Converter script can now read these two fields as a detailed base model and dataset source.
This was done so that it will be easier for Hugging Face to integrate detailed metadata as needed.
- base_model_sources (List[dict], optional)
- dataset_sources (List[dict], optional)
Dataset now represented as:
- general.dataset.count
- general.dataset.{id}.name
- general.dataset.{id}.author
- general.dataset.{id}.version
- general.dataset.{id}.organization
- general.dataset.{id}.description
- general.dataset.{id}.url
- general.dataset.{id}.doi
- general.dataset.{id}.uuid
- general.dataset.{id}.repo_url
This also adds to base model these metadata:
- general.base_model.{id}.description
self.gguf.add_base_model_version(key, base_model_entry["version"])
if "organization" in base_model_entry:
self.gguf.add_base_model_organization(key, base_model_entry["organization"])
+ if "description" in base_model_entry:
+ self.gguf.add_base_model_description(key, base_model_entry["description"])
if "url" in base_model_entry:
self.gguf.add_base_model_url(key, base_model_entry["url"])
if "doi" in base_model_entry:
if "repo_url" in base_model_entry:
self.gguf.add_base_model_repo_url(key, base_model_entry["repo_url"])
+ if metadata.datasets is not None:
+ self.gguf.add_dataset_count(len(metadata.datasets))
+ for key, dataset_entry in enumerate(metadata.datasets):
+ if "name" in dataset_entry:
+ self.gguf.add_dataset_name(key, dataset_entry["name"])
+ if "author" in dataset_entry:
+ self.gguf.add_dataset_author(key, dataset_entry["author"])
+ if "version" in dataset_entry:
+ self.gguf.add_dataset_version(key, dataset_entry["version"])
+ if "organization" in dataset_entry:
+ self.gguf.add_dataset_organization(key, dataset_entry["organization"])
+ if "description" in dataset_entry:
+ self.gguf.add_dataset_description(key, dataset_entry["description"])
+ if "url" in dataset_entry:
+ self.gguf.add_dataset_url(key, dataset_entry["url"])
+ if "doi" in dataset_entry:
+ self.gguf.add_dataset_doi(key, dataset_entry["doi"])
+ if "uuid" in dataset_entry:
+ self.gguf.add_dataset_uuid(key, dataset_entry["uuid"])
+ if "repo_url" in dataset_entry:
+ self.gguf.add_dataset_repo_url(key, dataset_entry["repo_url"])
+
if metadata.tags is not None:
self.gguf.add_tags(metadata.tags)
if metadata.languages is not None:
self.gguf.add_languages(metadata.languages)
- if metadata.datasets is not None:
- self.gguf.add_datasets(metadata.datasets)
def add_meta_arch(self, params: Params) -> None:
# Metadata About The Neural Architecture Itself
BASE_MODEL_AUTHOR = "general.base_model.{id}.author"
BASE_MODEL_VERSION = "general.base_model.{id}.version"
BASE_MODEL_ORGANIZATION = "general.base_model.{id}.organization"
+ BASE_MODEL_DESCRIPTION = "general.base_model.{id}.description"
BASE_MODEL_URL = "general.base_model.{id}.url" # Model Website/Paper
BASE_MODEL_DOI = "general.base_model.{id}.doi"
BASE_MODEL_UUID = "general.base_model.{id}.uuid"
BASE_MODEL_REPO_URL = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...)
+ # Dataset Source
+ DATASET_COUNT = "general.dataset.count"
+ DATASET_NAME = "general.dataset.{id}.name"
+ DATASET_AUTHOR = "general.dataset.{id}.author"
+ DATASET_VERSION = "general.dataset.{id}.version"
+ DATASET_ORGANIZATION = "general.dataset.{id}.organization"
+ DATASET_DESCRIPTION = "general.dataset.{id}.description"
+ DATASET_URL = "general.dataset.{id}.url" # Model Website/Paper
+ DATASET_DOI = "general.dataset.{id}.doi"
+ DATASET_UUID = "general.dataset.{id}.uuid"
+ DATASET_REPO_URL = "general.dataset.{id}.repo_url" # Model Source Repository (git/svn/etc...)
+
# Array based KV stores
TAGS = "general.tags"
LANGUAGES = "general.languages"
- DATASETS = "general.datasets"
class LLM:
VOCAB_SIZE = "{arch}.vocab_size"
def add_base_model_organization(self, source_id: int, organization: str) -> None:
self.add_string(Keys.General.BASE_MODEL_ORGANIZATION.format(id=source_id), organization)
+ def add_base_model_description(self, source_id: int, description: str) -> None:
+ self.add_string(Keys.General.BASE_MODEL_DESCRIPTION.format(id=source_id), description)
+
def add_base_model_url(self, source_id: int, url: str) -> None:
self.add_string(Keys.General.BASE_MODEL_URL.format(id=source_id), url)
def add_base_model_repo_url(self, source_id: int, repo_url: str) -> None:
self.add_string(Keys.General.BASE_MODEL_REPO_URL.format(id=source_id), repo_url)
+ def add_dataset_count(self, source_count: int) -> None:
+ self.add_uint32(Keys.General.DATASET_COUNT, source_count)
+
+ def add_dataset_name(self, source_id: int, name: str) -> None:
+ self.add_string(Keys.General.DATASET_NAME.format(id=source_id), name)
+
+ def add_dataset_author(self, source_id: int, author: str) -> None:
+ self.add_string(Keys.General.DATASET_AUTHOR.format(id=source_id), author)
+
+ def add_dataset_version(self, source_id: int, version: str) -> None:
+ self.add_string(Keys.General.DATASET_VERSION.format(id=source_id), version)
+
+ def add_dataset_organization(self, source_id: int, organization: str) -> None:
+ self.add_string(Keys.General.DATASET_ORGANIZATION.format(id=source_id), organization)
+
+ def add_dataset_description(self, source_id: int, description: str) -> None:
+ self.add_string(Keys.General.DATASET_DESCRIPTION.format(id=source_id), description)
+
+ def add_dataset_url(self, source_id: int, url: str) -> None:
+ self.add_string(Keys.General.DATASET_URL.format(id=source_id), url)
+
+ def add_dataset_doi(self, source_id: int, doi: str) -> None:
+ self.add_string(Keys.General.DATASET_DOI.format(id=source_id), doi)
+
+ def add_dataset_uuid(self, source_id: int, uuid: str) -> None:
+ self.add_string(Keys.General.DATASET_UUID.format(id=source_id), uuid)
+
+ def add_dataset_repo_url(self, source_id: int, repo_url: str) -> None:
+ self.add_string(Keys.General.DATASET_REPO_URL.format(id=source_id), repo_url)
+
def add_tags(self, tags: Sequence[str]) -> None:
self.add_array(Keys.General.TAGS, tags)
def add_languages(self, languages: Sequence[str]) -> None:
self.add_array(Keys.General.LANGUAGES, languages)
- def add_datasets(self, datasets: Sequence[str]) -> None:
- self.add_array(Keys.General.DATASETS, datasets)
-
def add_tensor_data_layout(self, layout: str) -> None:
self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
base_models: Optional[list[dict]] = None
tags: Optional[list[str]] = None
languages: Optional[list[str]] = None
- datasets: Optional[list[str]] = None
+ datasets: Optional[list[dict]] = 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:
# Base Models is received here as an array of models
metadata.base_models = metadata_override.get("general.base_models", metadata.base_models)
+ # Datasets is received here as an array of datasets
+ metadata.datasets = metadata_override.get("general.datasets", metadata.datasets)
+
metadata.tags = metadata_override.get(Keys.General.TAGS, metadata.tags)
metadata.languages = metadata_override.get(Keys.General.LANGUAGES, metadata.languages)
- metadata.datasets = metadata_override.get(Keys.General.DATASETS, metadata.datasets)
# Direct Metadata Override (via direct cli argument)
if model_name is not None:
use_model_card_metadata("author", "model_creator")
use_model_card_metadata("basename", "model_type")
- if "base_model" in model_card:
+ if "base_model" in model_card or "base_models" in model_card or "base_model_sources" in model_card:
# This represents the parent models that this is based on
# Example: stabilityai/stable-diffusion-xl-base-1.0. Can also be a list (for merges)
# Example of merges: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1/blob/main/README.md
metadata_base_models = []
- base_model_value = model_card.get("base_model", None)
+ base_model_value = model_card.get("base_model", model_card.get("base_models", model_card.get("base_model_sources", None)))
if base_model_value is not None:
if isinstance(base_model_value, str):
for model_id in metadata_base_models:
# NOTE: model size of base model is assumed to be similar to the size of the current model
- model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
base_model = {}
- if model_full_name_component is not None:
- base_model["name"] = Metadata.id_to_title(model_full_name_component)
- if org_component is not None:
- base_model["organization"] = Metadata.id_to_title(org_component)
- if version is not None:
- base_model["version"] = version
- if org_component is not None and model_full_name_component is not None:
- base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}"
+ if isinstance(model_id, str):
+ if model_id.startswith("http://") or model_id.startswith("https://") or model_id.startswith("ssh://"):
+ base_model["repo_url"] = model_id
+
+ # Check if Hugging Face ID is present in URL
+ if "huggingface.co" in model_id:
+ match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", model_id)
+ if match:
+ model_id_component = match.group(1)
+ model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id_component, total_params)
+
+ # Populate model dictionary with extracted components
+ if model_full_name_component is not None:
+ base_model["name"] = Metadata.id_to_title(model_full_name_component)
+ if org_component is not None:
+ base_model["organization"] = Metadata.id_to_title(org_component)
+ if version is not None:
+ base_model["version"] = version
+
+ else:
+ # Likely a Hugging Face ID
+ model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
+
+ # Populate model dictionary with extracted components
+ if model_full_name_component is not None:
+ base_model["name"] = Metadata.id_to_title(model_full_name_component)
+ if org_component is not None:
+ base_model["organization"] = Metadata.id_to_title(org_component)
+ if version is not None:
+ base_model["version"] = version
+ if org_component is not None and model_full_name_component is not None:
+ base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}"
+
+ elif isinstance(model_id, dict):
+ base_model = model_id
+
+ else:
+ logger.error(f"base model entry '{str(model_id)}' not in a known format")
+
metadata.base_models.append(base_model)
+ if "datasets" in model_card or "dataset" in model_card or "dataset_sources" in model_card:
+ # This represents the datasets that this was trained from
+ metadata_datasets = []
+ dataset_value = model_card.get("datasets", model_card.get("dataset", model_card.get("dataset_sources", None)))
+
+ if dataset_value is not None:
+ if isinstance(dataset_value, str):
+ metadata_datasets.append(dataset_value)
+ elif isinstance(dataset_value, list):
+ metadata_datasets.extend(dataset_value)
+
+ if metadata.datasets is None:
+ metadata.datasets = []
+
+ for dataset_id in metadata_datasets:
+ # NOTE: model size of base model is assumed to be similar to the size of the current model
+ dataset = {}
+ if isinstance(dataset_id, str):
+ if dataset_id.startswith(("http://", "https://", "ssh://")):
+ dataset["repo_url"] = dataset_id
+
+ # Check if Hugging Face ID is present in URL
+ if "huggingface.co" in dataset_id:
+ match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", dataset_id)
+ if match:
+ dataset_id_component = match.group(1)
+ dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id_component, total_params)
+
+ # Populate dataset dictionary with extracted components
+ if dataset_name_component is not None:
+ dataset["name"] = Metadata.id_to_title(dataset_name_component)
+ if org_component is not None:
+ dataset["organization"] = Metadata.id_to_title(org_component)
+ if version is not None:
+ dataset["version"] = version
+
+ else:
+ # Likely a Hugging Face ID
+ dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id, total_params)
+
+ # Populate dataset dictionary with extracted components
+ if dataset_name_component is not None:
+ dataset["name"] = Metadata.id_to_title(dataset_name_component)
+ if org_component is not None:
+ dataset["organization"] = Metadata.id_to_title(org_component)
+ if version is not None:
+ dataset["version"] = version
+ if org_component is not None and dataset_name_component is not None:
+ dataset["repo_url"] = f"https://huggingface.co/{org_component}/{dataset_name_component}"
+
+ elif isinstance(dataset_id, dict):
+ dataset = dataset_id
+
+ else:
+ logger.error(f"dataset entry '{str(dataset_id)}' not in a known format")
+
+ metadata.datasets.append(dataset)
+
use_model_card_metadata("license", "license")
use_model_card_metadata("license_name", "license_name")
use_model_card_metadata("license_link", "license_link")
use_array_model_card_metadata("languages", "languages")
use_array_model_card_metadata("languages", "language")
- use_array_model_card_metadata("datasets", "datasets")
- use_array_model_card_metadata("datasets", "dataset")
-
# Hugging Face Parameter Heuristics
####################################
gguf_writer.add_base_model_version(key, base_model_entry["version"])
if "organization" in base_model_entry:
gguf_writer.add_base_model_organization(key, base_model_entry["organization"])
+ if "description" in base_model_entry:
+ gguf_writer.add_base_model_description(key, base_model_entry["description"])
if "url" in base_model_entry:
gguf_writer.add_base_model_url(key, base_model_entry["url"])
if "doi" in base_model_entry:
if "repo_url" in base_model_entry:
gguf_writer.add_base_model_repo_url(key, base_model_entry["repo_url"])
+ if self.datasets is not None:
+ gguf_writer.add_dataset_count(len(self.datasets))
+ for key, dataset_entry in enumerate(self.datasets):
+ if "name" in dataset_entry:
+ gguf_writer.add_dataset_name(key, dataset_entry["name"])
+ if "author" in dataset_entry:
+ gguf_writer.add_dataset_author(key, dataset_entry["author"])
+ if "version" in dataset_entry:
+ gguf_writer.add_dataset_version(key, dataset_entry["version"])
+ if "organization" in dataset_entry:
+ gguf_writer.add_dataset_organization(key, dataset_entry["organization"])
+ if "description" in dataset_entry:
+ gguf_writer.add_dataset_description(key, dataset_entry["description"])
+ if "url" in dataset_entry:
+ gguf_writer.add_dataset_url(key, dataset_entry["url"])
+ if "doi" in dataset_entry:
+ gguf_writer.add_dataset_doi(key, dataset_entry["doi"])
+ if "uuid" in dataset_entry:
+ gguf_writer.add_dataset_uuid(key, dataset_entry["uuid"])
+ if "repo_url" in dataset_entry:
+ gguf_writer.add_dataset_repo_url(key, dataset_entry["repo_url"])
+
if self.tags is not None:
gguf_writer.add_tags(self.tags)
if self.languages is not None:
gguf_writer.add_languages(self.languages)
- if self.datasets is not None:
- gguf_writer.add_datasets(self.datasets)
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']
+ expect.datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]
+ self.assertEqual(got, expect)
+ # Base Model spec is inferred from model id
+ model_card = {'base_models': 'teknium/OpenHermes-2.5'}
+ expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
+ got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
+ self.assertEqual(got, expect)
+
+ # Base Model spec is only url
+ model_card = {'base_models': ['https://huggingface.co/teknium/OpenHermes-2.5']}
+ expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
+ got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
+ self.assertEqual(got, expect)
+
+ # Base Model spec is given directly
+ model_card = {'base_models': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]}
+ expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
+ got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
+ self.assertEqual(got, expect)
+
+ # Dataset spec is inferred from model id
+ model_card = {'datasets': 'teknium/OpenHermes-2.5'}
+ expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
+ got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
+ self.assertEqual(got, expect)
+
+ # Dataset spec is only url
+ model_card = {'datasets': ['https://huggingface.co/teknium/OpenHermes-2.5']}
+ expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
+ got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
+ self.assertEqual(got, expect)
+
+ # Dataset spec is given directly
+ model_card = {'datasets': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]}
+ expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
+ got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
self.assertEqual(got, expect)
def test_apply_metadata_heuristic_from_hf_parameters(self):
overview / pkg / ggml / sources / llama.cpp / commitdiff