convert : adapt MiniCPM3 to separate rope_freqs insertion

MiniCPM3's tokenizer is treated as a SentencePiece tokenizer to avoid
having to run its custom Python code which mixes tokenization
in the same file as tool calls.

gguf-py : add long and short RoPE factors to tensor mappings

Empty, but the key names are used to populate the mappings.

convert_hf_to_gguf.py

@@ -1862,8 +1862,6 @@ class MiniCPM3Model(Model):
     def set_gguf_parameters(self):
         hparams = self.hparams
 
-        rope_dims = hparams["qk_rope_head_dim"]
-
         self.gguf_writer.add_file_type(self.ftype)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
@@ -1879,24 +1877,25 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is None:
-            return
+        if rope_scaling is not None:
+            rope_dims = self.hparams["qk_rope_head_dim"]
 
-        long_factors = rope_scaling.get('long_factor', None)
-        short_factors = rope_scaling.get('short_factor', None)
+            long_factors = rope_scaling.get('long_factor', None)
+            short_factors = rope_scaling.get('short_factor', None)
 
-        if long_factors is None or short_factors is None:
-            raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
+            if long_factors is None or short_factors is None:
+                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
 
-        if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
-            raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
+            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
+                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
 
-        self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG]  + ".weight", np.array(long_factors, dtype=np.float32))
-        self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32))
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
 
     def set_vocab(self):
-        self._set_vocab_llama_hf()
+        self._set_vocab_sentencepiece()
 
     def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
         if n_kv_head is not None and n_head != n_kv_head:

gguf-py/gguf/constants.py

@@ -877,6 +877,8 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
         MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FACTORS_LONG,
+        MODEL_TENSOR.ROPE_FACTORS_SHORT,
         MODEL_TENSOR.ATTN_NORM,
         MODEL_TENSOR.ATTN_Q_A,
         MODEL_TENSOR.ATTN_Q_B,

gguf-py/gguf/tensor_mapping.py

@@ -87,6 +87,9 @@ class TensorNameMap:
             "rope.freqs",  # llama-pth
             "rotary_pos_emb.inv_freq",  # chatglm
         ),
+
+        MODEL_TENSOR.ROPE_FACTORS_LONG: (),
+        MODEL_TENSOR.ROPE_FACTORS_SHORT: (),
     }
 
     block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {