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transformers_code_embeddings_v3

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1
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24
117
embedding
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2.56k
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4
448
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeVisionPatchEmbed
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[ "Conv3d", "Model_5MoeVisionPatchEmbed", "Module", "__init__", "class", "config", "def", "dtype", "embed_dim", "forward", "hidden_size", "hidden_states", "in_channels", "kernel_size", "nn", "patch_size", "proj", "return", "self", "stride", "super", "target_dtype", "tempora...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeVisionPatchMerger
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[ "False", "GELU", "LayerNorm", "Linear", "Model_5MoeVisionPatchMerger", "Module", "__init__", "act_fn", "class", "config", "def", "else", "eps", "forward", "hidden_size", "if", "linear_fc1", "linear_fc2", "nn", "norm", "out_hidden_size", "return", "self", "spatial_merge_...
qwen3_5_moe/modeling_qwen3_5_moe.py:apply_rotary_pos_emb_vision
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[ "Model_rotary_pos_emb_vision", "cos", "def", "dtype", "float", "k", "k_embed", "orig_q_dtype", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze" ]
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeVisionAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "Model_5MoeVisionAttention", "Module", "None", "Tensor", "_", "__init__", "_attn_implementation", "apply_rotary_pos_emb_vision", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_outputs", "cat", "class"...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeVisionBlock
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[ "GradientCheckpointingLayer", "LayerNorm", "Model_5MoeVisionAttention", "Model_5MoeVisionBlock", "Model_5MoeVisionMLP", "None", "Tensor", "__init__", "attn", "attn_implementation", "class", "config", "cu_seqlens", "def", "eps", "forward", "hidden_size", "hidden_states", "kwargs",...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeVisionModel
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[ "BaseModelOutputWithPooling", "Embedding", "F", "False", "Model_5MoePreTrainedModel", "Model_5MoeVisionAttention", "Model_5MoeVisionBlock", "Model_5MoeVisionConfig", "Model_5MoeVisionModel", "Model_5MoeVisionPatchEmbed", "Model_5MoeVisionPatchMerger", "Model_5MoeVisionRotaryEmbedding", "Modu...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeModelOutputWithPast
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[ "ModelOutput", "Model_5MoeModelOutputWithPast", "None", "attentions", "class", "hidden_states", "last_hidden_state", "past_key_values", "r", "rope_deltas", "router_logits" ]
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeCausalLMOutputWithPast
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[ "ModelOutput", "Model_5MoeCausalLMOutputWithPast", "None", "attentions", "aux_loss", "class", "hidden_states", "logits", "loss", "past_key_values", "r", "rope_deltas", "router_logits" ]
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeTextModel
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[ "Embedding", "False", "Model_5MoeDecoderLayer", "Model_5MoeDynamicCache", "Model_5MoeModelOutputWithPast", "Model_5MoePreTrainedModel", "Model_5MoeRMSNorm", "Model_5MoeTextModel", "Model_5MoeTextRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "_update_linear_attn...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeModel
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[ "False", "Image", "Model_5MoeConfig", "Model_5MoeModel", "Model_5MoeModelOutputWithPast", "Model_5MoePreTrainedModel", "Model_5MoeTextDecoderLayer", "Model_5MoeTextModel", "Model_5MoeVisionBlock", "Model_5MoeVisionModel", "None", "True", "ValueError", "Video", "You", "_", "__init__",...
qwen3_5_moe/modeling_qwen3_5_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_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeForCausalLM
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[ "GenerationMixin", "Linear", "Model_5MoeForCausalLM", "Model_5MoePreTrainedModel", "Model_5MoeTextConfig", "Model_5MoeTextModel", "MoeCausalLMOutputWithPast", "None", "__init__", "_keys_to_ignore_on_load_unexpected", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attent...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeForConditionalGeneration
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[ "False", "GenerationMixin", "Linear", "Model_5MoeCausalLMOutputWithPast", "Model_5MoeConfig", "Model_5MoeForConditionalGeneration", "Model_5MoeModel", "Model_5MoePreTrainedModel", "None", "Tensor", "True", "__init__", "_checkpoint_conversion_mapping", "_expand_dict_for_generation", "_exp...
jamba/modeling_jamba.py:JambaRMSNorm
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[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "class", "def", "dtype", "eps", "extra_repr", "f", "float32", "forward", "hidden_size", "hidden_states", "input_dtype", "keepdim", "mean", "nn", "ones", "pow", "return", "rsqrt", "self", "shape", "super", ...
jamba/modeling_jamba.py:HybridMambaAttentionDynamicCache
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[ "Any", "False", "ModelMambaAttentionDynamicCache", "None", "_", "__getitem__", "__init__", "__len__", "append", "batch_size", "beam_idx", "cache_kwargs", "cache_position", "cat", "class", "config", "conv_kernel_size", "conv_states", "def", "device", "dim", "dtype", "else"...
jamba/modeling_jamba.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
jamba/modeling_jamba.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" ]
jamba/modeling_jamba.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" ]
jamba/modeling_jamba.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",...
jamba/modeling_jamba.py:JambaAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "Module", "None", "True", "__init__", "_attn_implementation", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_position", "class", "config", "contiguous", "def", "dropout", ...
jamba/modeling_jamba.py:JambaMambaMixer
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[ "A", "ACT2FN", "A_log", "B", "C", "CUDA", "Conv1d", "D", "False", "Fast", "HybridMambaAttentionDynamicCache", "Linear", "Make", "Mamba", "ModelMambaMixer", "ModelRMSNorm", "Module", "None", "Parameter", "The", "To", "True", "Turning", "_", "__init__", "a", "act", ...
jamba/modeling_jamba.py:JambaMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "class", "config", "def", "down_proj", "forward", "gate_proj", "hidden_act", "hidden_size", "intermediate_size", "nn", "return", "self", "super", "up_proj", "x" ]
jamba/modeling_jamba.py:JambaExperts
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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", ...
jamba/modeling_jamba.py:JambaSparseMoeBlock
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[ "Linear", "ModelExperts", "ModelSparseMoeBlock", "Module", "__init__", "batch_size", "class", "config", "def", "dim", "dtype", "experts", "ffn_dim", "float", "forward", "functional", "hidden_dim", "hidden_size", "hidden_states", "intermediate_size", "nn", "num_experts", "...
jamba/modeling_jamba.py:JambaAttentionDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelAttentionDecoderLayer", "ModelMLP", "ModelRMSNorm", "ModelSparseMoeBlock", "None", "_", "__init__", "attention_mask", "cache_position", "class", "config", "def", "else", "eps", "feed_forward", "ffn_layer_class", "fo...
jamba/modeling_jamba.py:JambaMambaDecoderLayer
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[ "GradientCheckpointingLayer", "ModelMLP", "ModelMambaDecoderLayer", "ModelMambaMixer", "ModelRMSNorm", "ModelSparseMoeBlock", "None", "__init__", "attention_mask", "cache_params", "class", "config", "def", "else", "eps", "feed_forward", "ffn_layer_class", "forward", "hidden_size"...
jamba/modeling_jamba.py:JambaPreTrainedModel
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[ "A", "A_log", "D", "Linear", "ModelAttention", "ModelAttentionDecoderLayer", "ModelConfig", "ModelExperts", "ModelMambaDecoderLayer", "ModelMambaMixer", "ModelPreTrainedModel", "None", "OutputRecorder", "PreTrainedModel", "True", "_can_record_outputs", "_init_weights", "_is_statefu...
jamba/modeling_jamba.py:JambaModel
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[ "ALL_DECODER_LAYER_TYPES", "Embedding", "False", "HybridMambaAttentionDynamicCache", "ModelMambaDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModuleList", "MoeModelOutputWithPast", "None", "True", "ValueError", "You", "__init__", "_update_mamba_mask", "all", ...
jamba/modeling_jamba.py:load_balancing_loss_func
[ -0.00027690583374351263, 0.01840798556804657, 0.001643570140004158, -0.028812499716877937, -0.0009432663209736347, 0.05808233842253685, 0.038874007761478424, -0.011547867208719254, 0, -0.02252405695617199, 0.031556546688079834, -0.0061455233953893185, -0.0008396499906666577, 0.014349081553...
[ "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", ...
jamba/modeling_jamba.py:JambaForCausalLM
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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", ...
jamba/modeling_jamba.py:JambaForSequenceClassification
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m2m_100/modeling_m2m_100.py:shift_tokens_right
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m2m_100/modeling_m2m_100.py:M2M100ScaledWordEmbedding
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m2m_100/modeling_m2m_100.py:M2M100SinusoidalPositionalEmbedding
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m2m_100/modeling_m2m_100.py:eager_attention_forward
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m2m_100/modeling_m2m_100.py:M2M100Attention
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m2m_100/modeling_m2m_100.py:M2M100EncoderLayer
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m2m_100/modeling_m2m_100.py:M2M100DecoderLayer
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m2m_100/modeling_m2m_100.py:M2M100PreTrainedModel
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m2m_100/modeling_m2m_100.py:M2M100Encoder
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m2m_100/modeling_m2m_100.py:M2M100Decoder
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m2m_100/modeling_m2m_100.py:M2M100Model
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m2m_100/modeling_m2m_100.py:M2M100ForConditionalGeneration
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xcodec/modeling_xcodec.py:XcodecOutput
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xcodec/modeling_xcodec.py:XcodecEncoderOutput
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xcodec/modeling_xcodec.py:XcodecDecoderOutput
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xcodec/modeling_xcodec.py:ResidualUnit
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xcodec/modeling_xcodec.py:SemanticEncoderBlock
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xcodec/modeling_xcodec.py:SemanticEncoder
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xcodec/modeling_xcodec.py:SemanticDecoderBlock
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xcodec/modeling_xcodec.py:SemanticDecoder
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xcodec/modeling_xcodec.py:XcodecEuclideanCodebook
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xcodec/modeling_xcodec.py:XcodecVectorQuantization
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xcodec/modeling_xcodec.py:XcodecResidualVectorQuantization
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xcodec/modeling_xcodec.py:XcodecPreTrainedModel
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xcodec/modeling_xcodec.py:XcodecModel
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qwen3_vl/modeling_qwen3_vl.py:BaseModelOutputWithDeepstackFeatures
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionMLP
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionPatchEmbed
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionRotaryEmbedding
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionPatchMerger
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qwen3_vl/modeling_qwen3_vl.py:rotate_half
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qwen3_vl/modeling_qwen3_vl.py:apply_rotary_pos_emb_vision
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qwen3_vl/modeling_qwen3_vl.py:repeat_kv
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qwen3_vl/modeling_qwen3_vl.py:eager_attention_forward
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionAttention
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionBlock
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextRotaryEmbedding
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextRMSNorm
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qwen3_vl/modeling_qwen3_vl.py:apply_rotary_pos_emb
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextAttention
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextMLP
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextDecoderLayer
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLModelOutputWithPast
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLPreTrainedModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLCausalLMOutputWithPast
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLForConditionalGeneration
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaCache
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falcon_mamba/modeling_falcon_mamba.py:rms_forward
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaMixer
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaRMSNorm
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaBlock
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaPreTrainedModel
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaOutput
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaCausalLMOutput
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaModel
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaForCausalLM
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