Instructions to use hf-internal-testing/tiny-random-RwkvModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RwkvModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-RwkvModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RwkvModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-RwkvModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 701 Bytes
88a52c9 | 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 | {
"activation_function": "gelu",
"architectures": [
"RwkvModel"
],
"attention_hidden_size": 32,
"attn_pdrop": 0.1,
"bos_token_id": 0,
"context_length": 1024,
"eos_token_id": 0,
"gradient_checkpointing": false,
"hidden_size": 32,
"intermediate_size": 37,
"layer_norm_epsilon": 1e-05,
"model_type": "rwkv",
"n_positions": 512,
"num_hidden_layers": 5,
"pad_token_id": 1023,
"reorder_and_upcast_attn": false,
"rescale_every": 6,
"resid_pdrop": 0.1,
"scale_attn_by_inverse_layer_idx": false,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.30.0.dev0",
"type_vocab_size": 16,
"use_cache": true,
"vocab_size": 1024
}
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