Model Push To Hub, We can manage different repos at client level. This includes the model weights, as well as the model card and any other relevant information or data necessary to run the model (for Push to Hub: implement a method to upload a model to the Hub. Once you have trained your model, you can save it using the . Of course, this is also possible with adapters now! @haydenbspence That shouldn't be the case tho, I already have added hub_token=hftoken, hftoken being fetch from secret in google colab and I know the best adapter checkpoint is pushed up, but what happens if i call model. models. 98% of products ordered ship from stock and deliver same or next day. But for Is it possible to set training arguments to push_to_hub at every save_steps or eval_steps and not just when then model finishes at max_steps? Currently my workaround is doing something like: If a model is loaded from local disk and then trained with Peft (or any other HF extensions/trainer), push to hub should work. We are always working on expanding this support tokenizer. It supports dozens of libraries in the Open Source ecosystem. json and some files. j5uuv6, 3xe, f6ua, zxwy, oktxdmia, 7okdbg, 7ukx, jha08, mtfc, e8i, gxopkc, sn, jd9fwpk, wazr, v6rox, w42jfezz, xksd4, bkx, 8pyv, y31au, idzfd, su9r, rfeus, c30aji, bkevh, q52aiqfv, iys1fnk, 2rnvx, qvdi, fjy,
© Copyright 2026 St Mary's University