Instructions to use hf-internal-testing/tiny-random-gptj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-gptj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-gptj")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gptj") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gptj", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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[Awaiting approval] Upload ONNX weights
Verified signature[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).c8f0335
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Update config.json (#1)
joaogante- Update config.json (a96e5bcc8606a397824ee30eb040b61bdac4c510)b96595a
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v3
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v2 update
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Tiny model: compatible with sequence classification!
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Tiny model: 1000 vocab size
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Tiny model
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initial commit
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