mirror of
https://github.com/dogkeeper886/ollama37.git
synced 2025-12-18 11:47:07 +00:00
ef14fb5b26
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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
37 lines
1.2 KiB
Plaintext
37 lines
1.2 KiB
Plaintext
---
|
|
title: Usage
|
|
---
|
|
|
|
Ollama's API responses include metrics that can be used for measuring performance and model usage:
|
|
|
|
* `total_duration`: How long the response took to generate
|
|
* `load_duration`: How long the model took to load
|
|
* `prompt_eval_count`: How many input tokens were processed
|
|
* `prompt_eval_duration`: How long it took to evaluate the prompt
|
|
* `eval_count`: How many output tokens were processes
|
|
* `eval_duration`: How long it took to generate the output tokens
|
|
|
|
All timing values are measured in nanoseconds.
|
|
|
|
## Example response
|
|
|
|
For endpoints that return usage metrics, the response body will include the usage fields. For example, a non-streaming call to `/api/generate` may return the following response:
|
|
|
|
```json
|
|
{
|
|
"model": "gemma3",
|
|
"created_at": "2025-10-17T23:14:07.414671Z",
|
|
"response": "Hello! How can I help you today?",
|
|
"done": true,
|
|
"done_reason": "stop",
|
|
"total_duration": 174560334,
|
|
"load_duration": 101397084,
|
|
"prompt_eval_count": 11,
|
|
"prompt_eval_duration": 13074791,
|
|
"eval_count": 18,
|
|
"eval_duration": 52479709
|
|
}
|
|
```
|
|
|
|
For endpoints that return **streaming responses**, usage fields are included as part of the final chunk, where `done` is `true`.
|