Instructions to use Tiiny/TurboSparse-Mistral-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiiny/TurboSparse-Mistral-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tiiny/TurboSparse-Mistral-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiiny/TurboSparse-Mistral-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/mnt/scratch/syx/checkpoint-500/checkpoint-500_bak", | |
| "architectures": [ | |
| "BambooForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_bamboo.BambooConfig", | |
| "AutoModel": "modeling_bamboo.BambooForCausalLM", | |
| "AutoModelForCausalLM": "modeling_bamboo.BambooForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "relu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 14336, | |
| "max_position_embeddings": 32768, | |
| "model_type": "bamboo", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "rms_norm_eps": 1e-05, | |
| "rope_theta": 10000.0, | |
| "sliding_window": 4096, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.41.0", | |
| "use_cache": false, | |
| "vocab_size": 32064 | |
| } | |