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[repo utils] Update models_to_deprecate.py (#41231)
* update models_to_deprecate * exclude this file * handle typos and aliases * don't commit files * PR suggestions; make fixup
This commit is contained in:
Joao Gante
GitHub
@ -12,7 +12,8 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Script to find a candidate list of models to deprecate based on the number of downloads and the date of the last commit.
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Script to find a candidate list of models to deprecate based on the number of downloads and the date of the last
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commit.
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"""
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import argparse
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@ -25,6 +26,9 @@ from pathlib import Path
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from git import Repo
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from huggingface_hub import HfApi
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from tqdm import tqdm
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from transformers.models.auto.configuration_auto import DEPRECATED_MODELS, MODEL_NAMES_MAPPING
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api = HfApi()
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@ -33,6 +37,97 @@ PATH_TO_REPO = Path(__file__).parent.parent.resolve()
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repo = Repo(PATH_TO_REPO)
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# Used when the folder name on the hub does not match the folder name in `transformers/models`
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# format = {folder name in `transformers/models`: expected tag on the hub}
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MODEL_FOLDER_NAME_TO_TAG_MAPPING = {
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"audio_spectrogram_transformer": "audio-spectrogram-transformer",
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"bert_generation": "bert-generation",
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"blenderbot_small": "blenderbot-small",
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"blip_2": "blip-2",
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"dab_detr": "dab-detr",
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"data2vec": "data2vec-audio", # actually, the base model is never used as a tag, but the sub models are
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"deberta_v2": "deberta-v2",
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"donut": "donut-swin",
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"encoder_decoder": "encoder-decoder",
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"grounding_dino": "grounding-dino",
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"kosmos2": "kosmos-2",
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"kosmos2_5": "kosmos-2.5",
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"megatron_bert": "megatron-bert",
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"mgp_str": "mgp-str",
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"mm_grounding_dino": "mm-grounding-dino",
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"modernbert_decoder": "modernbert-decoder",
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"nllb_moe": "nllb-moe",
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"omdet_turbo": "omdet-turbo",
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"openai": "openai-gpt",
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"roberta_prelayernorm": "roberta-prelayernorm",
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"sew_d": "sew-d",
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"speech_encoder_decoder": "speech-encoder-decoder",
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"table_transformer": "table-transformer",
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"unispeech_sat": "unispeech-sat",
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"vision_encoder_decoder": "vision-encoder-decoder",
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"vision_text_dual_encoder": "vision-text-dual-encoder",
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"wav2vec2_bert": "wav2vec2-bert",
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"wav2vec2_conformer": "wav2vec2-conformer",
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"x_clip": "xclip",
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"xlm_roberta": "xlm-roberta",
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"xlm_roberta_xl": "xlm-roberta-xl",
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}
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# Used on model architectures with multiple tags on the hub (e.g. on VLMs, we often support a text-only model).
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# Applied after the model folder name mapping. format = {base model tag: [extra tags]}
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EXTRA_TAGS_MAPPING = {
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"aimv2": ["aimv2_vision_model"],
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"aria": ["aria_text"],
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"bart": ["barthez", "bartpho"],
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"bert": ["bert-japanese", "bertweet", "herbert", "phobert"],
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"beit": ["dit"],
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"blip-2": ["blip_2_qformer"],
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"chinese_clip": ["chinese_clip_vision_model"],
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"clip": ["clip_text_model", "clip_vision_model"],
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"data2vec-audio": ["data2vec-text", "data2vec-vision"],
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"depth_anything": ["depth_anything_v2"],
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"donut-swin": ["nougat"],
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"edgetam": ["edgetam_vision_model"],
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"fastspeech2_conformer": ["fastspeech2_conformer_with_hifigan"],
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"gemma3": ["gemma3_text"],
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"gemma3n": ["gemma3n_audio", "gemma3n_text", "gemma3n_vision"],
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"gpt2": ["cpm", "dialogpt", "gpt-sw3", "megatron_gpt2"],
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"glm4v_moe": ["glm4v_moe_text"],
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"glm4v": ["glm4v_text"],
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"idefics3": ["idefics3_vision"],
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"internvl": ["internvl_vision"],
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"layoutlmv2": ["layoutxlm"],
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"llama": ["code_llama", "falcon3", "llama2", "llama3"],
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"llama4": ["llama4_text"],
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"llava_next": ["granitevision"],
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"luke": ["mluke"],
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"m2m_100": ["nllb"],
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"maskformer": ["maskformer-swin"],
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"mbart": ["mbart50"],
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"parakeet": ["parakeet_ctc", "parakeet_encoder"],
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"perception_lm": ["perception_encoder"],
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"pix2struct": ["deplot", "matcha"],
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"qwen2_5_vl": ["qwen2_5_vl_text"],
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"qwen2_audio": ["qwen2_audio_encoder"],
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"qwen2_vl": ["qwen2_vl_text"],
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"qwen3_vl_moe": ["qwen3_vl_moe_text"],
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"qwen3_vl": ["qwen3_vl_text"],
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"rt_detr": ["rt_detr_resnet"],
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"sam2": ["sam2_hiera_det_model", "sam2_vision_model"],
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"sam": ["sam_hq_vision_model", "sam_vision_model"],
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"siglip2": ["siglip2_vision_model"],
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"siglip": ["siglip_vision_model"],
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"smolvlm": ["smolvlm_vision"],
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"t5": ["byt5", "flan-t5", "flan-ul2", "madlad-400", "myt5", "t5v1.1", "ul2"],
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"voxtral": ["voxtral_encoder"],
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"wav2vec2": ["mms", "wav2vec2_phoneme", "xls_r", "xlsr_wav2vec2"],
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"xlm-roberta": ["xlm-v"],
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}
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# Similar to `DEPRECATED_MODELS`, but containing the tags when the model tag does not match the model folder name :'(
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DEPRECATED_MODELS_TAGS = {"gptsan-japanese", "open-llama", "transfo-xl", "xlm-prophetnet"}
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class HubModelLister:
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"""
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Utility for getting models from the hub based on tags. Handles errors without crashing the script.
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@ -40,7 +135,7 @@ class HubModelLister:
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def __init__(self, tags):
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self.tags = tags
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self.model_list = api.list_models(tags=tags)
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self.model_list = api.list_models(filter=tags)
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def __iter__(self):
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try:
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@ -97,9 +192,11 @@ def get_list_of_models_to_deprecate(
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info["first_commit_datetime"] = datetime.fromisoformat(info["first_commit_datetime"])
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else:
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# Build a dictionary of model info: first commit datetime, commit hash, model path
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print("Building a dictionary of basic model info...")
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models_info = defaultdict(dict)
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for model_path in model_paths:
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for i, model_path in enumerate(tqdm(sorted(model_paths))):
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if max_num_models != -1 and i > max_num_models:
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break
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model = model_path.split("/")[-2]
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if model in models_info:
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continue
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@ -111,12 +208,41 @@ def get_list_of_models_to_deprecate(
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models_info[model]["first_commit_datetime"] = committed_datetime
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models_info[model]["model_path"] = model_path
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models_info[model]["downloads"] = 0
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models_info[model]["tags"] = [model]
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# Some tags on the hub are formatted differently than in the library
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tags = [model]
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if "_" in model:
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tags.append(model.replace("_", "-"))
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models_info[model]["tags"] = tags
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# The keys in the dictionary above are the model folder names. In some cases, the model tag on the hub does not
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# match the model folder name. We replace the key and append the expected tag.
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for folder_name, expected_tag in MODEL_FOLDER_NAME_TO_TAG_MAPPING.items():
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if folder_name in models_info:
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models_info[expected_tag] = models_info[folder_name]
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models_info[expected_tag]["tags"] = [expected_tag]
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del models_info[folder_name]
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# Some models have multiple tags on the hub. We add the expected tag to the list of tags.
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for model_name, extra_tags in EXTRA_TAGS_MAPPING.items():
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if model_name in models_info:
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models_info[model_name]["tags"].extend(extra_tags)
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# Sanity check for the case with all models: the model tags must match the keys in the MODEL_NAMES_MAPPING
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# (= actual model tags on the hub)
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if max_num_models == -1:
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all_model_tags = set()
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for model_name in models_info:
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all_model_tags.update(models_info[model_name]["tags"])
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non_deprecated_model_tags = (
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set(MODEL_NAMES_MAPPING.keys()) - set(DEPRECATED_MODELS_TAGS) - set(DEPRECATED_MODELS)
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)
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if all_model_tags != non_deprecated_model_tags:
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raise ValueError(
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"The tags of the `models_info` dictionary must match the keys in the `MODEL_NAMES_MAPPING`!"
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"\nMissing tags in `model_info`: "
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+ str(sorted(non_deprecated_model_tags - all_model_tags))
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+ "\nExtra tags in `model_info`: "
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+ str(sorted(all_model_tags - non_deprecated_model_tags))
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+ "\n\nYou need to update one or more of the following: `MODEL_NAMES_MAPPING`, "
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"`EXTRA_TAGS_MAPPING` or `DEPRECATED_MODELS_TAGS`."
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)
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# Filter out models which were added less than a year ago
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models_info = {
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@ -124,19 +250,21 @@ def get_list_of_models_to_deprecate(
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}
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# We make successive calls to the hub, filtering based on the model tags
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n_seen = 0
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for model, model_info in models_info.items():
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print("Making calls to the hub to find models below the threshold number of downloads...")
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num_models = len(models_info)
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for i, (model, model_info) in enumerate(models_info.items()):
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print(f"{i + 1}/{num_models}: getting hub downloads for model='{model}' (tags={model_info['tags']})")
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for model_tag in model_info["tags"]:
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if model_info["downloads"] > thresh_num_downloads:
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break
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model_list = HubModelLister(tags=model_tag)
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for i, hub_model in enumerate(model_list):
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n_seen += 1
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if i % 100 == 0:
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print(f"Processing model {i} for tag {model_tag}")
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if max_num_models != -1 and i > n_seen:
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break
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for hub_model in model_list:
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if hub_model.private:
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continue
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model_info["downloads"] += hub_model.downloads
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# No need to make further hub calls, it's above the set threshold
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if model_info["downloads"] > thresh_num_downloads:
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break
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if save_model_info and not (use_cache and os.path.exists("models_info.json")):
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# Make datetimes serializable
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@ -156,7 +284,11 @@ def get_list_of_models_to_deprecate(
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print(f"\nModel: {model}")
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print(f"Downloads: {n_downloads}")
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print(f"Date: {info['first_commit_datetime']}")
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print("\nModels to deprecate: ", "\n" + "\n".join(models_to_deprecate.keys()))
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# sort models to deprecate by downloads (lowest downloads first)
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models_to_deprecate = sorted(models_to_deprecate.items(), key=lambda x: x[1]["downloads"])
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print("\nModels to deprecate: ", "\n" + "\n".join([model[0] for model in models_to_deprecate]))
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print(f"\nNumber of models to deprecate: {n_models_to_deprecate}")
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print("Before deprecating make sure to verify the models, including if they're used as a module in other models.")
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@ -171,19 +303,25 @@ if __name__ == "__main__":
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"--thresh_num_downloads",
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type=int,
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default=5_000,
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help="Threshold number of downloads below which a model should be deprecated. Default is 5,000.",
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help=(
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"Threshold number of downloads below which a model should be deprecated. Default is 5,000. If you are "
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"considering a sweep and using a cache, set this to the highest number of the sweep."
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),
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)
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parser.add_argument(
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"--thresh_date",
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type=str,
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default=None,
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help="Date to consider the first commit from. Format: YYYY-MM-DD. If unset, defaults to one year ago from today.",
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help=(
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"Date to consider the first commit from. Format: YYYY-MM-DD. If unset, defaults to one year ago from "
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"today."
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),
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)
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parser.add_argument(
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"--max_num_models",
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type=int,
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default=-1,
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help="Maximum number of models to consider from the hub. -1 means all models. Useful for testing.",
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help="Maximum number of models architectures to consider. -1 means all models. Useful for testing.",
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)
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args = parser.parse_args()
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