Example to download the modelhttps://huggingface.co/xai-org/grok-1(script code from the same repo) usingHuggingFace CLI: git clone https://github.com/xai-org/grok-1.git && cd grok-1 pip install huggingface_hub[hf_transfer] huggingface-cli download xai-org/grok-1 --repo-type model --...
For models with Parameter-Efficient Fine-Tuning (PEFT) adapters, you should first load the base model, and resize it as you did while training the model (as mentioned in the HuggingFace PEFT Troubleshooting Guide or see this notebook). As an example: from transformers impor...
and got satisfying results in inference, but when i try to use SFTTrainer.save_model, and load the model from the saved files using LlamaForCausalLM.from_pretrained, the inference result seem to just be of the not fine-tuned model
嘗試部署這類模型將會失敗,並顯示下列錯誤:ValueError: Loading <model> requires you to execute the configuration file in that repo on your local machine. Make sure you have read the code there to avoid malicious use, then set the option trust_remote_code=True to remove this error. 具有不正確...
4.Install the libraries needed to run PyTorch pip install torchvision torchaudio torch--index-url https://download.pytorch.org/whl/cu118 5.Run the following python code: fromtransformersimportAutoModelForCausalLM,AutoTokenizer# Load the model and tokenizermodel...
model = AutoModel.from_pretrained(r'/Users/maxiong/Workpace/Code/transformers/pre_model/bert_model.ckpt.index', from_tf=True, config=config) when I used TFAutoModel to load a model, there is like this model = TFAutoModel.from_pretrained(r'/Users/maxiong/Workpace/Code/transformers/pre_...
下表摘要說明每一個電腦視覺工作支援的舊版模型。 僅使用這些舊版模型,會使用舊版執行階段觸發執行 (其中每個個別執行或試用都會提交為命令作業)。 如需 HuggingFace 和 MMDetection 支援,請參閱下方。展開資料表 Task模型結構字串常值語法 以* 表示的 default_model* 影像分類 (多類別和多標籤) MobileNet:行動...
カタログで示されるモデルの一覧は、HuggingFaceレジストリから表示されます。 この例では、最新バージョンのbert_base_uncasedモデルをデプロイします。 モデル名とレジストリに基づく完全修飾model資産 ID はazureml://registries/HuggingFace/models/bert-base-uncased/labels/latestです。az ml onlin...
Also, we would use the Alpaca sample dataset fromHuggingFace, which required datasets package to acquire. pip install datasets Then, use the following code to acquire the data we need. from datasets import load_dataset # Load the dataset ...
而且如果想偷懒用HuggingFace集成的DeepSpeed做Model Parallelism,目前需要做的tricks还非常多,TP(张量并行) + PP(管道并行) + ZeRO-3(零冗余优化器) + 一堆骚操作之后,T5-11B确实是可以在4 * A100-40G上跑起来,但是根本不收敛是怎么回事?我怀疑是我哪里搞错了,但是真的太复杂了,我一点都不想再来一遍了_(:...