convert-hf-to-gguf.py

@@ -2393,16 +2393,6 @@ class JambaModel(Model):
 
         return "gpt-2"
 
-    def set_vocab(self):
-        if (self.dir_model / "tokenizer.model").is_file():
-            # Using Jamba's tokenizer.json causes errors on model load
-            # (something about "byte not found in vocab"),
-            # but there's a working tokenizer.model
-            self._set_vocab_sentencepiece()
-        else:
-            # Some Jamba models only have a tokenizer.json, which works.
-            self._set_vocab_gpt2()
-
     def set_gguf_parameters(self):
         d_model = self.find_hparam(["hidden_size", "mamba_d_model"])
         d_conv  = self.find_hparam(["mamba_d_conv"],  optional=True) or 4
@@ -2422,7 +2412,7 @@ class JambaModel(Model):
 
         self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_context_length(self.find_hparam(["max_position_embeddings", "n_ctx"]))
+        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(d_model)
         self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
         self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
@@ -2440,15 +2430,6 @@ class JambaModel(Model):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
 
-        # Mini-Jamba
-        name = name.replace(".moe.", ".feed_forward.")
-        if bid is not None:
-            moe_offset = self.hparams["expert_layer_offset"]
-            moe_period = self.hparams["expert_layer_period"]
-
-            if not (bid >= moe_offset and (bid - moe_offset) % moe_period == 0):
-                name = name.replace(".experts.0.", ".")
-
         # process the experts separately
         if ".feed_forward.experts." in name:
             n_experts = self.hparams["num_experts"]
@@ -2489,15 +2470,6 @@ class JambaModel(Model):
 
         yield new_name, data_torch
 
-    def write_tensors(self):
-        super().write_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
     # same as Mamba
     def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
         del n_dims  # unused

gguf-py/gguf/tensor_mapping.py

@@ -207,7 +207,6 @@ class TensorNameMap:
             "model.layers.{bid}.ffn_norm",                                   # internlm2
             "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
             "model.layers.{bid}.pre_ff_layernorm",                           # jamba
-            "model.layers.{bid}.pre_moe_layernorm",                          # mini-jamba
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (
@@ -391,12 +390,10 @@ class TensorNameMap:
 
         MODEL_TENSOR.SSM_B_NORM: (
             "model.layers.{bid}.mamba.b_layernorm",  # jamba
-            "model.layers.{bid}.mamba.B_layernorm",  # mini-jamba
         ),
 
         MODEL_TENSOR.SSM_C_NORM: (
             "model.layers.{bid}.mamba.c_layernorm",  # jamba
-            "model.layers.{bid}.mamba.C_layernorm",  # mini-jamba
         ),
 
         MODEL_TENSOR.SSM_D: (