Save llama-7b weights in the data folder docker run --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:sha-3c02262 --model-id /data/Llama-2-7b-chat-hf Expected behavior I would want the model to load and the API to start listening on the desi...
>>>fromoptimum.onnxruntimeimportORTModelForSequenceClassification>>>fromtransformersimportAutoTokenizer>>>model_checkpoint="distilbert_base_uncased_squad">>>save_directory="onnx/">>># Load a model from transformers and export it to ONNX>>>ort_model=ORTModelForSequenceClassification.from_pretrained(m...
trainer.save_model(cwd+"/finetuned_model") print("saved trainer locally") Run Code Online (Sandbox Code Playgroud) 以及到枢纽:model.push_to_hub("lucas0/empath-llama-7b", create_pr=1) Run Code Online (Sandbox Code Playgroud) 如何加载我的微调模型?
These modelshave an interesting feature. They run well on the cloud platform, but once you want to run them locally, you have to struggle. You can always see user feedback in the GitHub associated with the project: this model and code , I can't run it locally, it's too troublesome t...
使用transformer库需要两个部件:Tokenizer和model。 使用.from_pretrained(name)就可以下载Tokenizer和model。 一、 实例化Tokenizer和model: from transformersimport AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name ="distilbert-base-uncased-finetuned-sst-2-english" ...
1 Model classes 2 Configuration classes 3 Tokenizer classes All these classes can be instantiatedfrom pretrained instancesandsaved locally usingtwo methods: 1 from_pretrained() 允许您从库本身提供的预训练版本(支持的模型可以在模型中心找到)或用户本地(或服务器上)存储的预训练版本实例化模型/配置/标记器 ...
model_name, return_dict=True, torch_dtype=torch.float16, device_map="auto", ) model = PeftModel.from_pretrained(base_model, new_model) model = model.merge_and_unload() model.save_pretrained("the-final4") tokenizer.save_pretrained("the-final4")` and this is my Inference code: `# Fin...
we refrain from fine-tuning the model's identity. Thus, inquiries such as "Who are you" or "Who developed you" may yield random responses that are not necessarily accurate. If you enjoy our model, please give it a star on our Hugging Face repo and kindly cite our model. Your support ...
To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: How_to Run_Locally. For developers looking to dive deeper, we recommend exploring...
A demo is also available on the🤗 Hugging Face Space, and you can run the demo locally usingapp.pyin thedemofolder. (Thanks to all the HF team for their support) Here are some examples of how to use our model. 1) Code Completion ...