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)

gguf-py/gguf/constants.py

@@ -150,6 +150,7 @@ class Keys:
         EMBD_LENGTH_PER_LAYER_INP         = "{arch}.embedding_length_per_layer_input"
         SWIGLU_CLAMP_EXP                  = "{arch}.swiglu_clamp_exp"
         SWIGLU_CLAMP_SHEXP                = "{arch}.swiglu_clamp_shexp"
+        HIDDEN_ACT                        = "{arch}.hidden_activation"
         DENSE_FEAT_IN_SIZE                = "{arch}.{dense}_feat_in"
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
 

gguf-py/gguf/gguf_writer.py

@@ -853,6 +853,9 @@ class GGUFWriter:
     def add_swiglu_clamp_shexp(self, values: Sequence[float]) -> None:
         self.add_array(Keys.LLM.SWIGLU_CLAMP_SHEXP.format(arch=self.arch), values)
 
+    def add_hidden_act(self, value: str) -> None:
+        self.add_string(Keys.LLM.HIDDEN_ACT.format(arch=self.arch), value)
+
     def add_expert_group_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value)