em whitespace

Author: ngxson

convert_hf_to_gguf.py

@@ -9806,7 +9806,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 @ModelBase.register("JanusForConditionalGeneration")
 class JanusProModel(LlamaModel):
     model_arch = gguf.MODEL_ARCH.LLAMA  # reuse Llama arch
-    
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # Skip vision, aligner, and generation tensors
         skip_prefixes = (
@@ -9819,12 +9819,12 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         )
         if name.startswith(skip_prefixes):
             return []
-        
+
         if name.startswith('model.language_model.'):
             name = name.replace('model.language_model.', 'model.')
         elif name.startswith('language_model.'):
             name = name.replace('language_model.', '')
-        
+
         return super().modify_tensors(data_torch, name, bid)
 
 
@@ -9838,25 +9838,25 @@ def __init__(self, *args, **kwargs):
             hidden_size = self.hparams_vision.get("hidden_size")
             if mlp_ratio is not None and hidden_size is not None:
                 self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
-    
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         assert self.hparams_vision is not None
-        
+
         self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.JANUS_PRO)
-        
+
         self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
-        
+
         hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
         if hidden_act == "gelu":
             self.gguf_writer.add_vision_use_gelu(True)
         elif hidden_act == "silu":
             self.gguf_writer.add_vision_use_silu(True)
-        
+
     def _map_aligner_tensor(self, data_torch: Tensor, name: str) -> Iterable[tuple[str, Tensor]]:
         """Map aligner tensors to projector format"""
         suffix = ".bias" if name.endswith(".bias") else ".weight"
-        
+
         if name.startswith("model.aligner."):
             local_name = name[len("model.aligner."):]
         elif name.startswith("aligner."):
@@ -9873,17 +9873,17 @@ def _map_aligner_tensor(self, data_torch: Tensor, name: str) -> Iterable[tuple[s
             mm_index = int(parts[1]) + 1
         else:
             raise ValueError(f"Unsupported Janus aligner tensor: {name}")
-        
+
         tensor_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_index, suffix=suffix)
         return [(tensor_name, data_torch)]
-    
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
-        
+
         # Skip language model tensors as they will be handled by `JanusProModel`
         if name.startswith(('model.language_model.', 'language_model.')):
             return []
-        
+
         # Skip generation-related components
         skip_generation_prefixes = (
             'model.vqmodel.',
@@ -9897,15 +9897,15 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         )
         if name.startswith(skip_generation_prefixes):
             return []
-        
+
         # Handle aligner tensors
         if name.startswith(('model.aligner.', 'aligner.')):
             return list(self._map_aligner_tensor(data_torch, name))
-        
+
         # Handle vision tensors
         if name.startswith(('model.vision_model.', 'vision_model.')):
             return [(self.map_tensor_name(name), data_torch)]
-        
+
         return []
 
 

tools/mtmd/clip.cpp

@@ -597,7 +597,7 @@ struct clip_graph {
                 model.mm_1_w, model.mm_1_b,
                 hparams.ffn_op,
                 -1);
-                
+
         } else {
             GGML_ABORT("SigLIP: Unsupported projector type");
         }