Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
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24
117
embedding
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2.56k
2.56k
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4
444
llava_next/modeling_llava_next.py:get_anyres_image_grid_shape
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[ "Model_anyres_image_grid_shape", "Tensor", "TypeError", "a", "be", "def", "either", "f", "grid_pinpoints", "height", "if", "image_size", "invalid", "isinstance", "list", "lists", "ndarray", "not", "np", "of", "or", "patch_size", "raise", "return", "select_best_resolut...
llava_next/modeling_llava_next.py:image_size_to_num_patches
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[ "Model_size", "Model_size_to_num_patches", "Tensor", "TypeError", "a", "be", "best_resolution", "def", "f", "for", "grid_pinpoints", "height", "i", "if", "in", "invalid", "isinstance", "j", "list", "lists", "ndarray", "not", "np", "num_patches", "of", "or", "patch...
llava_next/modeling_llava_next.py:unpad_image
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[ "Model_image", "Modelded_tensor", "Tensor", "TypeError", "be", "current_aspect_ratio", "current_height", "current_width", "def", "either", "else", "f", "if", "image_size", "int", "invalid", "isinstance", "list", "ndarray", "new_height", "new_width", "not", "np", "or", ...
llava_next/modeling_llava_next.py:LlavaNextModelOutputWithPast
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[ "BaseModelOutputWithPast", "ModelModelOutputWithPast", "None", "class", "image_hidden_states", "r" ]
llava_next/modeling_llava_next.py:LlavaNextCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "logits", "loss", "past_key_values", "r" ]
llava_next/modeling_llava_next.py:LlavaNextMultiModalProjector
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[ "ACT2FN", "Linear", "ModelMultiModalProjector", "Module", "__init__", "act", "class", "config", "def", "else", "forward", "hidden_size", "hidden_states", "if", "image_features", "int", "isinstance", "len", "linear_1", "linear_2", "nn", "num_feature_layers", "projector_hid...
llava_next/modeling_llava_next.py:LlavaNextPreTrainedModel
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[ "LlamaDecoderLayer", "ModelConfig", "ModelModel", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_init_weights", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa"...
llava_next/modeling_llava_next.py:LlavaNextModel
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[ "AutoModel", "CLS", "Image", "ModelModel", "ModelModelOutputWithPast", "ModelMultiModalProjector", "ModelPreTrainedModel", "None", "Obtains", "Parameter", "True", "ValueError", "You", "__init__", "_pixel_values_list", "a", "all", "and", "append", "apply", "attention_mask", ...
llava_next/modeling_llava_next.py:LlavaNextForConditionalGeneration
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[ "GenerationMixin", "Linear", "ModelCausalLMOutputWithPast", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "config", "def", "else...
musicflamingo/modeling_musicflamingo.py:MusicFlamingoRotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "_compute_position_angles", "angle", "arange", "attention_factor", "attention_scaling", "audio_frame_step", "base", "broadcast_tensors", "cat", "class", "clone", "compute_default_rope_...
musicflamingo/modeling_musicflamingo.py:MusicFlamingoPreTrainedModel
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[ "ModelConfig", "ModelPreTrainedModel", "ModelRotaryEmbedding", "None", "PreTrainedModel", "True", "_compute_position_angles", "_init_weights", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_sdpa", "audio", "base_model...
musicflamingo/modeling_musicflamingo.py:MusicFlamingoModelOutputWithPast
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[ "BaseModelOutputWithPast", "ModelModelOutputWithPast", "None", "audio_hidden_states", "class", "r" ]
musicflamingo/modeling_musicflamingo.py:MusicFlamingoMultiModalProjector
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[ "ACT2FN", "Linear", "ModelMultiModalProjector", "Module", "__init__", "act", "audio_config", "audio_features", "class", "config", "def", "forward", "hidden_size", "hidden_states", "linear_1", "linear_2", "nn", "projector_hidden_act", "return", "self", "super", "text_config"...
musicflamingo/modeling_musicflamingo.py:rotate_half
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[ "Model_half", "def", "dim", "flatten", "reshape", "return", "shape", "stack", "torch", "unbind", "x", "x1", "x2" ]
musicflamingo/modeling_musicflamingo.py:apply_rotary_time_emb
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[ "Model_rotary_time_emb", "cat", "cos", "def", "dim", "float64", "hidden_states", "passthrough", "return", "rot_dim", "rotate_half", "rotated", "shape", "sin", "to", "torch" ]
musicflamingo/modeling_musicflamingo.py:MusicFlamingoModel
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[ "Audio", "AutoModel", "ModelModel", "ModelModelOutputWithPast", "ModelMultiModalProjector", "ModelPreTrainedModel", "ModelRotaryEmbedding", "None", "This", "True", "_", "__init__", "_build_audio_timestamps", "_get_feat_extract_output_lengths", "_keep_in_fp32_modules_strict", "_pp_plan"...
musicflamingo/modeling_musicflamingo.py:MusicFlamingoCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "audio_hidden_states", "class", "hidden_states", "logits", "loss", "past_key_values", "r" ]
musicflamingo/modeling_musicflamingo.py:MusicFlamingoForConditionalGeneration
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[ "GenerationMixin", "Linear", "ModelCausalLMOutputWithPast", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "audio_hidden_states", "auto_docstring", "can_return_tuple", "class", "config", ...
vaultgemma/modeling_vaultgemma.py:VaultGemmaRMSNorm
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[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "_norm", "class", "def", "dim", "eps", "extra_repr", "f", "float", "forward", "keepdim", "mean", "nn", "output", "pow", "return", "rsqrt", "self", "shape", "super", "torch", "tuple", "type_as", "weight",...
vaultgemma/modeling_vaultgemma.py:VaultGemmaMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "class", "config", "def", "down_proj", "forward", "gate_proj", "hidden_activation", "hidden_size", "intermediate_size", "nn", "return", "self", "super", "up_proj", "x" ]
vaultgemma/modeling_vaultgemma.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
vaultgemma/modeling_vaultgemma.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" ]
vaultgemma/modeling_vaultgemma.py:repeat_kv
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[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
vaultgemma/modeling_vaultgemma.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "head_dim", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", "...
vaultgemma/modeling_vaultgemma.py:VaultGemmaAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_logit_softcapping", "attn_output", "attn_weights", "class", "co...
vaultgemma/modeling_vaultgemma.py:VaultGemmaDecoderLayer
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[ "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "Tensor", "_", "__init__", "attention_mask", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "input_layernorm", "kwargs", "layer_idx", "mlp", ...
vaultgemma/modeling_vaultgemma.py:VaultGemmaRotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "and", "arange", "attention_factor", "attention_scaling", "base", "cat", "class", "clone", "compute_default_rope_parameters", "config", "cos", "cpu", "def", "default", "deprecate...
vaultgemma/modeling_vaultgemma.py:VaultGemmaTextScaledWordEmbedding
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vaultgemma/modeling_vaultgemma.py:VaultGemmaPreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelTextScaledWordEmbedding", "PreTrainedModel", "RMSNorm", "True", "__class__", "__name__", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modules", "_skip_keys_device_placement...
vaultgemma/modeling_vaultgemma.py:VaultGemmaModel
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[ "BaseModelOutputWithPast", "DynamicCache", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModelTextScaledWordEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_doc...
vaultgemma/modeling_vaultgemma.py:VaultGemmaForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_fsdp_plan", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "colwise_g...
cvt/modeling_cvt.py:BaseModelOutputWithCLSToken
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[ "ModelModelOutputWithCLSToken", "ModelOutput", "None", "class", "cls_token_value", "hidden_states", "last_hidden_state", "r" ]
cvt/modeling_cvt.py:CvtEmbeddings
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[ "Dropout", "ModelConvEmbeddings", "ModelEmbeddings", "Module", "__init__", "class", "convolution_embeddings", "def", "dropout", "dropout_rate", "embed_dim", "forward", "hidden_state", "nn", "num_channels", "padding", "patch_size", "pixel_values", "return", "self", "stride", ...
cvt/modeling_cvt.py:CvtConvEmbeddings
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[ "Conv2d", "Iterable", "LayerNorm", "ModelConvEmbeddings", "Module", "__init__", "abc", "batch_size", "class", "collections", "def", "else", "embed_dim", "forward", "height", "hidden_size", "if", "isinstance", "kernel_size", "nn", "normalization", "num_channels", "padding"...
cvt/modeling_cvt.py:CvtSelfAttentionConvProjection
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[ "BatchNorm2d", "Conv2d", "ModelSelfAttentionConvProjection", "Module", "__init__", "class", "convolution", "def", "embed_dim", "forward", "groups", "hidden_state", "kernel_size", "nn", "normalization", "padding", "return", "self", "stride", "super" ]
cvt/modeling_cvt.py:CvtSelfAttentionLinearProjection
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[ "ModelSelfAttentionLinearProjection", "Module", "batch_size", "class", "def", "forward", "height", "hidden_size", "hidden_state", "nn", "num_channels", "permute", "return", "self", "shape", "view", "width" ]
cvt/modeling_cvt.py:CvtSelfAttentionProjection
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[ "ModelSelfAttentionConvProjection", "ModelSelfAttentionLinearProjection", "ModelSelfAttentionProjection", "Module", "__init__", "class", "convolution_projection", "def", "dw_bn", "embed_dim", "forward", "hidden_state", "if", "kernel_size", "linear_projection", "nn", "padding", "pro...
cvt/modeling_cvt.py:CvtSelfAttention
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[ "Dropout", "Linear", "ModelSelfAttention", "ModelSelfAttentionProjection", "Module", "True", "_", "__init__", "attention_drop_rate", "attention_probs", "attention_score", "avg", "batch_size", "bhlk", "bhlt", "bhlv", "bhtk", "bhtv", "cat", "class", "cls_token", "context", ...
cvt/modeling_cvt.py:CvtSelfOutput
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[ "Dropout", "Linear", "ModelSelfOutput", "Module", "__init__", "class", "def", "dense", "drop_rate", "dropout", "embed_dim", "forward", "hidden_state", "input_tensor", "nn", "return", "self", "super" ]
cvt/modeling_cvt.py:CvtAttention
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[ "ModelAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "True", "__init__", "attention", "attention_drop_rate", "attention_output", "class", "def", "drop_rate", "embed_dim", "forward", "height", "hidden_state", "kernel_size", "nn", "num_heads", "output", "padding...
cvt/modeling_cvt.py:CvtIntermediate
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[ "GELU", "Linear", "ModelIntermediate", "Module", "__init__", "activation", "class", "def", "dense", "embed_dim", "forward", "hidden_state", "int", "mlp_ratio", "nn", "return", "self", "super" ]
cvt/modeling_cvt.py:CvtOutput
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[ "Dropout", "Linear", "ModelOutput", "Module", "__init__", "class", "def", "dense", "drop_rate", "dropout", "embed_dim", "forward", "hidden_state", "input_tensor", "int", "mlp_ratio", "nn", "return", "self", "super" ]
cvt/modeling_cvt.py:CvtDropPath
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[ "ModelDropPath", "Module", "__init__", "class", "def", "device", "div", "drop_prob", "dtype", "extra_repr", "f", "floor", "forward", "hidden_states", "if", "keep_prob", "ndim", "nn", "not", "or", "p", "rand", "random_tensor", "return", "self", "shape", "super", ...
cvt/modeling_cvt.py:CvtLayer
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[ "Identity", "LayerNorm", "ModelAttention", "ModelDropPath", "ModelIntermediate", "ModelLayer", "ModelOutput", "Module", "True", "__init__", "attention", "attention_drop_rate", "attention_output", "class", "def", "drop_path", "drop_path_rate", "drop_rate", "else", "embed_dim", ...
cvt/modeling_cvt.py:CvtStage
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[ "ModelEmbeddings", "ModelLayer", "ModelStage", "Module", "None", "Parameter", "Sequential", "_", "__init__", "attention_drop_rate", "batch_size", "cat", "class", "cls_token", "config", "cpu", "def", "depth", "device", "dim", "drop_path_rate", "drop_path_rates", "drop_rate...
cvt/modeling_cvt.py:CvtEncoder
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[ "BaseModelOutputWithCLSToken", "False", "ModelEncoder", "ModelStage", "Module", "ModuleList", "None", "True", "_", "__init__", "all_hidden_states", "append", "class", "cls_token", "cls_token_value", "config", "def", "depth", "else", "enumerate", "for", "forward", "hidden_...
cvt/modeling_cvt.py:CvtPreTrainedModel
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[ "Conv2d", "Linear", "Model", "ModelConfig", "ModelLayer", "ModelPreTrainedModel", "ModelStage", "None", "PreTrainedModel", "_init_weights", "_no_split_modules", "base_model_prefix", "bias", "class", "cls_token", "config", "def", "elif", "if", "init", "initializer_range", "i...
cvt/modeling_cvt.py:CvtModel
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[ "BaseModelOutputWithCLSToken", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "add_pooling_layer", "auto_docstring", "class", "cls_token_value", "config", "def", "else", "encoder", "encoder_outputs", "forward", "hidden_states", "if", "is", "...
cvt/modeling_cvt.py:CvtForImageClassification
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[ "BCEWithLogitsLoss", "CrossEntropyLoss", "False", "Identity", "ImageClassifierOutputWithNoAttention", "LayerNorm", "Linear", "MSELoss", "Model", "ModelForImageClassification", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "add_pooling_layer", "and", "auto_docstring", "b...
phi/modeling_phi.py:PhiRotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "and", "arange", "attention_factor", "attention_scaling", "base", "cat", "class", "clone", "compute_default_rope_parameters", "config", "cos", "cpu", "def", "default", "deprecate...
phi/modeling_phi.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
phi/modeling_phi.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" ]
phi/modeling_phi.py:repeat_kv
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[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
phi/modeling_phi.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", "p", "query",...
phi/modeling_phi.py:PhiAttention
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[ "ALL_ATTENTION_FUNCTIONS", "LayerNorm", "Linear", "ModelAttention", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cat", "class", "config...
phi/modeling_phi.py:PhiMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "activation_fn", "class", "config", "def", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
phi/modeling_phi.py:PhiDecoderLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "LayerNorm", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "None", "Tensor", "_", "__init__", "attention_mask", "attn_outputs", "class", "config", "def", "eps", "feed_forward_hidden_states", "forward", "hidden_size", "hid...
phi/modeling_phi.py:PhiPreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supp...
phi/modeling_phi.py:PhiModel
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[ "BaseModelOutputWithPast", "Dropout", "DynamicCache", "Embedding", "False", "LayerNorm", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", ...
phi/modeling_phi.py:PhiForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_fsdp_plan", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "colwise_g...
phi/modeling_phi.py:PhiForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
phi/modeling_phi.py:PhiForTokenClassification
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[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
xglm/modeling_xglm.py:XGLMScaledWordEmbedding
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[ "Embedding", "Model", "__init__", "class", "def", "embed_scale", "embedding_dim", "forward", "input_ids", "nn", "num_embeddings", "padding_idx", "return", "self", "super" ]
xglm/modeling_xglm.py:XGLMSinusoidalPositionalEmbedding
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[ "False", "Model", "Module", "None", "__init__", "arange", "bsz", "cat", "class", "cos", "def", "detach", "device", "dim", "dtype", "emb", "emb_weights", "embedding_dim", "exp", "float", "forward", "get_default_dtype", "get_embedding", "half_dim", "hasattr", "if", ...
xglm/modeling_xglm.py:XGLMAttention
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[ "Attention", "EncoderDecoderCache", "False", "Linear", "Model", "Module", "None", "True", "ValueError", "_", "__init__", "and", "attention_mask", "attn_output", "attn_probs", "attn_weights", "attn_weights_reshaped", "be", "bmm", "bsz", "but", "by", "class", "cross_atten...
xglm/modeling_xglm.py:XGLMDecoderLayer
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xglm/modeling_xglm.py:XGLMPreTrainedModel
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xglm/modeling_xglm.py:XGLMModel
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[ "BaseModelOutputWithPastAndCrossAttentions", "DynamicCache", "EncoderDecoderCache", "False", "LayerNorm", "Model", "ModelAttention", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "ModelSinusoidalPositionalEmbedding", "ModuleList", "None", "OutputRecorder", "Val...
xglm/modeling_xglm.py:XGLMForCausalLM
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[ "CausalLMOutputWithCrossAttentions", "GenerationMixin", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "base_model_prefix", "capture_outputs", "class", "config", "cross_attentions", ...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TGenerationOutput
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[ "ModelGenerationOutput", "ModelOutput", "None", "class", "r", "sequences", "unit_sequences", "waveform", "waveform_lengths" ]
seamless_m4t/modeling_seamless_m4t.py:shift_tokens_right
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[ "Model_tokens_right", "Modeled_input_ids", "None", "clone", "decoder_start_token_id", "def", "if", "input_ids", "is", "masked_fill_", "new_zeros", "pad_token_id", "return", "shape" ]
seamless_m4t/modeling_seamless_m4t.py:_compute_new_attention_mask
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seamless_m4t/modeling_seamless_m4t.py:format_speech_generation_kwargs
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[ "Model_speech_generation_kwargs", "def", "elif", "else", "for", "generation_config", "if", "in", "items", "key", "kwargs", "kwargs_speech", "kwargs_text", "len", "not", "return", "speech_", "startswith", "text_", "value" ]
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerPositionalConvEmbedding
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[ "ACT2FN", "Conv1d", "GatheredParameters", "ModelConformerPositionalConvEmbedding", "ModelConformerSamePadLayer", "Module", "__init__", "activation", "class", "config", "conv", "deepspeed", "def", "dim", "else", "forward", "groups", "hasattr", "hidden_size", "hidden_states", "...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerRotaryPositionalEmbedding
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[ "ModelConformerRotaryPositionalEmbedding", "Module", "None", "__init__", "and", "arange", "base", "cached_rotary_positional_embedding", "cached_sequence_length", "cat", "class", "config", "cos", "cos_embeddings", "def", "dim", "dtype", "einsum", "embeddings", "float", "forwar...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerRelPositionalEmbedding
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seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerSamePadLayer
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[ "ModelConformerSamePadLayer", "Module", "__init__", "class", "def", "else", "forward", "hidden_states", "if", "nn", "num_conv_pos_embeddings", "num_pad_remove", "return", "self", "super" ]
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerFeatureProjection
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[ "Dropout", "LayerNorm", "Linear", "ModelConformerFeatureProjection", "Module", "__init__", "class", "config", "def", "dropout", "eps", "feature_projection_input_dim", "forward", "hidden_size", "hidden_states", "layer_norm", "layer_norm_eps", "nn", "norm_hidden_states", "project...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerFeedForward
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[ "ACT2FN", "Dropout", "Linear", "ModelConformerFeedForward", "Module", "None", "__init__", "act_fn", "class", "config", "def", "dropout", "else", "forward", "hidden_size", "hidden_states", "if", "intermediate_act_fn", "intermediate_dense", "intermediate_dropout", "is", "isin...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerConvolutionModule
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[ "ACT2FN", "BatchNorm1d", "Conv1d", "Dropout", "GLU", "LayerNorm", "ModelConformerConvolutionModule", "Module", "None", "SAME", "ValueError", "__init__", "a", "activation", "attention_mask", "batch_norm", "be", "bool", "class", "config", "conv_depthwise_kernel_size", "def", ...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerSelfAttention
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[ "Dropout", "False", "Linear", "ModelConformerSelfAttention", "Module", "None", "Parameter", "True", "ValueError", "__init__", "_apply_relative_embeddings", "_apply_rotary_embedding", "attention_mask", "batch_size", "be", "cat", "class", "config", "cos", "def", "defined", "d...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerEncoderLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "LayerNorm", "ModelConformerConvolutionModule", "ModelConformerEncoderLayer", "ModelConformerFeedForward", "ModelConformerSelfAttention", "None", "__init__", "attention_mask", "attn_weigts", "class", "config", "conv_attention_mask", "co...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerEncoder
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[ "BaseModelOutput", "Dropout", "False", "LayerNorm", "ModelConformerEncoder", "ModelConformerEncoderLayer", "ModelConformerRelPositionalEmbedding", "ModelConformerRotaryPositionalEmbedding", "Module", "ModuleList", "None", "True", "_", "__init__", "all_hidden_states", "all_self_attentio...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerAdapterLayer
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[ "Conv1d", "Dropout", "False", "GLU", "LayerNorm", "ModelConformerAdapterLayer", "ModelConformerFeedForward", "ModelConformerSelfAttention", "Module", "None", "__init__", "_compute_new_attention_mask", "_compute_sub_sample_lengths_from_attention_mask", "act_fn", "activation", "adaptor_d...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TConformerAdapter
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seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TScaledWordEmbedding
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[ "Embedding", "ModelScaledWordEmbedding", "__init__", "class", "def", "embed_scale", "embedding_dim", "forward", "input_ids", "nn", "num_embeddings", "padding_idx", "return", "self", "super" ]
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TSinusoidalPositionalEmbedding
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[ "False", "ModelSinusoidalPositionalEmbedding", "Module", "None", "__init__", "arange", "bsz", "cat", "class", "contiguous", "cos", "create_position_ids_from_input_ids", "create_position_ids_from_inputs_embeds", "cumsum", "def", "detach", "device", "dim", "dtype", "else", "emb...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TAttention
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[ "Attention", "EncoderDecoderCache", "False", "Instantiating", "Linear", "ModelAttention", "Module", "None", "Please", "True", "ValueError", "_", "__class__", "__init__", "__name__", "a", "and", "attention_mask", "attn_output", "attn_probs", "attn_weights", "attn_weights_res...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TFeedForwardNetwork
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[ "ACT2FN", "Dropout", "Linear", "ModelFeedForwardNetwork", "Module", "Tensor", "__init__", "act", "activation_dropout", "activation_function", "and", "class", "config", "def", "dropout", "dtype", "fc1", "fc2", "ffn_dim", "forward", "hidden_size", "hidden_states", "if", "...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TEncoderLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "LayerNorm", "ModelAttention", "ModelEncoderLayer", "ModelFeedForwardNetwork", "None", "__init__", "activation_dropout", "attention_dropout", "attention_mask", "attn_dropout", "attn_weights", "class", "config", "def", "dropout", "...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TDecoderLayer
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seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TPreTrainedModel
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[ "Conv1d", "Model", "ModelConfig", "ModelConformerEncoderLayer", "ModelConformerFeatureProjection", "ModelConformerPositionalConvEmbedding", "ModelConformerRelPositionalEmbedding", "ModelConformerRotaryPositionalEmbedding", "ModelConformerSelfAttention", "ModelDecoderLayer", "ModelEncoderLayer", ...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TSpeechEncoder
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seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TEncoder
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[ "BaseModelOutput", "False", "LayerNorm", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "ModelSinusoidalPositionalEmbedding", "ModuleList", "None", "Pass", "True", "ValueError", "You", "_", "__init__", "all_attentions", "and", "append", ...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TDecoder
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[ "BaseModelOutputWithPastAndCrossAttentions", "DynamicCache", "EncoderDecoderCache", "False", "LayerNorm", "ModelDecoder", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "ModelSinusoidalPositionalEmbedding", "ModuleList", "None", "Setting", "True", "__init__", ...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TTextToUnitModel
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seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TTextToUnitForConditionalGeneration
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[ "CrossEntropyLoss", "False", "GenerationMixin", "Linear", "ModelPreTrainedModel", "ModelTextToUnitForConditionalGeneration", "ModelTextToUnitModel", "Model_COMMON_CUSTOM_ARGS", "None", "Seq2SeqLMOutput", "__init__", "__setattr__", "_keys_to_ignore_on_load_missing", "_tied_weights_keys", ...
seamless_m4t/modeling_seamless_m4t.py:HifiGanResidualBlock
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seamless_m4t/modeling_seamless_m4t.py:SeamlessM4TVariancePredictor
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[ "Conv1d", "Dropout", "LayerNorm", "Linear", "ModelVariancePredictor", "Module", "ReLU", "__init__", "activation_function", "class", "config", "conv1", "conv2", "def", "dim", "dropout_module", "embed_dim", "forward", "hidden_states", "kernel_size", "ln1", "ln2", "nn", "p...
seamless_m4t/modeling_seamless_m4t.py:SeamlessM4THifiGan
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[ "Conv1d", "ConvTranspose1d", "HifiGanResidualBlock", "ModelHifiGan", "Module", "ModuleList", "__init__", "append", "channels", "class", "config", "conv_post", "conv_pre", "def", "dilation", "enumerate", "for", "forward", "functional", "hidden_states", "i", "in", "inputs_e...