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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
63 lines
1.6 KiB
Go
63 lines
1.6 KiB
Go
package renderers
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import (
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"fmt"
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"github.com/ollama/ollama/api"
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)
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type Renderer interface {
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Render(messages []api.Message, tools []api.Tool, think *api.ThinkValue) (string, error)
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}
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type (
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RendererConstructor func() Renderer
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RendererRegistry struct {
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renderers map[string]RendererConstructor
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}
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)
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// RenderImgTags is a global flag that tells renderers to use [img] tags
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// for images. This is set by the Ollama server package on init, or left as
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// false for other environments where renderers are used
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var RenderImgTags bool
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func (r *RendererRegistry) Register(name string, renderer RendererConstructor) {
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r.renderers[name] = renderer
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}
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var registry = RendererRegistry{
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renderers: make(map[string]RendererConstructor),
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}
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func Register(name string, renderer RendererConstructor) {
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registry.Register(name, renderer)
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}
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func RenderWithRenderer(name string, msgs []api.Message, tools []api.Tool, think *api.ThinkValue) (string, error) {
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renderer := rendererForName(name)
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if renderer == nil {
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return "", fmt.Errorf("unknown renderer %q", name)
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}
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return renderer.Render(msgs, tools, think)
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}
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func rendererForName(name string) Renderer {
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if constructor, ok := registry.renderers[name]; ok {
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return constructor()
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}
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switch name {
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case "qwen3-coder":
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renderer := &Qwen3CoderRenderer{}
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return renderer
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case "qwen3-vl-instruct":
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renderer := &Qwen3VLRenderer{isThinking: false, useImgTags: RenderImgTags}
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return renderer
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case "qwen3-vl-thinking":
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renderer := &Qwen3VLRenderer{isThinking: true, useImgTags: RenderImgTags}
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return renderer
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default:
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return nil
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}
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}
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