model : support granite multilingual embeddings R2 (ibm-granite/granite-embedding-{97,311}m-multilingual-r2) (#22716)

* 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)

src/llama-hparams.h

@@ -23,6 +23,9 @@ enum llama_swa_type {
     LLAMA_SWA_TYPE_SYMMETRIC = 3,
 };
 
+// forward declaration; full definition in llama-graph.h
+enum llm_ffn_op_type : int;
+
 struct llama_hparams_posnet {
     uint32_t n_embd;
     uint32_t n_layer;
@@ -227,6 +230,14 @@ struct llama_hparams {
     enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
 
 
+    // Resolved FFN gated activation flavor for archs that read
+    // `<arch>.hidden_activation` from the GGUF (e.g. ModernBert derivatives).
+    // Defaults to LLM_FFN_NONE (sentinel = 0); the mapping from the GGUF
+    // string to a real op is done at hparam-load time via
+    // llm_ffn_op_type_from_string() in llama-model.cpp, mirroring how
+    // rope_scaling_type_train is handled.
+    enum llm_ffn_op_type llm_ffn_op;
+
     // Step35: optional per-layer clamps for (Swi)GLU
     std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_exp; // clamping for expert FFN
     std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_shexp; // shared expert