Instructions to use michaelbenayoun/llama-2-tiny-16layers-32kv-heads-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelbenayoun/llama-2-tiny-16layers-32kv-heads-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="michaelbenayoun/llama-2-tiny-16layers-32kv-heads-random")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("michaelbenayoun/llama-2-tiny-16layers-32kv-heads-random") model = AutoModel.from_pretrained("michaelbenayoun/llama-2-tiny-16layers-32kv-heads-random") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "michaelbenayoun/llama-2-tiny-16layers-random", | |
| "architectures": [ | |
| "LlamaModel" | |
| ], | |
| "attention_bias": false, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 32, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 32, | |
| "is_decoder": true, | |
| "max_position_embeddings": 4096, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 16, | |
| "num_key_value_heads": 32, | |
| "pad_token_id": 0, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.35.0", | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |