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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
114 lines
2.8 KiB
Plaintext
114 lines
2.8 KiB
Plaintext
---
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title: Embeddings
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description: Generate text embeddings for semantic search, retrieval, and RAG.
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---
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Embeddings turn text into numeric vectors you can store in a vector database, search with cosine similarity, or use in RAG pipelines. The vector length depends on the model (typically 384–1024 dimensions).
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## Recommended models
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- [embeddinggemma](https://ollama.com/library/embeddinggemma)
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- [qwen3-embedding](https://ollama.com/library/qwen3-embedding)
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- [all-minilm](https://ollama.com/library/all-minilm)
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## Generate embeddings
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Use `/api/embed` with a single string.
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<Tabs>
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<Tab title="cURL">
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```shell
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curl -X POST http://localhost:11434/api/embed \
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-H "Content-Type: application/json" \
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-d '{
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"model": "embeddinggemma",
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"input": "The quick brown fox jumps over the lazy dog."
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}'
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```
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</Tab>
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<Tab title="Python">
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```python
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import ollama
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single = ollama.embed(
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model='embeddinggemma',
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input='The quick brown fox jumps over the lazy dog.'
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)
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print(len(single['embeddings'][0])) # vector length
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```
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</Tab>
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<Tab title="JavaScript">
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```javascript
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import ollama from 'ollama'
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const single = await ollama.embed({
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model: 'embeddinggemma',
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input: 'The quick brown fox jumps over the lazy dog.',
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})
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console.log(single.embeddings[0].length) // vector length
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```
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</Tab>
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</Tabs>
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<Note>
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The `/api/embed` endpoint returns L2‑normalized (unit‑length) vectors.
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</Note>
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## Generate a batch of embeddings
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Pass an array of strings to `input`.
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<Tabs>
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<Tab title="cURL">
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```shell
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curl -X POST http://localhost:11434/api/embed \
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-H "Content-Type: application/json" \
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-d '{
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"model": "embeddinggemma",
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"input": [
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"First sentence",
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"Second sentence",
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"Third sentence"
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]
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}'
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```
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</Tab>
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<Tab title="Python">
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```python
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import ollama
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batch = ollama.embed(
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model='embeddinggemma',
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input=[
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'The quick brown fox jumps over the lazy dog.',
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'The five boxing wizards jump quickly.',
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'Jackdaws love my big sphinx of quartz.',
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]
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)
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print(len(batch['embeddings'])) # number of vectors
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```
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</Tab>
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<Tab title="JavaScript">
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```javascript
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import ollama from 'ollama'
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const batch = await ollama.embed({
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model: 'embeddinggemma',
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input: [
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'The quick brown fox jumps over the lazy dog.',
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'The five boxing wizards jump quickly.',
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'Jackdaws love my big sphinx of quartz.',
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],
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})
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console.log(batch.embeddings.length) // number of vectors
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```
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</Tab>
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</Tabs>
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## Tips
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- Use cosine similarity for most semantic search use cases.
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- Use the same embedding model for both indexing and querying.
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