convert-*.py: add --get-outfile command and refactor

convert_hf_to_gguf.py

@@ -13,7 +13,8 @@ import sys
 from enum import IntEnum
 from pathlib import Path
 from hashlib import sha256
-from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast, Optional
+from dataclasses import dataclass
 
 import math
 import numpy as np
@@ -29,6 +30,42 @@ import gguf
 logger = logging.getLogger("hf-to-gguf")
 
 
+@dataclass
+class Metadata:
+    name: Optional[str] = None
+    author: Optional[str] = None
+    version: Optional[str] = None
+    url: Optional[str] = None
+    description: Optional[str] = None
+    licence: Optional[str] = None
+    source_url: Optional[str] = None
+    source_hf_repo: Optional[str] = None
+
+    @staticmethod
+    def load(metadata_path: Path) -> Metadata:
+        if metadata_path is None or not metadata_path.exists():
+            return Metadata()
+
+        with open(metadata_path, 'r') as file:
+            data = json.load(file)
+
+        # Create a new Metadata instance
+        metadata = Metadata()
+
+        # Assigning values to Metadata attributes if they exist in the JSON file
+        # This is based on LLM_KV_NAMES mapping in llama.cpp
+        metadata.name = data.get("general.name")
+        metadata.author = data.get("general.author")
+        metadata.version = data.get("general.version")
+        metadata.url = data.get("general.url")
+        metadata.description = data.get("general.description")
+        metadata.license = data.get("general.license")
+        metadata.source_url = data.get("general.source.url")
+        metadata.source_hf_repo = data.get("general.source.huggingface.repository")
+
+        return metadata
+
+
 ###### MODEL DEFINITIONS ######
 
 class SentencePieceTokenTypes(IntEnum):
@@ -46,13 +83,13 @@ AnyModel = TypeVar("AnyModel", bound="type[Model]")
 class Model:
     _model_classes: dict[str, type[Model]] = {}
 
+    model_name: str
     dir_model: Path
     ftype: gguf.LlamaFileType
     is_big_endian: bool
     endianess: gguf.GGUFEndian
     use_temp_file: bool
     lazy: bool
-    model_name: str | None
     part_names: list[str]
     is_safetensors: bool
     hparams: dict[str, Any]
@@ -60,12 +97,14 @@ class Model:
     tensor_map: gguf.TensorNameMap
     tensor_names: set[str] | None
     fname_out: Path
+    fname_default: Path
     gguf_writer: gguf.GGUFWriter
+    metadata: Metadata
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
 
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool,
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool, metadata: Metadata,
                  model_name: str | None, split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
         if type(self) is Model:
             raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
@@ -84,15 +123,20 @@ class Model:
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
         self.tensor_names = None
+        self.metadata = metadata
+
+        model_tensors = self.get_tensors()
+
         if self.ftype == gguf.LlamaFileType.GUESSED:
             # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
-            _, first_tensor = next(self.get_tensors())
+            _, first_tensor = next(model_tensors)
             if first_tensor.dtype == torch.float16:
                 logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})")
                 self.ftype = gguf.LlamaFileType.MOSTLY_F16
             else:
                 logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
                 self.ftype = gguf.LlamaFileType.MOSTLY_BF16
+
         ftype_up: str = self.ftype.name.partition("_")[2].upper()
         ftype_lw: str = ftype_up.lower()
         # allow templating the file name with the output ftype, useful with the "auto" ftype
@@ -100,6 +144,84 @@ class Model:
         self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
                                            split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
 
+        # Update any missing authorship metadata with huggingface_parameters
+        if self.metadata is not None and self.metadata.source_hf_repo is None:
+            if self.hparams is not None and "_name_or_path" in self.hparams:
+                self.metadata.source_hf_repo = self.hparams["_name_or_path"]
+
+        # Set model name based on latest metadata either provided or calculated from environment
+        def get_model_name(metadata, huggingface_parameters, dir_model, model_arch):
+            if metadata is not None and metadata.name is not None:
+                # Explicit Metadata Was Provided By User
+                return metadata.name
+            elif huggingface_parameters is not None and "_name_or_path" in huggingface_parameters:
+                # Hugging Face Parameters Model Name or Model Folder Name is Provided
+                return huggingface_parameters["_name_or_path"]
+            elif huggingface_parameters is not None and "model_type" in huggingface_parameters:
+                # Hugging Face Parameters Model Type is Provided
+                return huggingface_parameters["model_type"]
+            elif dir_model is not None and dir_model.name is not None:
+                # Use directory folder name
+                return dir_model.name
+            else:
+                return gguf.MODEL_ARCH_NAMES[model_arch]
+        self.model_name = get_model_name(self.metadata, self.hparams, self.dir_model, self.model_arch)
+
+        # Extracts and converts the encoding scheme from the given file type name. e.g. 'gguf.LlamaFileType.ALL_F32' --> 'F32'
+        encodingScheme = self.ftype.name.partition("_")[2]
+
+        # Get Expert Count From huggingface_parameters
+        expert_count = self.hparams["num_local_experts"] if "num_local_experts" in self.hparams else None
+
+        def per_model_weight_count_estimation(tensors, expert_count):
+            # TODO: Ensure parameter count is accurate throughout various model type
+            #       May currently overestimate parameter count in Mamba model because
+            #       output weights is tied with token embeddings.
+            sum_weight_estimate = 0
+            for name, data_torch in tensors:
+                # Got A Tensor
+
+                # We don't need these
+                if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+                    continue
+
+                # Calculate Tensor Volume
+                sum_weights_in_tensor = 1
+                for dim in data_torch.shape:
+                    sum_weights_in_tensor *= dim
+
+                # Add Tensor Volume To Running Count
+                sum_weight_estimate += sum_weights_in_tensor
+
+            # Calculate weight estimate per model
+            per_model_weight_estimate = (sum_weight_estimate / expert_count) if (expert_count > 0) else sum_weight_estimate
+
+            return per_model_weight_estimate
+
+        weight_estimate = per_model_weight_count_estimation(model_tensors, expert_count)
+
+        # Generate default filename based on model specification and available metadata
+        self.fname_default = gguf.naming_convention(self.model_name, self.metadata.version, expert_count, weight_estimate, encodingScheme)
+
+        # Filename Output
+        if fname_out is not None:
+            # custom defined filename and path was provided
+            def fill_templated_filename(filename: str, encodingScheme: str):
+                # Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf'
+                ftype_uppercase: str = encodingScheme.upper()
+                ftype_lowercase: str = encodingScheme.lower()
+                return filename.format(ftype_lowercase,
+                                       outtype=ftype_lowercase, ftype=ftype_lowercase,
+                                       OUTTYPE=ftype_uppercase, FTYPE=ftype_uppercase)
+
+            self.fname_out = fname_out.parent / fill_templated_filename(fname_out.name, encodingScheme)
+        else:
+            # output in the same directory as the model by default
+            self.fname_out = dir_model.parent / self.fname_default
+
+        # Configure GGUF Writer
+        self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
+
     @classmethod
     def __init_subclass__(cls):
         # can't use an abstract property, because overriding it without type errors
@@ -185,8 +307,26 @@ class Model:
             raise ValueError(f"Can not map tensor {name!r}")
         return new_name
 
+    def set_gguf_meta_model(self):
+        self.gguf_writer.add_name(self.model_name)
+
+        if self.metadata is not None:
+            if self.metadata.author is not None:
+                self.gguf_writer.add_author(self.metadata.author)
+            if self.metadata.version is not None:
+                self.gguf_writer.add_version(self.metadata.version)
+            if self.metadata.url is not None:
+                self.gguf_writer.add_url(self.metadata.url)
+            if self.metadata.description is not None:
+                self.gguf_writer.add_description(self.metadata.description)
+            if self.metadata.licence is not None:
+                self.gguf_writer.add_licence(self.metadata.licence)
+            if self.metadata.source_url is not None:
+                self.gguf_writer.add_source_url(self.metadata.source_url)
+            if self.metadata.source_hf_repo is not None:
+                self.gguf_writer.add_source_hf_repo(self.metadata.source_hf_repo)
+
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_block_count(self.block_count)
 
         if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
@@ -773,7 +913,6 @@ class GPTNeoXModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -829,7 +968,6 @@ class BloomModel(Model):
     model_arch = gguf.MODEL_ARCH.BLOOM
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Bloom")
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
@@ -906,7 +1044,6 @@ class MPTModel(Model):
 
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layers"]
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
         self.gguf_writer.add_embedding_length(self.hparams["d_model"])
         self.gguf_writer.add_block_count(block_count)
@@ -945,7 +1082,6 @@ class OrionModel(Model):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -958,8 +1094,6 @@ class OrionModel(Model):
             raise ValueError("gguf: can not find ctx length parameter.")
 
         self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -983,7 +1117,6 @@ class BaichuanModel(Model):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -995,8 +1128,6 @@ class BaichuanModel(Model):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1110,7 +1241,6 @@ class XverseModel(Model):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -1122,8 +1252,6 @@ class XverseModel(Model):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1182,7 +1310,6 @@ class FalconModel(Model):
         if n_head_kv is None:
             n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
 
-        self.gguf_writer.add_name("Falcon")
         self.gguf_writer.add_context_length(2048)  # not in config.json
         self.gguf_writer.add_tensor_data_layout("jploski")  # qkv tensor transform
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1227,7 +1354,6 @@ class StarCoderModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("StarCoder")
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
         self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
@@ -1262,7 +1388,6 @@ class RefactModel(Model):
 
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("Refact")
         # refact uses Alibi. So this is from config.json which might be used by training.
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -1317,7 +1442,6 @@ class StableLMModel(Model):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -1560,7 +1684,6 @@ class GrokModel(Model):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        self.gguf_writer.add_name("Grok")
 
     _experts: list[dict[str, Tensor]] | None = None
 
@@ -1609,7 +1732,6 @@ class DbrxModel(Model):
     def set_gguf_parameters(self):
         ffn_config = self.hparams["ffn_config"]
         attn_config = self.hparams["attn_config"]
-        self.gguf_writer.add_name(self.hparams["model_type"])
         self.gguf_writer.add_block_count(self.hparams["n_layers"])
 
         self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
@@ -1678,7 +1800,6 @@ class MiniCPMModel(Model):
 
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
-        self.gguf_writer.add_name("MiniCPM")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -1748,7 +1869,6 @@ class QwenModel(Model):
         self._set_vocab_qwen()
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Qwen")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1839,7 +1959,6 @@ class GPT2Model(Model):
     model_arch = gguf.MODEL_ARCH.GPT2
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
         self.gguf_writer.add_context_length(self.hparams["n_ctx"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -1882,7 +2001,6 @@ class Phi2Model(Model):
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
 
-        self.gguf_writer.add_name("Phi2")
         self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
 
         self.gguf_writer.add_embedding_length(n_embd)
@@ -2004,7 +2122,6 @@ class Phi3MiniModel(Model):
         orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
         rope_dims = n_embd // n_head
 
-        self.gguf_writer.add_name("Phi3")
         self.gguf_writer.add_context_length(max_pos_embds)
         self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
         self.gguf_writer.add_embedding_length(n_embd)
@@ -2061,7 +2178,6 @@ class PlamoModel(Model):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name("PLaMo")
         self.gguf_writer.add_context_length(4096)  # not in config.json
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
@@ -2106,7 +2222,6 @@ class CodeShellModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("CodeShell")
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
         self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
@@ -2265,7 +2380,6 @@ class InternLM2Model(Model):
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("InternLM2")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -2433,7 +2547,6 @@ class GemmaModel(Model):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -2549,7 +2662,6 @@ class MambaModel(Model):
         # Fail early for models which don't have a block expansion factor of 2
         assert d_inner == 2 * d_model
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
         self.gguf_writer.add_embedding_length(d_model)
         self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
@@ -3505,6 +3617,14 @@ def parse_args() -> argparse.Namespace:
         "--no-tensor-first-split", action="store_true",
         help="do not add tensors to the first split (disabled by default)"
     )
+    parser.add_argument(
+        "--metadata", type=Path,
+        help="Specify the path for a metadata file"
+    )
+    parser.add_argument(
+        "--get-outfile", action="store_true",
+        help="get calculated default outfile name"
+    )
 
     return parser.parse_args()
 
@@ -3530,8 +3650,15 @@ def split_str_to_n_bytes(split_str: str) -> int:
 def main() -> None:
     args = parse_args()
 
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+    if args.verbose:
+        logging.basicConfig(level=logging.DEBUG)
+    elif args.get_outfile:
+        # Avoid printing anything besides the dump output
+        logging.basicConfig(level=logging.WARNING)
+    else:
+        logging.basicConfig(level=logging.INFO)
 
+    metadata = Metadata.load(args.metadata)
     dir_model = args.model
 
     if not dir_model.is_dir():
@@ -3562,17 +3689,27 @@ def main() -> None:
     hparams = Model.load_hparams(dir_model)
 
     with torch.inference_mode():
+        encodingScheme = ftype_map[args.outtype]
+        model_architecture = hparams["architectures"][0]
+
         try:
-            model_class = Model.from_model_architecture(hparams["architectures"][0])
+            model_class = Model.from_model_architecture(model_architecture)
         except NotImplementedError:
             logger.error(f"Model {hparams['architectures'][0]} is not supported")
             sys.exit(1)
 
-        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file,
+        model_instance = model_class(dir_model, encodingScheme, fname_out, args.bigendian, args.use_temp_file,
                                      args.no_lazy, args.model_name, split_max_tensors=args.split_max_tensors,
                                      split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
                                      small_first_shard=args.no_tensor_first_split)
 
+        if args.get_outfile:
+            print(f"{model_instance.fname_default}") # noqa: NP100
+            return
+
+        logger.info("Set meta model")
+        model_instance.set_gguf_meta_model()
+
         logger.info("Set model parameters")
         model_instance.gguf_writer.add_type(gguf.GGUFType.MODEL)
         model_instance.set_gguf_parameters()

examples/convert_legacy_llama.py

@@ -1021,35 +1021,28 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT
     raise ValueError(f"Unexpected combination of types: {name_to_type}")
 
 
-def model_parameter_count(model: LazyModel) -> int:
-    total_model_parameters = 0
-    for i, (name, lazy_tensor) in enumerate(model.items()):
+def per_model_weight_count_estimation(model: LazyModel, expert_count:int) -> int:
+    # TODO: Ensure parameter count is accurate throughout various model type
+    sum_weight_estimate = 0
+    for name, lazy_tensor in model.items():
+        # We don't need these
+        if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+            continue
+
+        # Got A Tensor
         sum_weights_in_tensor = 1
+
+        # Tensor Volume
         for dim in lazy_tensor.shape:
             sum_weights_in_tensor *= dim
-        total_model_parameters += sum_weights_in_tensor
-    return total_model_parameters
 
+        # Add Tensor Volume To Running Count
+        sum_weight_estimate += sum_weights_in_tensor
 
-def model_parameter_count_rounded_notation(model_params_count: int) -> str:
-    if model_params_count > 1e12 :
-        # Trillions Of Parameters
-        scaled_model_params = model_params_count * 1e-12
-        scale_suffix = "T"
-    elif model_params_count > 1e9 :
-        # Billions Of Parameters
-        scaled_model_params = model_params_count * 1e-9
-        scale_suffix = "B"
-    elif model_params_count > 1e6 :
-        # Millions Of Parameters
-        scaled_model_params = model_params_count * 1e-6
-        scale_suffix = "M"
-    else:
-        # Thousands Of Parameters
-        scaled_model_params = model_params_count * 1e-3
-        scale_suffix = "K"
+    # Calculate weight estimate per model
+    per_model_weight_estimate = (sum_weight_estimate / expert_count) if (expert_count > 0) else sum_weight_estimate
 
-    return f"{round(scaled_model_params)}{scale_suffix}"
+    return per_model_weight_estimate
 
 
 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
@@ -1231,34 +1224,21 @@ class VocabFactory:
         return vocab, special_vocab
 
 
-def default_convention_outfile(file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> str:
-    quantization = {
+def default_convention_outfile(file_type: GGMLFileType, model_name:str, expert_count:int, model_params_count: int, metadata: Metadata) -> str:
+    name = metadata.name if metadata is not None and metadata.name is not None else model_name
+    version = metadata.version if metadata is not None and metadata.version is not None else None
+
+    encodingScheme = {
         GGMLFileType.AllF32:    "F32",
         GGMLFileType.MostlyF16: "F16",
         GGMLFileType.MostlyQ8_0: "Q8_0",
     }[file_type]
 
-    parameters = model_parameter_count_rounded_notation(model_params_count)
-
-    expert_count = ""
-    if params.n_experts is not None:
-        expert_count = f"{params.n_experts}x"
-
-    version = ""
-    if metadata is not None and metadata.version is not None:
-        version = f"-{metadata.version}"
-
-    name = "ggml-model"
-    if metadata is not None and metadata.name is not None:
-        name = metadata.name
-    elif params.path_model is not None:
-        name = params.path_model.name
-
-    return f"{name}{version}-{expert_count}{parameters}-{quantization}"
+    return gguf.naming_convention(name, version, expert_count, model_params_count, encodingScheme)
 
 
-def default_outfile(model_paths: list[Path], file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> Path:
-    default_filename = default_convention_outfile(file_type, params, model_params_count, metadata)
+def default_outfile(model_paths: list[Path], file_type: GGMLFileType, model_name:str, expert_count:int, model_params_count: int, metadata: Metadata) -> Path:
+    default_filename = default_convention_outfile(file_type, model_name, expert_count, model_params_count, metadata)
     ret = model_paths[0].parent / f"{default_filename}.gguf"
     if ret in model_paths:
         logger.error(
@@ -1315,10 +1295,10 @@ def main(args_in: list[str] | None = None) -> None:
     if args.get_outfile:
         model_plus = load_some_model(args.model)
         params = Params.load(model_plus)
-        model   = convert_model_names(model_plus.model, params, args.skip_unknown)
-        model_params_count = model_parameter_count(model_plus.model)
-        ftype   = pick_output_type(model, args.outtype)
-        print(f"{default_convention_outfile(ftype, params, model_params_count, metadata)}") # noqa: NP100
+        model = convert_model_names(model_plus.model, params, args.skip_unknown)
+        model_params_count = per_model_weight_count_estimation(model_plus.model, params.n_experts)
+        ftype = pick_output_type(model, args.outtype)
+        print(f"{default_convention_outfile(ftype, params.path_model.name, params.n_experts, model_params_count, metadata)}") # noqa: NP100
         return
 
     if args.no_vocab and args.vocab_only:
@@ -1334,8 +1314,8 @@ def main(args_in: list[str] | None = None) -> None:
     else:
         model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
 
-    model_params_count = model_parameter_count(model_plus.model)
-    logger.info(f"model parameters count : {model_params_count} ({model_parameter_count_rounded_notation(model_params_count)})")
+    model_params_count = per_model_weight_count_estimation(model_plus.model, params.n_experts)
+    logger.info(f"model parameters count : {model_params_count} ({gguf.model_weight_count_rounded_notation(model_params_count)})")
 
     if args.dump:
         do_dump_model(model_plus)
@@ -1405,7 +1385,7 @@ def main(args_in: list[str] | None = None) -> None:
     model   = convert_model_names(model, params, args.skip_unknown)
     ftype   = pick_output_type(model, args.outtype)
     model   = convert_to_output_type(model, ftype)
-    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params, model_params_count, metadata)
+    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params.path_model.name, params.n_experts, model_params_count, metadata)
 
     params.ftype = ftype
     logger.info(f"Writing {outfile}, format {ftype}")

gguf-py/gguf/__init__.py

@@ -5,3 +5,4 @@ from .gguf_writer import *
 from .quants import *
 from .tensor_mapping import *
 from .vocab import *
+from .utility import *

gguf-py/gguf/utility.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+
+def model_weight_count_rounded_notation(model_params_count: int) -> str:
+    if model_params_count > 1e15 :
+        # Quadrillion Of Parameters
+        scaled_model_params = model_params_count * 1e-15
+        scale_suffix = "Q"
+    elif model_params_count > 1e12 :
+        # Trillions Of Parameters
+        scaled_model_params = model_params_count * 1e-12
+        scale_suffix = "T"
+    elif model_params_count > 1e9 :
+        # Billions Of Parameters
+        scaled_model_params = model_params_count * 1e-9
+        scale_suffix = "B"
+    elif model_params_count > 1e6 :
+        # Millions Of Parameters
+        scaled_model_params = model_params_count * 1e-6
+        scale_suffix = "M"
+    else:
+        # Thousands Of Parameters
+        scaled_model_params = model_params_count * 1e-3
+        scale_suffix = "K"
+    return f"{round(scaled_model_params)}{scale_suffix}"
+
+
+def naming_convention(model_name: str, version_string:str, expert_count_int:int, model_params_count: int, encodingScheme: str) -> str:
+    # Reference: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#gguf-naming-convention
+    name = model_name.strip().replace(' ', '-') if model_name is not None else "ggml-model"
+    version = f"-{version_string}" if version_string is not None else ""
+    expert_count_chunk = f"{expert_count_int}x" if expert_count_int is not None and expert_count_int > 0 else ""
+    parameters = model_weight_count_rounded_notation(model_params_count)
+    encodingScheme = encodingScheme.upper()
+    return f"{name}{version}-{expert_count_chunk}{parameters}-{encodingScheme}"