mirror of
https://github.com/dogkeeper886/ollama37.git
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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
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92 lines
1005 B
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---
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title: CLI Reference
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---
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### Run a model
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```
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ollama run gemma3
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```
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#### Multiline input
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For multiline input, you can wrap text with `"""`:
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```
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>>> """Hello,
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... world!
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... """
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I'm a basic program that prints the famous "Hello, world!" message to the console.
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```
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#### Multimodal models
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```
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ollama run gemma3 "What's in this image? /Users/jmorgan/Desktop/smile.png"
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```
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### Download a model
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```
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ollama pull gemma3
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```
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### Remove a model
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```
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ollama rm gemma3
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```
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### List models
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```
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ollama ls
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```
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### Sign in to Ollama
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```
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ollama signin
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```
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### Sign out of Ollama
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```
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ollama signout
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```
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### Create a customized model
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First, create a `Modelfile`
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```
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FROM gemma3
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SYSTEM """You are a happy cat."""
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```
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Then run `ollama create`:
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```
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ollama create -f Modelfile
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```
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### List running models
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```
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ollama ps
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```
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### Stop a running model
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```
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ollama stop gemma3
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```
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### Start Ollama
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```
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ollama serve
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```
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To view a list of environment variables that can be set run `ollama serve --help`
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