gguf-py : fix some metadata name extraction edge cases

convert_hf_to_gguf.py

@@ -62,6 +62,7 @@ class Model:
     gguf_writer: gguf.GGUFWriter
     model_name: str | None
     metadata_override: Path | None
+    dir_model_card: Path
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
@@ -90,6 +91,7 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path |
         self.tensor_names = None
         self.metadata_override = metadata_override
         self.model_name = model_name
+        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
 
         # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
         if self.ftype == gguf.LlamaFileType.GUESSED:
@@ -345,7 +347,7 @@ def prepare_metadata(self, vocab_only: bool):
 
         total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()
 
-        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model, self.model_name, total_params)
+        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model, self.model_name, self.dir_model_card, total_params)
 
         # Fallback to model directory name if metadata name is still missing
         if self.metadata.name is None:

convert_lora_to_gguf.py

@@ -304,12 +304,6 @@ def parse_args() -> argparse.Namespace:
     # load base model
     logger.info(f"Loading base model: {dir_base_model.name}")
     hparams = Model.load_hparams(dir_base_model)
-
-    with open(lora_config, "r") as f:
-        lparams: dict[str, Any] = json.load(f)
-
-    alpha: float = lparams["lora_alpha"]
-
     with torch.inference_mode():
         try:
             model_class = Model.from_model_architecture(hparams["architectures"][0])
@@ -320,12 +314,21 @@ def parse_args() -> argparse.Namespace:
         class LoraModel(model_class):
             model_arch = model_class.model_arch
 
+            lora_alpha: float
+
+            def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs):
+
+                super().__init__(*args, **kwargs)
+
+                self.dir_model_card = dir_lora_model
+                self.lora_alpha = float(lora_alpha)
+
             def set_type(self):
                 self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
                 self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
 
             def set_gguf_parameters(self):
-                self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, float(alpha))
+                self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
                 super().set_gguf_parameters()
 
             def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
@@ -368,6 +371,11 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
                     yield (dest_name + ".lora_a", lora_a)
                     yield (dest_name + ".lora_b", lora_b)
 
+        with open(lora_config, "r") as f:
+            lparams: dict[str, Any] = json.load(f)
+
+        alpha: float = lparams["lora_alpha"]
+
         model_instance = LoraModel(
             dir_base_model,
             ftype,
@@ -376,6 +384,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
             use_temp_file=False,
             eager=args.no_lazy,
             dry_run=args.dry_run,
+            dir_lora_model=dir_lora,
+            lora_alpha=alpha,
         )
 
         logger.info("Exporting model...")

gguf-py/gguf/metadata.py

@@ -44,19 +44,22 @@ class Metadata:
     datasets: Optional[list[str]] = None
 
     @staticmethod
-    def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata:
+    def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, model_card_path: Optional[Path] = None, total_params: int = 0) -> Metadata:
         # This grabs as many contextual authorship metadata as possible from the model repository
         # making any conversion as required to match the gguf kv store metadata format
         # as well as giving users the ability to override any authorship metadata that may be incorrect
 
         # Create a new Metadata instance
         metadata = Metadata()
 
-        model_card = Metadata.load_model_card(model_path)
+        if model_card_path is None:
+            model_card_path = model_path
+
+        model_card = Metadata.load_model_card(model_card_path)
         hf_params = Metadata.load_hf_parameters(model_path)
 
         # heuristics
-        metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params)
+        metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_card_path, total_params)
 
         # Metadata Override File Provided
         # This is based on LLM_KV_NAMES mapping in llama.cpp
@@ -177,6 +180,12 @@ def get_model_id_components(model_id: Optional[str] = None, total_params: int =
             org_component = None
 
         name_parts: list[str] = model_full_name_component.split('-')
+
+        # Remove empty parts
+        for i in reversed(range(len(name_parts))):
+            if len(name_parts[i]) == 0:
+                del name_parts[i]
+
         name_types: list[
             set[Literal["basename", "size_label", "finetune", "version", "type"]]
         ] = [set() for _ in name_parts]
@@ -227,6 +236,13 @@ def get_model_id_components(model_id: Optional[str] = None, total_params: int =
                 if part.lower() == "lora":
                     name_parts[i] = "LoRA"
 
+        # Ignore word-based size labels when there is at least a number-based one present
+        if any(c.isdecimal() for n, t in zip(name_parts, name_types) if "size_label" in t for c in n):
+            for n, t in zip(name_parts, name_types):
+                if "size_label" in t:
+                    if all(c.isalpha() for c in n):
+                        t.remove("size_label")
+
         at_start = True
         # Find the basename through the annotated name
         for part, t in zip(name_parts, name_types):
@@ -240,18 +256,18 @@ def get_model_id_components(model_id: Optional[str] = None, total_params: int =
 
         # Remove the basename annotation from trailing version
         for part, t in zip(reversed(name_parts), reversed(name_types)):
-            if "basename" in t:
-                if len(t) > 1:
-                    t.remove("basename")
+            if "basename" in t and len(t) > 1:
+                t.remove("basename")
             else:
                 break
 
         basename = "-".join(n for n, t in zip(name_parts, name_types) if "basename" in t) or None
-        size_label = "-".join(s for s, t in zip(name_parts, name_types) if "size_label" in t) or None
+        # Deduplicate size labels using order-preserving 'dict' ('set' seems to sort the keys)
+        size_label = "-".join(dict.fromkeys(s for s, t in zip(name_parts, name_types) if "size_label" in t).keys()) or None
         finetune = "-".join(f for f, t in zip(name_parts, name_types) if "finetune" in t) or None
         # TODO: should the basename version always be excluded?
-        # TODO: should multiple versions be joined together?
-        version = ([v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t] or [None])[-1]
+        # NOTE: multiple finetune versions are joined together
+        version = "-".join(v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t) or None
 
         if size_label is None and finetune is None and version is None:
             # Too ambiguous, output nothing

gguf-py/tests/test_metadata.py

@@ -54,7 +54,7 @@ def test_get_model_id_components(self):
         self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Meta-Llama-3-8B"),
                          ('Meta-Llama-3-8B', "NousResearch", 'Meta-Llama-3', None, None, '8B'))
 
-        # Can't detect all non standard form in a heuristically safe way... best to err in caution and output nothing...
+        # Non standard naming
         self.assertEqual(gguf.Metadata.get_model_id_components("Qwen1.5-MoE-A2.7B-Chat"),
                          ('Qwen1.5-MoE-A2.7B-Chat', None, 'Qwen1.5-MoE', 'Chat', None, 'A2.7B'))
 
@@ -71,7 +71,7 @@ def test_get_model_id_components(self):
         self.assertEqual(gguf.Metadata.get_model_id_components("delphi-suite/stories-llama2-50k", 50 * 10**3),
                          ('stories-llama2-50k', 'delphi-suite', 'stories-llama2', None, None, '50K'))
 
-        # None standard and not easy to disambiguate
+        # Non standard and not easy to disambiguate
         self.assertEqual(gguf.Metadata.get_model_id_components("DeepSeek-Coder-V2-Lite-Instruct"),
                          ('DeepSeek-Coder-V2-Lite-Instruct', None, 'DeepSeek-Coder-V2-Lite', 'Instruct', None, None))
 
@@ -123,6 +123,34 @@ def test_get_model_id_components(self):
         self.assertEqual(gguf.Metadata.get_model_id_components("bigscience/bloom-7b1-petals"),
                          ('bloom-7b1-petals', 'bigscience', 'bloom', 'petals', None, '7.1B'))
 
+        # Ignore full-text size labels when there are number-based ones, and deduplicate size labels
+        self.assertEqual(gguf.Metadata.get_model_id_components("MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1"),
+                         ('GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1', 'MaziyarPanahi', 'GreenNode-mini', 'multilingual-v1olet-Mistral-Instruct', 'v0.1', '7B'))
+
+        # Instruct in a name without a size label
+        self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/Mistral-Nemo-Instruct-2407"),
+                         ('Mistral-Nemo-Instruct-2407', 'mistralai', 'Mistral-Nemo', 'Instruct', '2407', None))
+
+        # Non-obvious splitting relying on 'chat' keyword
+        self.assertEqual(gguf.Metadata.get_model_id_components("deepseek-ai/DeepSeek-V2-Chat-0628"),
+                         ('DeepSeek-V2-Chat-0628', 'deepseek-ai', 'DeepSeek-V2', 'Chat', '0628', None))
+
+        # Multiple versions
+        self.assertEqual(gguf.Metadata.get_model_id_components("OpenGVLab/Mini-InternVL-Chat-2B-V1-5"),
+                         ('Mini-InternVL-Chat-2B-V1-5', 'OpenGVLab', 'Mini-InternVL', 'Chat', 'V1-5', '2B'))
+
+        # Too ambiguous
+        # TODO: should "base" be a 'finetune' or 'size_label'?
+        # (in this case it should be a size label, but other models use it to signal that they are not finetuned)
+        self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Florence-2-base"),
+                         ('Florence-2-base', 'microsoft', None, None, None, None))
+
+        ## Invalid cases ##
+
+        # Start with a dash and has dashes in rows
+        self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/-Mistral--Nemo-Base-2407-"),
+                         ('-Mistral--Nemo-Base-2407-', 'mistralai', 'Mistral-Nemo-Base', None, '2407', None))
+
     def test_apply_metadata_heuristic_from_model_card(self):
         model_card = {
             'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'],
@@ -134,7 +162,7 @@ def test_apply_metadata_heuristic_from_model_card(self):
         }
         got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
         expect = gguf.Metadata()
-        expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': 'v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
+        expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
         expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl']
         expect.languages=['en']
         expect.datasets=['teknium/OpenHermes-2.5']