7 @@ func TestGenerateChat(t *testing.T) {
|
|||
t.Errorf("expected status 200, got %d", w.Code)
|
||||
}
|
||||
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "user: Hello!\n"); diff != "" {
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "User: Hello! "); diff != "" {
|
||||
t.Errorf("mismatch (-got +want):\n%s", diff)
|
||||
}
|
||||
|
||||
|
|
@ -302,7 +292,7 @@ func TestGenerateChat(t *testing.T) {
|
|||
t.Errorf("expected status 200, got %d", w.Code)
|
||||
}
|
||||
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "system: You are a helpful assistant.\nuser: Hello!\n"); diff != "" {
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You are a helpful assistant. User: Hello! "); diff != "" {
|
||||
t.Errorf("mismatch (-got +want):\n%s", diff)
|
||||
}
|
||||
|
||||
|
|
@ -324,7 +314,7 @@ func TestGenerateChat(t *testing.T) {
|
|||
t.Errorf("expected status 200, got %d", w.Code)
|
||||
}
|
||||
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "system: You can perform magic tricks.\nuser: Hello!\n"); diff != "" {
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You can perform magic tricks. User: Hello! "); diff != "" {
|
||||
t.Errorf("mismatch (-got +want):\n%s", diff)
|
||||
}
|
||||
|
||||
|
|
@ -347,242 +337,12 @@ func TestGenerateChat(t *testing.T) {
|
|||
t.Errorf("expected status 200, got %d", w.Code)
|
||||
}
|
||||
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "system: You are a helpful assistant.\nuser: Hello!\nassistant: I can help you with that.\nsystem: You can perform magic tricks.\nuser: Help me write tests.\n"); diff != "" {
|
||||
if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You are a helpful assistant. User: Hello! Assistant: I can help you with that. System: You can perform magic tricks. User: Help me write tests. "); diff != "" {
|
||||
t.Errorf("mismatch (-got +want):\n%s", diff)
|
||||
}
|
||||
|
||||
checkChatResponse(t, w.Body, "test-system", "Abra kadabra!")
|
||||
})
|
||||
|
||||
t.Run("messages with tools (non-streaming)", func(t *testing.T) {
|
||||
if w.Code != http.StatusOK {
|
||||
t.Fatalf("failed to create test-system model: %d", w.Code)
|
||||
}
|
||||
|
||||
tools := []api.Tool{
|
||||
{
|
||||
Type: "function",
|
||||
Function: api.ToolFunction{
|
||||
Name: "get_weather",
|
||||
Description: "Get the current weather",
|
||||
Parameters: struct {
|
||||
Type string `json:"type"`
|
||||
Required []string `json:"required"`
|
||||
Properties map[string]struct {
|
||||
Type string `json:"type"`
|
||||
Description string `json:"description"`
|
||||
Enum []string `json:"enum,omitempty"`
|
||||
} `json:"properties"`
|
||||
}{
|
||||
Type: "object",
|
||||
Required: []string{"location"},
|
||||
Properties: map[string]struct {
|
||||
Type string `json:"type"`
|
||||
Description string `json:"description"`
|
||||
Enum []string `json:"enum,omitempty"`
|
||||
}{
|
||||
"location": {
|
||||
Type: "string",
|
||||
Description: "The city and state",
|
||||
},
|
||||
"unit": {
|
||||
Type: "string",
|
||||
Enum: []string{"celsius", "fahrenheit"},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
mock.CompletionResponse = llm.CompletionResponse{
|
||||
Content: `{"name":"get_weather","arguments":{"location":"Seattle, WA","unit":"celsius"}}`,
|
||||
Done: true,
|
||||
DoneReason: "done",
|
||||
PromptEvalCount: 1,
|
||||
PromptEvalDuration: 1,
|
||||
EvalCount: 1,
|
||||
EvalDuration: 1,
|
||||
}
|
||||
|
||||
streamRequest := true
|
||||
|
||||
w := createRequest(t, s.ChatHandler, api.ChatRequest{
|
||||
Model: "test-system",
|
||||
Messages: []api.Message{
|
||||
{Role: "user", Content: "What's the weather in Seattle?"},
|
||||
},
|
||||
Tools: tools,
|
||||
Stream: &streamRequest,
|
||||
})
|
||||
|
||||
if w.Code != http.StatusOK {
|
||||
var errResp struct {
|
||||
Error string `json:"error"`
|
||||
}
|
||||
if err := json.NewDecoder(w.Body).Decode(&errResp); err != nil {
|
||||
t.Logf("Failed to decode error response: %v", err)
|
||||
} else {
|
||||
t.Logf("Error response: %s", errResp.Error)
|
||||
}
|
||||
}
|
||||
|
||||
if w.Code != http.StatusOK {
|
||||
t.Errorf("expected status 200, got %d", w.Code)
|
||||
}
|
||||
|
||||
var resp api.ChatResponse
|
||||
if err := json.NewDecoder(w.Body).Decode(&resp); err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
if resp.Message.ToolCalls == nil {
|
||||
t.Error("expected tool calls, got nil")
|
||||
}
|
||||
|
||||
expectedToolCall := api.ToolCall{
|
||||
Function: api.ToolCallFunction{
|
||||
Name: "get_weather",
|
||||
Arguments: api.ToolCallFunctionArguments{
|
||||
"location": "Seattle, WA",
|
||||
"unit": "celsius",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
if diff := cmp.Diff(resp.Message.ToolCalls[0], expectedToolCall); diff != "" {
|
||||
t.Errorf("tool call mismatch (-got +want):\n%s", diff)
|
||||
}
|
||||
})
|
||||
|
||||
t.Run("messages with tools (streaming)", func(t *testing.T) {
|
||||
tools := []api.Tool{
|
||||
{
|
||||
Type: "function",
|
||||
Function: api.ToolFunction{
|
||||
Name: "get_weather",
|
||||
Description: "Get the current weather",
|
||||
Parameters: struct {
|
||||
Type string `json:"type"`
|
||||
Required []string `json:"required"`
|
||||
Properties map[string]struct {
|
||||
Type string `json:"type"`
|
||||
Description string `json:"description"`
|
||||
Enum []string `json:"enum,omitempty"`
|
||||
} `json:"properties"`
|
||||
}{
|
||||
Type: "object",
|
||||
Required: []string{"location"},
|
||||
Properties: map[string]struct {
|
||||
Type string `json:"type"`
|
||||
Description string `json:"description"`
|
||||
Enum []string `json:"enum,omitempty"`
|
||||
}{
|
||||
"location": {
|
||||
Type: "string",
|
||||
Description: "The city and state",
|
||||
},
|
||||
"unit": {
|
||||
Type: "string",
|
||||
Enum: []string{"celsius", "fahrenheit"},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
// Simulate streaming response with multiple chunks
|
||||
var wg sync.WaitGroup
|
||||
wg.Add(1)
|
||||
|
||||
mock.CompletionFn = func(ctx context.Context, r llm.CompletionRequest, fn func(r llm.CompletionResponse)) error {
|
||||
defer wg.Done()
|
||||
|
||||
// Send chunks with small delays to simulate streaming
|
||||
responses := []llm.CompletionResponse{
|
||||
{
|
||||
Content: `{"name":"get_`,
|
||||
Done: false,
|
||||
PromptEvalCount: 1,
|
||||
PromptEvalDuration: 1,
|
||||
},
|
||||
{
|
||||
Content: `weather","arguments":{"location":"Seattle`,
|
||||
Done: false,
|
||||
PromptEvalCount: 2,
|
||||
PromptEvalDuration: 1,
|
||||
},
|
||||
{
|
||||
Content: `, WA","unit":"celsius"}}`,
|
||||
Done: true,
|
||||
DoneReason: "tool_call",
|
||||
PromptEvalCount: 3,
|
||||
PromptEvalDuration: 1,
|
||||
},
|
||||
}
|
||||
|
||||
for _, resp := range responses {
|
||||
select {
|
||||
case <-ctx.Done():
|
||||
return ctx.Err()
|
||||
default:
|
||||
fn(resp)
|
||||
time.Sleep(10 * time.Millisecond) // Small delay between chunks
|
||||
}
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
w := createRequest(t, s.ChatHandler, api.ChatRequest{
|
||||
Model: "test-system",
|
||||
Messages: []api.Message{
|
||||
{Role: "user", Content: "What's the weather in Seattle?"},
|
||||
},
|
||||
Tools: tools,
|
||||
Stream: &stream,
|
||||
})
|
||||
|
||||
wg.Wait()
|
||||
|
||||
if w.Code != http.StatusOK {
|
||||
t.Errorf("expected status 200, got %d", w.Code)
|
||||
}
|
||||
|
||||
// Read and validate the streamed responses
|
||||
decoder := json.NewDecoder(w.Body)
|
||||
var finalToolCall api.ToolCall
|
||||
|
||||
for {
|
||||
var resp api.ChatResponse
|
||||
if err := decoder.Decode(&resp); err == io.EOF {
|
||||
break
|
||||
} else if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
if resp.Done {
|
||||
if len(resp.Message.ToolCalls) != 1 {
|
||||
t.Errorf("expected 1 tool call in final response, got %d", len(resp.Message.ToolCalls))
|
||||
}
|
||||
finalToolCall = resp.Message.ToolCalls[0]
|
||||
}
|
||||
}
|
||||
|
||||
expectedToolCall := api.ToolCall{
|
||||
Function: api.ToolCallFunction{
|
||||
Name: "get_weather",
|
||||
Arguments: api.ToolCallFunctionArguments{
|
||||
"location": "Seattle, WA",
|
||||
"unit": "celsius",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
if diff := cmp.Diff(finalToolCall, expectedToolCall); diff != "" {
|
||||
t.Errorf("final tool call mismatch (-got +want):\n%s", diff)
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
func TestGenerate(t *testing.T) {
|
||||
|
|
|
|||
|
|
@ -7,18 +7,13 @@ import (
|
|||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"io/fs"
|
||||
"math"
|
||||
"math/rand/v2"
|
||||
"net"
|
||||
"net/http"
|
||||
"net/http/httptest"
|
||||
"os"
|
||||
"path/filepath"
|
||||
"sort"
|
||||
"strings"
|
||||
"testing"
|
||||
"unicode"
|
||||
|
||||
"github.com/ollama/ollama/api"
|
||||
"github.com/ollama/ollama/llm"
|
||||
|
|
@ -478,129 +473,83 @@ func Test_Routes(t *testing.T) {
|
|||
}
|
||||
}
|
||||
|
||||
func casingShuffle(s string) string {
|
||||
rr := []rune(s)
|
||||
for i := range rr {
|
||||
if rand.N(2) == 0 {
|
||||
rr[i] = unicode.ToUpper(rr[i])
|
||||
} else {
|
||||
rr[i] = unicode.ToLower(rr[i])
|
||||
}
|
||||
}
|
||||
return string(rr)
|
||||
}
|
||||
|
||||
func TestManifestCaseSensitivity(t *testing.T) {
|
||||
func TestCase(t *testing.T) {
|
||||
t.Setenv("OLLAMA_MODELS", t.TempDir())
|
||||
|
||||
r := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
|
||||
w.WriteHeader(http.StatusOK)
|
||||
io.WriteString(w, `{}`) //nolint:errcheck
|
||||
}))
|
||||
defer r.Close()
|
||||
|
||||
nameUsed := make(map[string]bool)
|
||||
name := func() string {
|
||||
const fqmn = "example/namespace/model:tag"
|
||||
for {
|
||||
v := casingShuffle(fqmn)
|
||||
if nameUsed[v] {
|
||||
continue
|
||||
}
|
||||
nameUsed[v] = true
|
||||
return v
|
||||
}
|
||||
}
|
||||
|
||||
wantStableName := name()
|
||||
|
||||
// checkManifestList tests that there is strictly one manifest in the
|
||||
// models directory, and that the manifest is for the model under test.
|
||||
checkManifestList := func() {
|
||||
t.Helper()
|
||||
|
||||
mandir := filepath.Join(os.Getenv("OLLAMA_MODELS"), "manifests/")
|
||||
var entries []string
|
||||
t.Logf("dir entries:")
|
||||
fsys := os.DirFS(mandir)
|
||||
err := fs.WalkDir(fsys, ".", func(path string, info fs.DirEntry, err error) error {
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
t.Logf(" %s", fs.FormatDirEntry(info))
|
||||
if info.IsDir() {
|
||||
return nil
|
||||
}
|
||||
path = strings.TrimPrefix(path, mandir)
|
||||
entries = append(entries, path)
|
||||
return nil
|
||||
})
|
||||
if err != nil {
|
||||
t.Fatalf("failed to walk directory: %v", err)
|
||||
}
|
||||
|
||||
if len(entries) != 1 {
|
||||
t.Errorf("len(got) = %d, want 1", len(entries))
|
||||
return // do not use Fatal so following steps run
|
||||
}
|
||||
|
||||
g := entries[0] // raw path
|
||||
g = filepath.ToSlash(g)
|
||||
w := model.ParseName(wantStableName).Filepath()
|
||||
w = filepath.ToSlash(w)
|
||||
if g != w {
|
||||
t.Errorf("\ngot: %s\nwant: %s", g, w)
|
||||
}
|
||||
}
|
||||
|
||||
checkOK := func(w *httptest.ResponseRecorder) {
|
||||
t.Helper()
|
||||
if w.Code != http.StatusOK {
|
||||
t.Errorf("code = %d, want 200", w.Code)
|
||||
t.Logf("body: %s", w.Body.String())
|
||||
}
|
||||
cases := []string{
|
||||
"mistral",
|
||||
"llama3:latest",
|
||||
"library/phi3:q4_0",
|
||||
"registry.ollama.ai/library/gemma:q5_K_M",
|
||||
// TODO: host:port currently fails on windows (#4107)
|
||||
// "localhost:5000/alice/bob:latest",
|
||||
}
|
||||
|
||||
var s Server
|
||||
testMakeRequestDialContext = func(ctx context.Context, _, _ string) (net.Conn, error) {
|
||||
var d net.Dialer
|
||||
return d.DialContext(ctx, "tcp", r.Listener.Addr().String())
|
||||
for _, tt := range cases {
|
||||
t.Run(tt, func(t *testing.T) {
|
||||
w := createRequest(t, s.CreateHandler, api.CreateRequest{
|
||||
Name: tt,
|
||||
Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, nil, nil)),
|
||||
Stream: &stream,
|
||||
})
|
||||
|
||||
if w.Code != http.StatusOK {
|
||||
t.Fatalf("expected status 200 got %d", w.Code)
|
||||
}
|
||||
|
||||
expect, err := json.Marshal(map[string]string{"error": "a model with that name already exists"})
|
||||
if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
t.Run("create", func(t *testing.T) {
|
||||
w = createRequest(t, s.CreateHandler, api.CreateRequest{
|
||||
Name: strings.ToUpper(tt),
|
||||
Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, nil, nil)),
|
||||
Stream: &stream,
|
||||
})
|
||||
|
||||
if w.Code != http.StatusBadRequest {
|
||||
t.Fatalf("expected status 500 got %d", w.Code)
|
||||
}
|
||||
|
||||
if !bytes.Equal(w.Body.Bytes(), expect) {
|
||||
t.Fatalf("expected error %s got %s", expect, w.Body.String())
|
||||
}
|
||||
})
|
||||
|
||||
t.Run("pull", func(t *testing.T) {
|
||||
w := createRequest(t, s.PullHandler, api.PullRequest{
|
||||
Name: strings.ToUpper(tt),
|
||||
Stream: &stream,
|
||||
})
|
||||
|
||||
if w.Code != http.StatusBadRequest {
|
||||
t.Fatalf("expected status 500 got %d", w.Code)
|
||||
}
|
||||
|
||||
if !bytes.Equal(w.Body.Bytes(), expect) {
|
||||
t.Fatalf("expected error %s got %s", expect, w.Body.String())
|
||||
}
|
||||
})
|
||||
|
||||
t.Run("copy", func(t *testing.T) {
|
||||
w := createRequest(t, s.CopyHandler, api.CopyRequest{
|
||||
Source: tt,
|
||||
Destination: strings.ToUpper(tt),
|
||||
})
|
||||
|
||||
if w.Code != http.StatusBadRequest {
|
||||
t.Fatalf("expected status 500 got %d", w.Code)
|
||||
}
|
||||
|
||||
if !bytes.Equal(w.Body.Bytes(), expect) {
|
||||
t.Fatalf("expected error %s got %s", expect, w.Body.String())
|
||||
}
|
||||
})
|
||||
})
|
||||
}
|
||||
t.Cleanup(func() { testMakeRequestDialContext = nil })
|
||||
|
||||
t.Logf("creating")
|
||||
checkOK(createRequest(t, s.CreateHandler, api.CreateRequest{
|
||||
// Start with the stable name, and later use a case-shuffled
|
||||
// version.
|
||||
Name: wantStableName,
|
||||
|
||||
Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, nil, nil)),
|
||||
Stream: &stream,
|
||||
}))
|
||||
checkManifestList()
|
||||
|
||||
t.Logf("creating (again)")
|
||||
checkOK(createRequest(t, s.CreateHandler, api.CreateRequest{
|
||||
Name: name(),
|
||||
Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, nil, nil)),
|
||||
Stream: &stream,
|
||||
}))
|
||||
checkManifestList()
|
||||
|
||||
t.Logf("pulling")
|
||||
checkOK(createRequest(t, s.PullHandler, api.PullRequest{
|
||||
Name: name(),
|
||||
Stream: &stream,
|
||||
Insecure: true,
|
||||
}))
|
||||
checkManifestList()
|
||||
|
||||
t.Logf("copying")
|
||||
checkOK(createRequest(t, s.CopyHandler, api.CopyRequest{
|
||||
Source: name(),
|
||||
Destination: name(),
|
||||
}))
|
||||
checkManifestList()
|
||||
}
|
||||
|
||||
func TestShow(t *testing.T) {
|
||||
|
|
|
|||
|
|
@ -298,13 +298,6 @@ func (n Name) LogValue() slog.Value {
|
|||
return slog.StringValue(n.String())
|
||||
}
|
||||
|
||||
func (n Name) EqualFold(o Name) bool {
|
||||
return strings.EqualFold(n.Host, o.Host) &&
|
||||
strings.EqualFold(n.Namespace, o.Namespace) &&
|
||||
strings.EqualFold(n.Model, o.Model) &&
|
||||
strings.EqualFold(n.Tag, o.Tag)
|
||||
}
|
||||
|
||||
func isValidLen(kind partKind, s string) bool {
|
||||
switch kind {
|
||||
case kindHost:
|
||||
|
|
|
|||
Loading…
Reference in a new issue