Instructions to use hf-internal-testing/tiny-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-llama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-llama")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-llama") model = AutoModel.from_pretrained("hf-internal-testing/tiny-llama") - Notebooks
- Google Colab
- Kaggle
File size: 714 Bytes
c5c83da | 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 | {
"architectures": [
"LlamaModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "float32",
"eos_token_id": 2,
"head_dim": 3,
"hidden_act": "silu",
"hidden_size": 12,
"initializer_range": 0.02,
"intermediate_size": 12,
"max_position_embeddings": 2048,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"num_key_value_heads": 4,
"pad_token_id": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"tie_word_embeddings": false,
"transformers_version": "5.13.0.dev0",
"use_cache": true,
"vocab_size": 99
}
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