Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
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24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
blenderbot/modeling_blenderbot.py:BlenderbotForCausalLM
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[ "CausalLMOutputWithCrossAttentions", "CrossEntropyLoss", "False", "GenerationMixin", "Linear", "ModelDecoderWrapper", "ModelForCausalLM", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache_position", "cla...
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", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "head_dim", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_v...
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", "cache_kwargs"...
vaultgemma/modeling_vaultgemma.py:VaultGemmaDecoderLayer
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[ "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "Tensor", "_", "__init__", "attention_mask", "attention_type", "cache_position", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "input_layernorm...
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", "device", ...
vaultgemma/modeling_vaultgemma.py:VaultGemmaPreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "RMSNorm", "True", "__class__", "__name__", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend"...
vaultgemma/modeling_vaultgemma.py:VaultGemmaModel
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "attention_type", "auto_do...
vaultgemma/modeling_vaultgemma.py:VaultGemmaForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "class", "colwi...
qwen3_moe/modeling_qwen3_moe.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
qwen3_moe/modeling_qwen3_moe.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" ]
qwen3_moe/modeling_qwen3_moe.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" ]
qwen3_moe/modeling_qwen3_moe.py:eager_attention_forward
[ 0, 0.021594731137156487, 0.01775064319372177, -0.018768195062875748, -0.00008832923776935786, 0.039797618985176086, 0.05359111353754997, -0.035049039870500565, 0.02069023996591568, 0.011645326390862465, 0.029395969584584236, 0.022499222308397293, 0.002741739386692643, -0.014584923163056374...
[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", ...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "ModelRMSNorm", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_kwargs", "cache...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "None", "__init__", "act_fn", "class", "config", "def", "down_proj", "else", "forward", "gate_proj", "hidden_act", "hidden_size", "if", "intermediate_size", "is", "nn", "return", "self", "super", "up_proj", "x" ]
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeExperts
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[ "ACT2FN", "ModelExperts", "Module", "None", "Parameter", "__init__", "act_fn", "chunk", "class", "config", "continue", "current_hidden_states", "current_state", "def", "dim", "down_proj", "dtype", "empty", "expert_hit", "expert_idx", "expert_mask", "final_hidden_states", ...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeTopKRouter
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[ "F", "ModelTopKRouter", "Module", "Parameter", "True", "__init__", "class", "config", "def", "dim", "dtype", "float", "forward", "functional", "hidden_dim", "hidden_size", "hidden_states", "if", "keepdim", "linear", "nn", "norm_topk_prob", "num_experts", "num_experts_pe...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeSparseMoeBlock
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[ "ModelExperts", "ModelSparseMoeBlock", "ModelTopKRouter", "Module", "_", "__init__", "batch_size", "class", "config", "def", "experts", "final_hidden_states", "forward", "gate", "hidden_dim", "hidden_states", "hidden_states_reshaped", "nn", "reshape", "return", "routing_weigh...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeRMSNorm
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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", ...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "ModelSparseMoeBlock", "None", "Tensor", "_", "__init__", "and", "attention_mask", "cache_position", "class", "config", "decoder_sparse_step", "def", "else", "eps", "forw...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoePreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelExperts", "ModelPreTrainedModel", "ModelTopKRouter", "OutputRecorder", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modules", "_skip_keys_device_placement", "_supports_a...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeRotaryEmbedding
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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", "device", ...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeModel
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[ "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "MoeModelOutputWithPast", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "cache_po...
qwen3_moe/modeling_qwen3_moe.py:load_balancing_loss_func
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[ "Model_balancing_loss_func", "None", "_", "attention_mask", "batch_size", "cat", "compute_device", "concatenated_gate_logits", "def", "device", "dim", "else", "expand", "expert_attention_mask", "expert_mask", "float", "for", "functional", "gate_logits", "if", "in", "is", ...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeForCausalLM
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[ "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "MoeCausalLMOutputWithPast", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "aux_loss", "cache_position", "can_return_tuple", ...
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeForTokenClassification
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[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
qwen3_moe/modeling_qwen3_moe.py:Qwen3MoeForQuestionAnswering
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yoso/modeling_yoso.py:load_cuda_kernels
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yoso/modeling_yoso.py:to_contiguous
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yoso/modeling_yoso.py:normalize
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yoso/modeling_yoso.py:hashing
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yoso/modeling_yoso.py:YosoCumulation
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yoso/modeling_yoso.py:YosoLSHCumulation
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yoso/modeling_yoso.py:YosoEmbeddings
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yoso/modeling_yoso.py:YosoSelfAttention
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yoso/modeling_yoso.py:YosoSelfOutput
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yoso/modeling_yoso.py:YosoAttention
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yoso/modeling_yoso.py:YosoIntermediate
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yoso/modeling_yoso.py:YosoOutput
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yoso/modeling_yoso.py:YosoLayer
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yoso/modeling_yoso.py:YosoEncoder
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yoso/modeling_yoso.py:YosoPredictionHeadTransform
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yoso/modeling_yoso.py:YosoLMPredictionHead
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yoso/modeling_yoso.py:YosoOnlyMLMHead
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yoso/modeling_yoso.py:YosoPreTrainedModel
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yoso/modeling_yoso.py:YosoModel
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yoso/modeling_yoso.py:YosoForMaskedLM
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yoso/modeling_yoso.py:YosoClassificationHead
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yoso/modeling_yoso.py:YosoForSequenceClassification
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yoso/modeling_yoso.py:YosoForMultipleChoice
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yoso/modeling_yoso.py:YosoForTokenClassification
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yoso/modeling_yoso.py:YosoForQuestionAnswering
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hunyuan_v1_moe/modeling_hunyuan_v1_moe.py:HunYuanMoEV1RMSNorm
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hunyuan_v1_moe/modeling_hunyuan_v1_moe.py:HunYuanMoEV1MLP
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hunyuan_v1_moe/modeling_hunyuan_v1_moe.py:rotate_half
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hunyuan_v1_moe/modeling_hunyuan_v1_moe.py:apply_rotary_pos_emb
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hunyuan_v1_moe/modeling_hunyuan_v1_moe.py:repeat_kv
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hunyuan_v1_moe/modeling_hunyuan_v1_moe.py:eager_attention_forward
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hunyuan_v1_moe/modeling_hunyuan_v1_moe.py:HunYuanMoEV1Attention
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dinov3_vit/modeling_dinov3_vit.py:DINOv3ViTEmbeddings
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dinov3_vit/modeling_dinov3_vit.py:rotate_half
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owlv2/modeling_owlv2.py:contrastive_loss
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owlv2/modeling_owlv2.py:_upcast
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owlv2/modeling_owlv2.py:box_area
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owlv2/modeling_owlv2.py:box_iou
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owlv2/modeling_owlv2.py:generalized_box_iou
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owlv2/modeling_owlv2.py:Owlv2ObjectDetectionOutput
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owlv2/modeling_owlv2.py:Owlv2ImageGuidedObjectDetectionOutput
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owlv2/modeling_owlv2.py:Owlv2VisionEmbeddings
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