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")# 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") - Notebooks
- Google Colab
- Kaggle
File size: 807 Bytes
d95a0e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"activation_function": "gelu_new",
"architectures": [
"GPTJModel"
],
"attention_probs_dropout_prob": 0.0,
"attn_pdrop": 0.0,
"bos_token_id": 98,
"embd_pdrop": 0.0,
"eos_token_id": 98,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_epsilon": 1e-05,
"model_type": "gptj",
"n_ctx": 512,
"n_embd": 32,
"n_head": 4,
"n_inner": null,
"n_layer": 5,
"n_positions": 512,
"pad_token_id": 98,
"resid_pdrop": 0.0,
"rotary_dim": 4,
"scale_attn_weights": true,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.52.0.dev0",
"type_vocab_size": 16,
"use_cache": true,
"vocab_size": 1000
}
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