Sync with upstream ollama/ollama and restore Tesla K80 (compute 3.7) support

This commit represents a complete rework after pulling the latest changes from official ollama/ollama repository and re-applying Tesla K80 compatibility patches. ## Key Changes ### CUDA Compute Capability 3.7 Support (Tesla K80) - Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt - Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset - Using 37-virtual (PTX with JIT compilation) for maximum compatibility ### Legacy Toolchain Compatibility - **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80) - **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7) - **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h) ### CPU Architecture Trade-offs Due to GCC 10.5 limitation, sacrificed newer CPU optimizations: - Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+) - Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA - Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility) ### Build System Updates - Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7 - Added -Wno-deprecated-gpu-targets flag to suppress warnings - Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI ### Upstream Sync Merged latest llama.cpp changes including: - Enhanced KV cache management with ISWA and hybrid memory support - Improved multi-modal support (mtmd framework) - New model architectures (Gemma3, Llama4, Qwen3, etc.) - GPU backend improvements for CUDA, Metal, and ROCm - Updated quantization support and GGUF format handling ### Documentation - Updated CLAUDE.md with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in official Ollama due to legacy driver/CUDA requirements. The toolchain constraint creates a deadlock: - K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI We accept the loss of cutting-edge CPU optimizations to enable running modern LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-18 03:37:09 +00:00 · 2025-11-05 14:03:05 +08:00
parent fabe2c5cb7
commit ef14fb5b26
817 changed files with 241634 additions and 70888 deletions
--- a/docs/integrations/cline.mdx
+++ b/docs/integrations/cline.mdx
@@ -0,0 +1,38 @@
+---
+title: Cline
+---
+
+## Install
+
+Install [Cline](https://docs.cline.bot/getting-started/installing-cline) in your IDE.
+
+
+## Usage with Ollama
+
+1. Open Cline settings > `API Configuration` and set `API Provider` to `Ollama`
+2. Select a model under `Model` or type one (e.g. `qwen3`)
+3. Update the context window to at least 32K tokens under `Context Window`
+
+<Note>Coding tools require a larger context window. It is recommended to use a context window of at least 32K tokens. See [Context length](/context-length) for more information.</Note>
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/cline-settings.png" 
+    alt="Cline settings configuration showing API Provider set to Ollama"
+    width="50%"
+  />
+</div>
+
+
+    
+## Connecting to ollama.com
+1. Create an [API key](https://ollama.com/settings/keys) from ollama.com
+2. Click on `Use custom base URL` and set it to `https://ollama.com`
+3. Enter your **Ollama API Key**
+4. Select a model from the list
+
+
+### Recommended Models
+
+- `qwen3-coder:480b` 
+- `deepseek-v3.1:671b`
--- a/docs/integrations/codex.mdx
+++ b/docs/integrations/codex.mdx
@@ -0,0 +1,56 @@
+---
+title: Codex
+---
+
+
+## Install
+
+Install the [Codex CLI](https://developers.openai.com/codex/cli/):
+
+```
+npm install -g @openai/codex
+```
+
+## Usage with Ollama
+
+<Note>Codex requires a larger context window. It is recommended to use a context window of at least 32K tokens.</Note>
+
+To use `codex` with Ollama, use the `--oss` flag:
+
+```
+codex --oss
+```
+
+### Changing Models
+
+By default, codex will use the local `gpt-oss:20b` model. However, you can specify a different model with the `-m` flag:
+
+```
+codex --oss -m gpt-oss:120b
+```
+
+### Cloud Models
+
+```
+codex --oss -m gpt-oss:120b-cloud
+```
+
+
+## Connecting to ollama.com
+
+
+Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
+
+To use ollama.com directly, edit your `~/.codex/config.toml` file to point to ollama.com.
+
+```toml
+model = "gpt-oss:120b"
+model_provider = "ollama"
+
+[model_providers.ollama]
+name = "Ollama"
+base_url = "https://ollama.com/v1"
+env_key = "OLLAMA_API_KEY"
+```
+
+Run `codex` in a new terminal to load the new settings.
--- a/docs/integrations/droid.mdx
+++ b/docs/integrations/droid.mdx
@@ -0,0 +1,76 @@
+---
+title: Droid
+---
+
+
+## Install
+
+Install the [Droid CLI](https://factory.ai/):
+
+```bash
+curl -fsSL https://app.factory.ai/cli | sh
+```
+
+<Note>Droid requires a larger context window. It is recommended to use a context window of at least 32K tokens. See [Context length](/context-length) for more information.</Note>
+
+## Usage with Ollama
+
+Add a local configuration block to `~/.factory/config.json`:
+
+```json
+{
+  "custom_models": [
+    {
+      "model_display_name": "qwen3-coder [Ollama]",
+      "model": "qwen3-coder",
+      "base_url": "http://localhost:11434/v1/",
+      "api_key": "not-needed",
+      "provider": "generic-chat-completion-api",
+      "max_tokens": 32000 
+    }
+  ]
+}
+```
+
+
+## Cloud Models
+`qwen3-coder:480b-cloud` is the recommended model for use with Droid.
+
+Add the cloud configuration block to `~/.factory/config.json`:
+
+```json
+{
+  "custom_models": [
+    {
+      "model_display_name": "qwen3-coder [Ollama Cloud]",
+      "model": "qwen3-coder:480b-cloud",
+      "base_url": "http://localhost:11434/v1/",
+      "api_key": "not-needed",
+      "provider": "generic-chat-completion-api",
+      "max_tokens": 128000
+    }
+  ]
+}
+```
+
+## Connecting to ollama.com
+
+1. Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
+2. Add the cloud configuration block to `~/.factory/config.json`:
+
+   ```json
+   {
+     "custom_models": [
+       {
+         "model_display_name": "qwen3-coder [Ollama Cloud]",
+         "model": "qwen3-coder:480b",
+         "base_url": "https://ollama.com/v1/",
+         "api_key": "OLLAMA_API_KEY",
+         "provider": "generic-chat-completion-api",
+         "max_tokens": 128000
+       }
+     ]
+   }
+   ```
+
+Run `droid` in a new terminal to load the new settings.
--- a/docs/integrations/goose.mdx
+++ b/docs/integrations/goose.mdx
@@ -0,0 +1,49 @@
+---
+title: Goose
+---
+
+## Goose Desktop
+
+Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) Desktop.
+
+### Usage with Ollama
+1. In Goose, open **Settings** → **Configure Provider**.  
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/goose-settings.png" 
+    alt="Goose settings Panel"
+    width="75%"
+  />
+</div>
+2. Find **Ollama**, click **Configure** 
+3. Confirm **API Host** is `http://localhost:11434` and click Submit
+
+
+### Connecting to ollama.com
+
+1. Create an [API key](https://ollama.com/settings/keys) on ollama.com and save it in your `.env` 
+2. In Goose, set **API Host** to `https://ollama.com`
+
+
+## Goose CLI
+
+Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) CLI
+
+### Usage with Ollama
+1. Run `goose configure`
+2. Select **Configure Providers** and select **Ollama**
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/goose-cli.png" 
+    alt="Goose CLI"
+    width="50%"
+  />
+</div>
+3. Enter model name (e.g `qwen3`)
+
+### Connecting to ollama.com
+
+1. Create an [API key](https://ollama.com/settings/keys) on ollama.com and save it in your `.env` 
+2. Run `goose configure`
+3. Select **Configure Providers** and select **Ollama**
+4. Update **OLLAMA_HOST** to `https://ollama.com`
--- a/docs/integrations/jetbrains.mdx
+++ b/docs/integrations/jetbrains.mdx
@@ -0,0 +1,47 @@
+---
+title: JetBrains
+---
+
+<Note>This example uses **IntelliJ**; same steps apply to other JetBrains IDEs (e.g., PyCharm).</Note>
+
+## Install
+
+Install [IntelliJ](https://www.jetbrains.com/idea/).
+
+## Usage with Ollama
+
+<Note>
+  To use **Ollama**,  you will need a [JetBrains AI Subscription](https://www.jetbrains.com/ai-ides/buy/?section=personal&billing=yearly).
+</Note>
+
+1. In Intellij, click the **chat icon** located in the right sidebar
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/intellij-chat-sidebar.png" 
+    alt="Intellij Sidebar Chat"
+    width="50%"
+  />
+</div>
+
+2. Select the **current model** in the sidebar, then click **Set up Local Models**
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/intellij-current-model.png" 
+    alt="Intellij model bottom right corner"
+    width="50%"
+  />
+</div>
+
+3. Under **Third Party AI Providers**, choose **Ollama**  
+4. Confirm the **Host URL** is `http://localhost:11434`, then click **Ok**  
+5. Once connected, select a model under **Local models by Ollama**
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/intellij-local-models.png" 
+    alt="Zed star icon in bottom right corner"
+    width="50%"
+  />
+</div>
--- a/docs/integrations/n8n.mdx
+++ b/docs/integrations/n8n.mdx
@@ -0,0 +1,53 @@
+---
+title: n8n
+---
+
+## Install
+
+Install [n8n](https://docs.n8n.io/choose-n8n/).
+
+## Using Ollama Locally
+
+1. In the top right corner, click the dropdown and select **Create Credential**
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/n8n-credential-creation.png" 
+    alt="Create a n8n Credential"
+    width="75%"
+  />
+</div>
+
+2. Under **Add new credential** select **Ollama**
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/n8n-ollama-form.png" 
+    alt="Select Ollama under Credential"
+    width="75%"
+  />
+</div>
+3. Confirm Base URL is set to `http://localhost:11434` and click **Save**
+<Note> If connecting to `http://localhost:11434` fails, use `http://127.0.0.1:11434`</Note>
+4. When creating a new workflow, select **Add a first step** and select an **Ollama node** 
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/n8n-chat-node.png" 
+    alt="Add a first step with Ollama node"
+    width="75%"
+  />
+</div>
+5. Select your model of choice (e.g. `qwen3-coder`) 
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/n8n-models.png" 
+    alt="Set up Ollama credentials"
+    width="75%"
+  />
+</div>
+
+## Connecting to ollama.com
+1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**.  
+2. In n8n, click **Create Credential** and select **Ollama**
+4. Set the **API URL** to `https://ollama.com`
+5. Enter your **API Key** and click **Save**
+
+
--- a/docs/integrations/roo-code.mdx
+++ b/docs/integrations/roo-code.mdx
@@ -0,0 +1,30 @@
+---
+title: Roo Code
+---
+
+
+## Install
+
+Install [Roo Code](https://marketplace.visualstudio.com/items?itemName=RooVeterinaryInc.roo-cline) from the VS Code Marketplace.
+
+## Usage with Ollama
+
+1. Open Roo Code in VS Code and click the **gear icon** on the top right corner of the Roo Code window to open **Provider Settings**
+2. Set `API Provider` to `Ollama`
+3. (Optional) Update `Base URL` if your Ollama instance is running remotely. The default is `http://localhost:11434`
+4. Enter a valid `Model ID` (for example `qwen3` or `qwen3-coder:480b-cloud`)
+5. Adjust the `Context Window` to at least 32K tokens for coding tasks
+
+<Note>Coding tools require a larger context window. It is recommended to use a context window of at least 32K tokens. See [Context length](/context-length) for more information.</Note>
+
+## Connecting to ollama.com
+
+1. Create an [API key](https://ollama.com/settings/keys) from ollama.com
+2. Enable `Use custom base URL` and set it to `https://ollama.com`
+3. Enter your **Ollama API Key**
+4. Select a model from the list
+
+### Recommended Models
+
+- `qwen3-coder:480b`
+- `deepseek-v3.1:671b`
--- a/docs/integrations/vscode.mdx
+++ b/docs/integrations/vscode.mdx
@@ -0,0 +1,34 @@
+---
+title: VS Code 
+---
+
+## Install
+
+Install [VSCode](https://code.visualstudio.com/download). 
+
+## Usage with Ollama 
+
+1. Open Copilot side bar found in top right window
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/vscode-sidebar.png" 
+    alt="VSCode chat Sidebar"
+    width="75%"
+  />
+</div>
+2. Select the model drowpdown > **Manage models**
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/vscode-models.png" 
+    alt="VSCode model picker"
+    width="75%"
+  />
+</div>
+3. Enter **Ollama** under **Provider Dropdown** and select desired models (e.g `qwen3, qwen3-coder:480b-cloud`)
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/vscode-model-options.png" 
+    alt="VSCode model options dropdown"
+    width="75%"
+  />
+</div>
--- a/docs/integrations/xcode.mdx
+++ b/docs/integrations/xcode.mdx
@@ -0,0 +1,45 @@
+---
+title: Xcode 
+---
+
+## Install
+
+Install [XCode](https://developer.apple.com/xcode/)
+
+
+## Usage with Ollama 
+<Note> Ensure Apple Intelligence is setup and the latest XCode version is v26.0 </Note>
+
+1. Click **XCode** in top left corner > **Settings** 
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/xcode-intelligence-window.png" 
+    alt="Xcode Intelligence window"
+    width="50%"
+  />
+</div>
+
+2. Select **Locally Hosted**, enter port **11434** and click **Add**
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/xcode-locally-hosted.png" 
+    alt="Xcode settings"
+    width="50%"
+  />
+</div>
+
+3. Select the **star icon** on the top left corner and click the **dropdown**
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/xcode-chat-icon.png" 
+    alt="Xcode settings"
+    width="50%"
+  />
+</div>
+4. Click **My Account** and select your desired model
+
+
+## Connecting to ollama.com directly
+1. Create an [API key](https://ollama.com/settings/keys) from ollama.com 
+2. Select **Internet Hosted** and enter URL as `https://ollama.com`  
+3. Enter your **Ollama API Key** and click **Add** 
--- a/docs/integrations/zed.mdx
+++ b/docs/integrations/zed.mdx
@@ -0,0 +1,38 @@
+---
+title: Zed
+---
+
+## Install
+
+Install [Zed](https://zed.dev/download).
+
+## Usage with Ollama
+
+1. In Zed, click the **star icon** in the bottom-right corner, then select **Configure**.
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/zed-settings.png" 
+    alt="Zed star icon in bottom right corner"
+    width="50%"
+  />
+</div>
+
+2. Under **LLM Providers**, choose **Ollama**
+3. Confirm the **Host URL** is `http://localhost:11434`, then click **Connect**
+4. Once connected, select a model under **Ollama**
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/zed-ollama-dropdown.png" 
+    alt="Zed star icon in bottom right corner"
+    width="50%"
+  />
+</div>
+
+## Connecting to ollama.com
+1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**
+2. In Zed, open the **star icon** → **Configure**
+3. Under **LLM Providers**, select **Ollama**
+4. Set the **API URL** to `https://ollama.com`
+