Support StaticEmbedding model#790
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This commit adapts text-embeddings-inference for NVIDIA Jetson Orin (SM87) and L4 GPU (SM89), and integrates valuable community PRs. Changes: 1. SM87/SM89 CUDA Support - Added compute capability 8.7 and 8.9 support - Modified Dockerfile-cuda-all for multi-arch builds - Updated compute_cap.rs for SM87/89 detection Files: Dockerfile-cuda-all, cuda-all-entrypoint.sh, compute_cap.rs 2. PR huggingface#730: Qwen3 Reranker Support - Added classification head for Qwen3 reranking - Implemented template formatting system for chat-based reranking Files: models/qwen3.rs, core/templates.rs, core/lib.rs 3. PR huggingface#787: Batch Notification Performance Optimization - Implemented AtomicUsize counter for batch processing - Reduced unnecessary notify_one() calls - Only last request in batch triggers thread notification Files: core/infer.rs, router/http/server.rs, router/grpc/server.rs 4. PR huggingface#753: GeLU Activation Consistency Fix - Changed Gelu from approximate (gelu) to exact (gelu_erf) - Added NewGelu variant for backward compatibility Files: layers/linear.rs 5. PR huggingface#790: StaticEmbedding Model Support - Added support for 0_StaticEmbedding/ directory structure - Implemented fallback loading for model weights and tokenizer - Default to Mean pooling for StaticEmbedding models Files: models/static_embedding.rs (new), lib.rs, download.rs, router/lib.rs 6. PR huggingface#746: DebertaV2 Sequence Classification Support - Complete DebertaV2 model implementation - Support for sequence classification tasks (e.g., Llama Prompt Guard) - CPU and CUDA device support Files: models/debertav2.rs (new), lib.rs, models/mod.rs All changes have been tested and compile successfully with: cargo check --all-targets Compilation verified with CUDA support: cargo install --path router -F candle-cuda Target Hardware: NVIDIA Jetson Orin AGX (SM87), L4 GPU (SM89) Date: January 5, 2026
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What does this PR do?
Close #731
Looks like, for StaticEmbedding models, only the
0_StaticEmbedding/directory exists (containing both model weights and tokenizer). I've added fallback logic to load from there when root files are missing. (But, I believe that there might be a better way to handle this)Additionally, I've opened the PR, adding the
config.jsonfile to the root directory, which is essential for working with TEI.Log
Before submitting
instasnapshots?Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@alvarobartt