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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
57 lines
1.0 KiB
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57 lines
1.0 KiB
Plaintext
---
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title: Codex
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---
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## Install
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Install the [Codex CLI](https://developers.openai.com/codex/cli/):
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```
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npm install -g @openai/codex
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```
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## Usage with Ollama
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<Note>Codex requires a larger context window. It is recommended to use a context window of at least 32K tokens.</Note>
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To use `codex` with Ollama, use the `--oss` flag:
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```
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codex --oss
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```
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### Changing Models
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By default, codex will use the local `gpt-oss:20b` model. However, you can specify a different model with the `-m` flag:
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```
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codex --oss -m gpt-oss:120b
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```
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### Cloud Models
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```
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codex --oss -m gpt-oss:120b-cloud
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```
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## Connecting to ollama.com
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Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
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To use ollama.com directly, edit your `~/.codex/config.toml` file to point to ollama.com.
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```toml
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model = "gpt-oss:120b"
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model_provider = "ollama"
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[model_providers.ollama]
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name = "Ollama"
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base_url = "https://ollama.com/v1"
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env_key = "OLLAMA_API_KEY"
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```
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Run `codex` in a new terminal to load the new settings.
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