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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
47 lines
904 B
Go
47 lines
904 B
Go
package llm
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import (
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"bytes"
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"os"
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)
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// StatusWriter is a writer that captures error messages from the llama runner process
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type StatusWriter struct {
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LastErrMsg string
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out *os.File
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}
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func NewStatusWriter(out *os.File) *StatusWriter {
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return &StatusWriter{
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out: out,
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}
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}
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// TODO - regex matching to detect errors like
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// libcublasLt.so.11: cannot open shared object file: No such file or directory
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var errorPrefixes = []string{
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"error:",
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"CUDA error",
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"ROCm error",
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"cudaMalloc failed",
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"\"ERR\"",
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"error loading model",
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"GGML_ASSERT",
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"Deepseek2 does not support K-shift",
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}
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func (w *StatusWriter) Write(b []byte) (int, error) {
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var errMsg string
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for _, prefix := range errorPrefixes {
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if _, after, ok := bytes.Cut(b, []byte(prefix)); ok {
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errMsg = prefix + string(bytes.TrimSpace(after))
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}
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}
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if errMsg != "" {
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w.LastErrMsg = errMsg
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}
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return w.out.Write(b)
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}
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