EAGLE3 is an encoder-decoder based speculative decoding method:
- Extracts features from target model at specific layers
- Uses feature fusion layer to compress target features
- Generates draft tokens with single-layer decoder
- Maps draft vocabulary to target vocabulary via d2t tensor
Key changes:
- Add LLM_ARCH_EAGLE3 architecture
- Add EAGLE3 encoder/decoder graph (src/models/eagle3.cpp)
- Add feature extraction from target model layers
- Add g_embeddings handling for decoder input
- Add GGML_TENSOR_FLAG_SYNC for GPU synchronization
- Add --eagle3 flag for speculative-simple example
- Add EAGLE3 model conversion in convert_hf_to_gguf.py
* llama-server : implement universal assisted decoding
* Erase prompt tail for kv-cache
* set vocab_dft_compatible in common_speculative
* rename ctx_main to ctx_tgt
* move vocab_dft_compatible to spec struct
* clear mem_dft, remove mem
* detokenize id_last for incompatible models
* update comment
* add --spec-replace flag
* accept special tokens when translating between draft/main models
* Escape spec-replace
* clamp draft result to size to params.n_draft
* fix comment
* clean up code
* restore old example
* log common_speculative_are_compatible in speculative example
* fix
* Update common/speculative.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update common/speculative.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update common/speculative.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
ruixiangw
g2mt
Georgi Gerganov
Maxim Evtush