Transformers
Safetensors
PyTorch
aliceai_t5_moe
text2text-generation
encoder-decoder
mixture-of-experts
ul2
custom_code
Instructions to use yandex/AliceAI-T5-35B-A0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yandex/AliceAI-T5-35B-A0.6B with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("yandex/AliceAI-T5-35B-A0.6B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from .configuration_aliceai_t5 import AliceAIT5Config | |
| class AliceAIT5MoEConfig(AliceAIT5Config): | |
| model_type = "aliceai_t5_moe" | |
| def __init__( | |
| self, | |
| expert_intermediate_size: int = 768, | |
| n_experts: int = 256, | |
| activation_type: str = "silu", | |
| routed_scaling_factor: float = 1.0, | |
| experts_n_group: int = 8, | |
| experts_top_k_group: int = 4, | |
| moe_normalize_expert_weights: int | float | bool | None = True, | |
| experts_gating_top: int = 4, | |
| group_routing: bool = False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.expert_intermediate_size = expert_intermediate_size | |
| self.n_experts = n_experts | |
| self.activation_type = activation_type | |
| self.routed_scaling_factor = routed_scaling_factor | |
| self.experts_n_group = experts_n_group | |
| self.experts_top_k_group = experts_top_k_group | |
| self.moe_normalize_expert_weights = moe_normalize_expert_weights | |
| self.experts_gating_top = experts_gating_top | |
| self.group_routing = group_routing | |