Datasets:
itazap
/
transformers_code_embeddings_v3

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1
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24
102
embedding
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2.56k
2.56k
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4
448
cpmant/modeling_cpmant.py:CpmAntLayerNorm
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[ "ModelLayerNorm", "Module", "Parameter", "True", "__init__", "class", "config", "def", "dim", "dim_norm", "empty", "eps", "float32", "forward", "hidden_size", "hidden_states", "if", "keepdim", "mean", "nn", "pow", "return", "rsqrt", "self", "size", "super", "to", ...
cpmant/modeling_cpmant.py:CpmAntAttention
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[ "Dropout", "False", "Linear", "ModelAttention", "Module", "None", "Softmax", "__init__", "attention_mask", "attention_out", "attn_weights", "batch_size", "cache_position", "class", "config", "contiguous", "def", "device", "dim", "dim_head", "dim_model", "dropout", "dropou...
cpmant/modeling_cpmant.py:CpmAntSelfAttentionBlock
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cpmant/modeling_cpmant.py:CpmAntDenseGatedACT
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cpmant/modeling_cpmant.py:CpmAntFeedForward
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cpmant/modeling_cpmant.py:CpmAntFFNBlock
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cpmant/modeling_cpmant.py:CpmAntTransformerBlock
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[ "False", "ModelFFNBlock", "ModelSelfAttentionBlock", "ModelTransformerBlock", "Module", "None", "__init__", "attention_mask", "attn_weights", "cache_position", "class", "config", "def", "ffn", "forward", "hidden_states", "layer_idx", "nn", "output_attentions", "past_key_values"...
cpmant/modeling_cpmant.py:CpmAntEncoder
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[ "ModelEncoder", "ModelLayerNorm", "ModelTransformerBlock", "Module", "ModuleList", "None", "__init__", "all_hidden_states", "all_self_attns", "attention_mask", "attn_weights", "cache_position", "class", "config", "def", "else", "enumerate", "for", "forward", "hidden_states", ...
cpmant/modeling_cpmant.py:CpmAntIntermediate
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cpmant/modeling_cpmant.py:CpmAntSegmentPositionEmbedding
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[ "AssertionError", "F", "ModelSegmentPositionEmbedding", "Module", "None", "Parameter", "__init__", "_position_bucket", "_segment_relative_position_bucket", "abs", "absolute_position_bucket", "and", "arange", "batch", "be", "but", "class", "config", "contiguous", "def", "devic...
cpmant/modeling_cpmant.py:CpmAntOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "self", "su...
cpmant/modeling_cpmant.py:CpmAntPreTrainedModel
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cpmant/modeling_cpmant.py:CpmAntModel
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "ModelSegmentPositionEmbedding", "None", "__init__", "_prepare_attention_mask", "all_attentions", "all_hidden_states", "and", "arange", "attention", "attention_mask", "attent...
cpmant/modeling_cpmant.py:CpmAntForCausalLM
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[ "CausalLMOutputWithPast", "CrossEntropyLoss", "GenerationMixin", "Linear", "Model", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache_position", "class", "config", "def", "e...
mistral3/modeling_mistral3.py:Mistral3RMSNorm
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[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "class", "def", "eps", "extra_repr", "f", "float32", "forward", "hidden_size", "hidden_states", "keepdim", "mean", "nn", "ones", "pow", "return", "rsqrt", "self", "shape", "super", "to", "torch", "tuple", ...
mistral3/modeling_mistral3.py:Mistral3PatchMerger
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mistral3/modeling_mistral3.py:Mistral3MultiModalProjector
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[ "ACT2FN", "Linear", "ModelMultiModalProjector", "ModelPatchMerger", "ModelRMSNorm", "Module", "__init__", "act", "class", "config", "def", "else", "eps", "forward", "hidden_size", "hidden_states", "if", "image_features", "image_sizes", "int", "isinstance", "len", "linear_...
mistral3/modeling_mistral3.py:Mistral3CausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "logits", "loss", "past_key_values", "r" ]
mistral3/modeling_mistral3.py:Mistral3ModelOutputWithPast
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[ "BaseModelOutputWithPast", "ModelModelOutputWithPast", "None", "class", "image_hidden_states", "r" ]
mistral3/modeling_mistral3.py:Mistral3PreTrainedModel
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[ "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "base_model_prefix", "class", "config", "image", "input_modalities", ...
mistral3/modeling_mistral3.py:Mistral3Model
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mistral3/modeling_mistral3.py:Mistral3ForConditionalGeneration
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[ "False", "GenerationMixin", "Linear", "ModelCausalLMOutputWithPast", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_checkpoint_conversion_mapping", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache_posit...
persimmon/modeling_persimmon.py:PersimmonRotaryEmbedding
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persimmon/modeling_persimmon.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
persimmon/modeling_persimmon.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "cos", "def", "k", "k_embed", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze", "unsqueeze_dim" ]
persimmon/modeling_persimmon.py:PersimmonMLP
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persimmon/modeling_persimmon.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling",...
persimmon/modeling_persimmon.py:PersimmonAttention
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persimmon/modeling_persimmon.py:PersimmonDecoderLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "LayerNorm", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "None", "Tensor", "__init__", "attention_mask", "cache_position", "class", "config", "def", "dropout", "eps", "forward", "hidden_dropout", "hidden_size", "hidden_...
persimmon/modeling_persimmon.py:PersimmonPreTrainedModel
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[ "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_sdpa", "base_model_prefix", "class", "config", "model", ...
persimmon/modeling_persimmon.py:PersimmonModel
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[ "AttentionMaskConverter", "BaseModelOutputWithPast", "BlockMask", "DynamicCache", "Embedding", "False", "LayerNorm", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRotaryEmbedding", "ModuleList", "None", "Setting", "Tensor", "True", "ValueError", "You", "__init_...
persimmon/modeling_persimmon.py:PersimmonForCausalLM
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persimmon/modeling_persimmon.py:PersimmonForSequenceClassification
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persimmon/modeling_persimmon.py:PersimmonForTokenClassification
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sam3_video/modeling_sam3_video.py:_load_cv_utils_kernel_once
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sam3_video/modeling_sam3_video.py:Sam3VideoInferenceCache
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sam3_video/modeling_sam3_video.py:Sam3VideoInferenceSession
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sam3_video/modeling_sam3_video.py:Sam3VideoSegmentationOutput
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sam3_video/modeling_sam3_video.py:Sam3VideoPreTrainedModel
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sam3_video/modeling_sam3_video.py:Sam3VideoModel
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sam3_video/modeling_sam3_video.py:fast_diag_box_iou
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sam3_video/modeling_sam3_video.py:mask_iou
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sam3_video/modeling_sam3_video.py:nms_masks
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sam3_video/modeling_sam3_video.py:fill_holes_in_mask_scores
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sam3_video/modeling_sam3_video.py:_get_connected_components_with_padding
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autoformer/modeling_autoformer.py:AutoFormerDecoderOutput
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autoformer/modeling_autoformer.py:AutoformerModelOutput
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autoformer/modeling_autoformer.py:AutoformerFeatureEmbedder
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autoformer/modeling_autoformer.py:AutoformerStdScaler
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autoformer/modeling_autoformer.py:AutoformerMeanScaler
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autoformer/modeling_autoformer.py:AutoformerNOPScaler
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autoformer/modeling_autoformer.py:weighted_average
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autoformer/modeling_autoformer.py:nll
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autoformer/modeling_autoformer.py:AutoformerSinusoidalPositionalEmbedding
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autoformer/modeling_autoformer.py:AutoformerValueEmbedding
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autoformer/modeling_autoformer.py:AutoformerSeriesDecompositionLayer
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autoformer/modeling_autoformer.py:AutoformerLayernorm
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autoformer/modeling_autoformer.py:AutoformerAttention
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autoformer/modeling_autoformer.py:AutoformerEncoderLayer
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autoformer/modeling_autoformer.py:AutoformerDecoderLayer
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autoformer/modeling_autoformer.py:AutoformerPreTrainedModel
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autoformer/modeling_autoformer.py:AutoformerEncoder
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autoformer/modeling_autoformer.py:AutoformerDecoder
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autoformer/modeling_autoformer.py:AutoformerModel
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autoformer/modeling_autoformer.py:AutoformerForPrediction
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dia/modeling_dia.py:DiaPreTrainedModel
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dia/modeling_dia.py:DiaMultiChannelEmbedding
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dia/modeling_dia.py:DiaMLP
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dia/modeling_dia.py:DiaRMSNorm
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dia/modeling_dia.py:DiaRotaryEmbedding
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dia/modeling_dia.py:rotate_half
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dia/modeling_dia.py:apply_rotary_pos_emb
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dia/modeling_dia.py:repeat_kv
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dia/modeling_dia.py:eager_attention_forward
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dia/modeling_dia.py:DiaSelfAttention
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dia/modeling_dia.py:DiaCrossAttention
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dia/modeling_dia.py:DiaEncoderLayer
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dia/modeling_dia.py:DiaEncoder
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dia/modeling_dia.py:DiaDecoderLayer
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dia/modeling_dia.py:DiaDecoder
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dia/modeling_dia.py:DiaModel
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dia/modeling_dia.py:DiaForConditionalGeneration
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florence2/modeling_florence2.py:drop_path
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florence2/modeling_florence2.py:Florence2VisionDropPath
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florence2/modeling_florence2.py:Florence2VisionLearnedAbsolutePositionEmbedding2D
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florence2/modeling_florence2.py:Florence2VisionPositionalEmbeddingCosine1D
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florence2/modeling_florence2.py:Florence2VisionMLP
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florence2/modeling_florence2.py:Florence2VisionConvEmbed
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florence2/modeling_florence2.py:eager_attention_forward
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florence2/modeling_florence2.py:Florence2VisionChannelAttention
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florence2/modeling_florence2.py:Florence2VisionChannelBlock
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florence2/modeling_florence2.py:Florence2VisionWindowAttention
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florence2/modeling_florence2.py:Florence2VisionSpatialBlock
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florence2/modeling_florence2.py:Florence2VisionBlock
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florence2/modeling_florence2.py:Florence2VisionPreTrainedModel
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florence2/modeling_florence2.py:Florence2VisionBackbone
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florence2/modeling_florence2.py:Florence2MultiModalProjector
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florence2/modeling_florence2.py:Florence2Seq2SeqModelOutput
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[ "ModelSeq2SeqModelOutput", "None", "Seq2SeqModelOutput", "class", "image_hidden_states", "r" ]
florence2/modeling_florence2.py:Florence2Seq2SeqLMOutput
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[ "ModelSeq2SeqLMOutput", "None", "Seq2SeqLMOutput", "class", "image_hidden_states", "r" ]
florence2/modeling_florence2.py:Florence2PreTrainedModel
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