Instructions to use chomeed/robometer-4b-full-threading-d0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chomeed/robometer-4b-full-threading-d0 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, RFM processor = AutoProcessor.from_pretrained("chomeed/robometer-4b-full-threading-d0") model = RFM.from_pretrained("chomeed/robometer-4b-full-threading-d0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from chomeed/robometer-4b-full-threading-d0: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/chomeed/robometer-4b-full-threading-d0/resolve/main/training_args.bin
- Command line
-
hf download hf://chomeed/robometer-4b-full-threading-d0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/chomeed/robometer-4b-full-threading-d0/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- bc29ad209de423bd65496557b5afc9d7c16269042cc7e25ec31d2b1f0b22a17c
- Size of remote file:
- 5.84 kB
- SHA256:
- 146cefd13fd5d69db471454c874c947c9e8f345427d1dd56b1b7dc21d9e0acc4
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