Instructions to use xiaotinghe/buffer-embedding-002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaotinghe/buffer-embedding-002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="xiaotinghe/buffer-embedding-002", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("xiaotinghe/buffer-embedding-002", trust_remote_code=True) model = AutoModel.from_pretrained("xiaotinghe/buffer-embedding-002", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
| from transformers.utils import logging | |
| from transformers.configuration_utils import PretrainedConfig | |
| logger = logging.get_logger(__name__) | |
| INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {} | |
| class BufferEmbeddingConfig(PretrainedConfig): | |
| model_type = "buffer_embedding" | |
| _auto_class = "AutoConfig" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| attribute_map = { | |
| "num_hidden_layers": "n_layer", | |
| "num_attention_heads": "n_head", | |
| } | |
| def __init__( | |
| self, | |
| vocab_size=250880, | |
| hidden_size=64, | |
| n_layer=2, | |
| n_head=8, | |
| layer_norm_epsilon=1e-5, | |
| initializer_range=0.02, | |
| use_cache=True, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| apply_residual_connection_post_layernorm=False, | |
| hidden_dropout=0.0, | |
| attention_dropout=0.0, | |
| pretraining_tp=1, # TP rank used when training with megatron | |
| slow_but_exact=False, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| # Backward compatibility with n_embed kwarg | |
| n_embed = kwargs.pop("n_embed", None) | |
| self.hidden_size = hidden_size if n_embed is None else n_embed | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.initializer_range = initializer_range | |
| self.use_cache = use_cache | |
| self.pretraining_tp = pretraining_tp | |
| self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm | |
| self.hidden_dropout = hidden_dropout | |
| self.attention_dropout = attention_dropout | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| self.slow_but_exact = slow_but_exact | |
| super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |