Revert "nvidia nemotron nano v2 (nemotronh) (ggml-org#15507)"

Author: Nexesenex

convert_hf_to_gguf.py

@@ -7546,13 +7546,9 @@ def __init__(self, *args, **kwargs):
         ]
 
         # n_group and d_inner are used during reshape_tensors for mamba2
-        # NOTE: Explicitly include hparam prefix prefix for d_model to
-        #   disambiguate with top-level head_dim
-        # NOTE 2: If needed for future models, this can be isolated in a method
-        #   to separate the prefix setting and teh keys used
-        self.d_model = self.find_hparam([f"{self.hparam_prefixes[0]}_head_dim", "hidden_size", "d_model"])
-        self.n_group = self.find_hparam(["n_groups", "num_groups"])
-        self.d_inner = self.find_hparam(["expand", "num_heads"]) * self.d_model
+        self.d_model = self.find_hparam(["hidden_size", "d_model"])
+        self.n_group = self.find_hparam(["n_groups"])
+        self.d_inner = self.find_hparam(["expand"]) * self.d_model
 
     def get_attn_layers(self):
         # Explicit list of layer type names
@@ -7613,12 +7609,12 @@ def set_gguf_parameters(self):
 
         ## Mamba mixer params ##
         self.gguf_writer.add_ssm_conv_kernel(self.find_hparam(["conv_kernel", "d_conv"]))
-        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state", "state_dim", "ssm_state_size"]))
+        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state"]))
         self.gguf_writer.add_ssm_group_count(self.n_group)
         self.gguf_writer.add_ssm_inner_size(self.d_inner)
         # NOTE: The mamba_dt_rank is _not_ the right field for how this is used
         #   in llama.cpp
-        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads", "num_heads"]))
+        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads"]))
 
         ## Attention params ##
         head_count_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
@@ -7645,55 +7641,6 @@ def set_vocab(self):
         Mamba2Model.set_vocab(self)
 
 
-@ModelBase.register("NemotronHForCausalLM")
-class NemotronHModel(GraniteHybridModel):
-    """Hybrid mamba2/attention model from NVIDIA"""
-    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Save the top-level head_dim for later
-        self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
-        assert self.head_dim is not None, "Could not find the attention head dim in config"
-
-        # Don't use expand to calculate d_inner
-        self.d_inner = self.find_hparam(["num_heads"]) * self.d_model
-
-        # Update the ssm / attn / mlp layers
-        # M: Mamba2, *: Attention, -: MLP
-        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
-        self._ssm_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "M"]
-        self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "-"]
-
-    def get_attn_layers(self):
-        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
-        assert len(hybrid_override_pattern) == self.block_count, "Mismatch between hybrid override and num_hidden_layers!"
-        return [i for i, val in enumerate(hybrid_override_pattern) if val == "*"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_key_length(self.head_dim)
-        self.gguf_writer.add_value_length(self.head_dim)
-
-        # Set feed_forward_length
-        # NOTE: This will trigger an override warning. This is preferrable to
-        #   duplicating all the parent logic
-        n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
-        self.gguf_writer.add_feed_forward_length([
-            n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
-        ])
-
-    def set_vocab(self):
-        super().set_vocab()
-
-        # The tokenizer _does_ add a BOS token (via post_processor type
-        # TemplateProcessing) but does not set add_bos_token to true in the
-        # config, so we need to explicitly override it here.
-        self.gguf_writer.add_add_bos_token(True)
-
-
 @ModelBase.register("BailingMoeForCausalLM")
 class BailingMoeModel(TextModel):
     model_arch = gguf.MODEL_ARCH.BAILINGMOE

gguf-py/gguf/constants.py

@@ -367,7 +367,6 @@ class MODEL_ARCH(IntEnum):
     T5ENCODER        = auto()
     JAIS             = auto()
     NEMOTRON         = auto()
-    NEMOTRON_H       = auto()
     EXAONE           = auto()
     EXAONE4          = auto()
     GRANITE          = auto()
@@ -701,7 +700,6 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.T5ENCODER:        "t5encoder",
     MODEL_ARCH.JAIS:             "jais",
     MODEL_ARCH.NEMOTRON:         "nemotron",
-    MODEL_ARCH.NEMOTRON_H:       "nemotron_h",
     MODEL_ARCH.EXAONE:           "exaone",
     MODEL_ARCH.EXAONE4:          "exaone4",
     MODEL_ARCH.GRANITE:          "granite",
@@ -2299,25 +2297,6 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
-    MODEL_ARCH.NEMOTRON_H: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.SSM_IN,
-        MODEL_TENSOR.SSM_CONV1D,
-        MODEL_TENSOR.SSM_DT,
-        MODEL_TENSOR.SSM_A,
-        MODEL_TENSOR.SSM_D,
-        MODEL_TENSOR.SSM_NORM,
-        MODEL_TENSOR.SSM_OUT,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
     MODEL_ARCH.EXAONE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/tensor_mapping.py

@@ -191,7 +191,6 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.q_proj",                       # llama4
             "model.transformer.blocks.{bid}.q_proj",                     # llada
             "layers.{bid}.self_attn.q_proj",                             # qwen3-embedding
-            "backbone.layers.{bid}.mixer.q_proj",                        # nemotron-h
         ),
 
         # Attention key
@@ -210,7 +209,6 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.k_proj",                     # llama4
             "model.transformer.blocks.{bid}.k_proj",                   # llada
             "layers.{bid}.self_attn.k_proj",                           # qwen3-embedding
-            "backbone.layers.{bid}.mixer.k_proj",                      # nemotron-h
         ),
 
         # Attention value
@@ -228,7 +226,6 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.v_proj",                       # llama4
             "model.transformer.blocks.{bid}.v_proj",                     # llada
             "layers.{bid}.self_attn.v_proj",                             # qwen3-embedding
-            "backbone.layers.{bid}.mixer.v_proj",                        # nemotron-h
         ),
 
         # Attention output
@@ -263,7 +260,6 @@ class TensorNameMap:
             "transformer_encoder.{bid}.wo",                                 # neobert
             "model.transformer.blocks.{bid}.attn_out",                      # llada
             "layers.{bid}.self_attn.o_proj",                                # qwen3-embedding
-            "backbone.layers.{bid}.mixer.o_proj",                           # nemotron-h
         ),
 
         # Attention output norm
@@ -391,7 +387,6 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.up",                 # smallthinker
             "model.transformer.blocks.{bid}.up_proj",                 # llada
             "layers.{bid}.mlp.up_proj",                               # qwen3-embedding
-            "backbone.layers.{bid}.mixer.up_proj",                    # nemotron-h
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
@@ -485,7 +480,6 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.down",               # smallthinker
             "model.transformer.blocks.{bid}.ff_out",                  # llada
             "layers.{bid}.mlp.down_proj",                             # qwen3-embedding
-            "backbone.layers.{bid}.mixer.down_proj",                  # nemotron-h
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (

src/llama-arch.cpp

@@ -69,7 +69,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_T5ENCODER,        "t5encoder"        },
     { LLM_ARCH_JAIS,             "jais"             },
     { LLM_ARCH_NEMOTRON,         "nemotron"         },
-    { LLM_ARCH_NEMOTRON_H,       "nemotron_h"       },
     { LLM_ARCH_EXAONE,           "exaone"           },
     { LLM_ARCH_EXAONE4,          "exaone4"          },
     { LLM_ARCH_RWKV6,            "rwkv6"            },
@@ -1551,31 +1550,6 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
-    {
-        LLM_ARCH_NEMOTRON_H,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
-            { LLM_TENSOR_OUTPUT,         "output" },
-            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
-            // mamba(2) ssm layers
-            { LLM_TENSOR_SSM_IN,         "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,     "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_DT,         "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,          "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,          "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_NORM,       "blk.%d.ssm_norm" },
-            { LLM_TENSOR_SSM_OUT,        "blk.%d.ssm_out" },
-            // attention layers
-            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output" },
-            // dense FFN
-            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
-        },
-    },
     {
         LLM_ARCH_EXAONE,
         {
@@ -2381,7 +2355,6 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_PLAMO2:
         case LLM_ARCH_GRANITE_HYBRID:
         case LLM_ARCH_LFM2:
-        case LLM_ARCH_NEMOTRON_H:
             return true;
         default:
             return false;

src/llama-arch.h

@@ -73,7 +73,6 @@ enum llm_arch {
     LLM_ARCH_T5ENCODER,
     LLM_ARCH_JAIS,
     LLM_ARCH_NEMOTRON,
-    LLM_ARCH_NEMOTRON_H,
     LLM_ARCH_EXAONE,
     LLM_ARCH_EXAONE4,
     LLM_ARCH_RWKV6,

src/llama-model-loader.cpp

@@ -793,7 +793,9 @@ const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::stri
 }
 
 struct ggml_tensor * llama_model_loader::create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list<int64_t> & ne, int flags) {
-   // LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, name.c_str());
+
+    // LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, name.c_str());
+
     const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
 
     if (cur == NULL) {