Repository: ggerganov/llama.cpp
File: gguf-py/gguf/constants.py (Python)

Code before:
    MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
    MODEL_ARCH.PLM:              "plm",
    MODEL_ARCH.BAILINGMOE:       "bailingmoe",
    MODEL_ARCH.BAILINGMOE2:      "bailingmoe2",
    MODEL_ARCH.DOTS1:            "dots1",
    MODEL_ARCH.ARCEE:            "arcee",
    MODEL_ARCH.ERNIE4_5:         "ernie4_5",
    MODEL_ARCH.ERNIE4_5_MOE:     "ernie4_5-moe",
    MODEL_ARCH.FALCON_H1:        "falcon-h1",
    MODEL_ARCH.HUNYUAN_MOE:      "hunyuan-moe",
    MODEL_ARCH.HUNYUAN_DENSE:    "hunyuan-dense",
    MODEL_ARCH.SMOLLM3:          "smollm3",
    MODEL_ARCH.GPT_OSS:          "gpt-oss",
    MODEL_ARCH.LFM2:             "lfm2",
    MODEL_ARCH.LFM2MOE:          "lfm2moe",
    MODEL_ARCH.DREAM:            "dream",
    MODEL_ARCH.SMALLTHINKER:     "smallthinker",
    MODEL_ARCH.LLADA:            "llada",
    MODEL_ARCH.LLADA_MOE:        "llada-moe",
    MODEL_ARCH.SEED_OSS:         "seed_oss",
    MODEL_ARCH.GROVEMOE:         "grovemoe",
    MODEL_ARCH.APERTUS:          "apertus",
    MODEL_ARCH.MINIMAXM2:        "minimax-m2",
    MODEL_ARCH.COGVLM:           "cogvlm",
}

VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
    VISION_PROJECTOR_TYPE.MLP:       "mlp",
    VISION_PROJECTOR_TYPE.LDP:       "ldp",
    VISION_PROJECTOR_TYPE.LDPV2:     "ldpv2",
    VISION_PROJECTOR_TYPE.RESAMPLER: "resampler",
    VISION_PROJECTOR_TYPE.GLM_EDGE:  "adapter",
    VISION_PROJECTOR_TYPE.MERGER:    "qwen2vl_merger",
    VISION_PROJECTOR_TYPE.GEMMA3:    "gemma3",
}

TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.TOKEN_EMBD:                "token_embd",
    MODEL_TENSOR.TOKEN_EMBD_NORM:           "token_embd_norm",
    MODEL_TENSOR.TOKEN_TYPES:               "token_types",
    MODEL_TENSOR.POS_EMBD:                  "position_embd",
    MODEL_TENSOR.OUTPUT_NORM:               "output_norm",
    MODEL_TENSOR.OUTPUT:                    "output",
    MODEL_TENSOR.DENSE_2_OUT:                "dense_2", # embeddinggemma 2_Dense
    MODEL_TENSOR.DENSE_3_OUT:                "dense_3", # embeddinggemma 2_Dense
    MODEL_TENSOR.ROPE_FREQS:                "rope_freqs",
    MODEL_TENSOR.ROPE_FACTORS_LONG:         "rope_factors_long",
    MODEL_TENSOR.ROPE_FACTORS_SHORT:        "rope_factors_short",
    MODEL_TENSOR.ATTN_NORM:                 "blk.{bid}.attn_norm",
    MODEL_TENSOR.ATTN_NORM_2:               "blk.{bid}.attn_norm_2",
    MODEL_TENSOR.ATTN_QKV:                  "blk.{bid}.attn_qkv",

Review comment (suggestion):
```suggestion
    MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
```

Code after:
    MODEL_ARCH.CHAMELEON:        "chameleon",
    MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
    MODEL_ARCH.PLM:              "plm",
    MODEL_ARCH.BAILINGMOE:       "bailingmoe",
    MODEL_ARCH.BAILINGMOE2:      "bailingmoe2",
    MODEL_ARCH.DOTS1:            "dots1",
    MODEL_ARCH.ARCEE:            "arcee",
    MODEL_ARCH.ERNIE4_5:         "ernie4_5",
    MODEL_ARCH.ERNIE4_5_MOE:     "ernie4_5-moe",
    MODEL_ARCH.FALCON_H1:        "falcon-h1",
    MODEL_ARCH.HUNYUAN_MOE:      "hunyuan-moe",
    MODEL_ARCH.HUNYUAN_DENSE:    "hunyuan-dense",
    MODEL_ARCH.SMOLLM3:          "smollm3",
    MODEL_ARCH.GPT_OSS:          "gpt-oss",
    MODEL_ARCH.LFM2:             "lfm2",
    MODEL_ARCH.LFM2MOE:          "lfm2moe",
    MODEL_ARCH.DREAM:            "dream",
    MODEL_ARCH.SMALLTHINKER:     "smallthinker",
    MODEL_ARCH.LLADA:            "llada",
    MODEL_ARCH.LLADA_MOE:        "llada-moe",
    MODEL_ARCH.SEED_OSS:         "seed_oss",
    MODEL_ARCH.GROVEMOE:         "grovemoe",
    MODEL_ARCH.APERTUS:          "apertus",
    MODEL_ARCH.MINIMAXM2:        "minimax-m2",
    MODEL_ARCH.COGVLM:           "cogvlm",
    MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
}

VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
    VISION_PROJECTOR_TYPE.MLP:       "mlp",
    VISION_PROJECTOR_TYPE.LDP:       "ldp",
    VISION_PROJECTOR_TYPE.LDPV2:     "ldpv2",
    VISION_PROJECTOR_TYPE.RESAMPLER: "resampler",
    VISION_PROJECTOR_TYPE.GLM_EDGE:  "adapter",
    VISION_PROJECTOR_TYPE.MERGER:    "qwen2vl_merger",
    VISION_PROJECTOR_TYPE.GEMMA3:    "gemma3",
}

TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.TOKEN_EMBD:                "token_embd",
    MODEL_TENSOR.TOKEN_EMBD_NORM:           "token_embd_norm",
    MODEL_TENSOR.TOKEN_TYPES:               "token_types",
    MODEL_TENSOR.POS_EMBD:                  "position_embd",
    MODEL_TENSOR.OUTPUT_NORM:               "output_norm",
    MODEL_TENSOR.OUTPUT:                    "output",
    MODEL_TENSOR.DENSE_2_OUT:                "dense_2", # embeddinggemma 2_Dense
    MODEL_TENSOR.DENSE_3_OUT:                "dense_3", # embeddinggemma 2_Dense
    MODEL_TENSOR.ROPE_FREQS:                "rope_freqs",
    MODEL_TENSOR.ROPE_FACTORS_LONG:         "rope_factors_long",
    MODEL_TENSOR.ROPE_FACTORS_SHORT:        "rope_factors_short",
    MODEL_TENSOR.ATTN_NORM:                 "blk.{bid}.attn_norm",