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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
76 lines
1.7 KiB
Plaintext
76 lines
1.7 KiB
Plaintext
---
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title: Droid
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---
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## Install
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Install the [Droid CLI](https://factory.ai/):
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```bash
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curl -fsSL https://app.factory.ai/cli | sh
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```
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<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>
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## Usage with Ollama
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Add a local configuration block to `~/.factory/config.json`:
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```json
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{
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"custom_models": [
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{
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"model_display_name": "qwen3-coder [Ollama]",
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"model": "qwen3-coder",
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"base_url": "http://localhost:11434/v1/",
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"api_key": "not-needed",
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"provider": "generic-chat-completion-api",
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"max_tokens": 32000
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}
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]
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}
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```
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## Cloud Models
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`qwen3-coder:480b-cloud` is the recommended model for use with Droid.
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Add the cloud configuration block to `~/.factory/config.json`:
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```json
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{
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"custom_models": [
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{
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"model_display_name": "qwen3-coder [Ollama Cloud]",
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"model": "qwen3-coder:480b-cloud",
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"base_url": "http://localhost:11434/v1/",
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"api_key": "not-needed",
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"provider": "generic-chat-completion-api",
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"max_tokens": 128000
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}
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]
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}
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```
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## Connecting to ollama.com
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1. Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
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2. Add the cloud configuration block to `~/.factory/config.json`:
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```json
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{
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"custom_models": [
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{
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"model_display_name": "qwen3-coder [Ollama Cloud]",
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"model": "qwen3-coder:480b",
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"base_url": "https://ollama.com/v1/",
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"api_key": "OLLAMA_API_KEY",
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"provider": "generic-chat-completion-api",
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"max_tokens": 128000
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}
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]
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}
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```
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Run `droid` in a new terminal to load the new settings. |