Instructions to use dusersad12/ProdModel-Release with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/ProdModel-Release with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dusersad12/ProdModel-Release")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dusersad12/ProdModel-Release") model = AutoModel.from_pretrained("dusersad12/ProdModel-Release", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload best checkpoint (exp_alpha/step_2000, overall score 0.759) with model card and figures
6811da3 verified Download pytorch_model.bin from dusersad12/ProdModel-Release: direct link, hf CLI and curl.
- Browser
- Download file 27 Bytes
-
https://huggingface.co/dusersad12/ProdModel-Release/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dusersad12/ProdModel-Release/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dusersad12/ProdModel-Release/resolve/main/pytorch_model.bin
27 Bytes
- Xet hash:
- 9338553709acb5ddbd75dcdce5ab2080b7490f0aa538fc54bc63204f4712a424
- Size of remote file:
- 27 Bytes
- SHA256:
- 7fd02e9330cdd440961c4b5e3d5a9971e754e265a112713ff948d1f0d351ccfa
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