* init conversion
* convert: ok
* model loaded
* add server code
* improve conversion script
* support shared prompt prefix
* add docs, imorove UX a bit
* add vision support
* add openjev tiny model for testing
* add dev docs
* support lev & kev
* clean up
* fix lev noul
* fix py lint
* nits docs
* clarify about not supporting date_facts
* Add support for the ibm-granite/granite-embedding-{97m,311m}-multilingual-r2 embedding models:
* Added a version of the gpt4o tokenizer that has a fixed regex (better handling of marks), and different token merging setting for the 97m model
* Reused gemma4 tokenizer for the 311m model
* granite-embedding-*-multilingual-r2 : add support SwiGLU FFN for Granite Embedding Multilingual R2
* added new GGUF key <arch>.hidden_activation (LLM_KV_HIDDEN_ACT) + writer
* added a forward declaration of llm_ffn_op_type to llama-hparams.h
* added llm_ffn_op in hparams
* added LLM_FFN_NONE = 0 sentinel to llm_ffn_op_type (value-initialization), modern-bert: explicitly assigns LLM_FFN_GEGLU before reading GGUF (unchanged).
* centralized hidden_act mapping in llama-model.cpp, added llm_ffn_op_type_from_string() helper, mirroring rope_scaling_type/llama_rope_scaling_type_from_string()
* modern-bert reads the GGUF key (when present) and uses the resulting op in its FFN graph
* Added granite-embedding-{97m,311m}-multilingual-r2 to the converter code
* Added the hashes for the granite embedding multilingual R2 models
* Set the hidden_activation in the GGUF if the field is present in config.json (such as for the granite embedding models)
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture
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Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Xuan-Son Nguyen