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hann window
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@ -101,6 +101,9 @@ def flatten_state_dict(state_dict, parent_key='', sep='.'):
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if new_key.endswith("gamma"):
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new_key = new_key.replace("gamma", "gamma.weight")
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if new_key == "head.istft.window":
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new_key = "head.istft.window.weight"
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size_mb = value.element_size() * value.nelement() / (1024 * 1024)
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print(f"{size_mb:8.2f} MB - {new_key}: {value.shape}")
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@ -28,7 +28,6 @@ class TensorNameMap:
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"transformer.token_embeddings", # openelm
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"shared", # t5
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"rwkv.embeddings", # rwkv
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"feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed" # outetts
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),
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# Token type embeddings
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@ -3070,6 +3070,8 @@ struct llama_model {
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struct ggml_tensor * conv_1d = nullptr;
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struct ggml_tensor * conv_1d_b = nullptr;
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struct ggml_tensor * hann_window = nullptr;
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// resnet 0
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struct ggml_tensor * posnet_0_norm1 = nullptr;
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struct ggml_tensor * posnet_0_norm1_b = nullptr;
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@ -5121,8 +5123,7 @@ struct llama_model_loader {
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void done_getting_tensors() const {
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if (n_created != n_tensors) {
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// TODO: TEMPORARY DISABLED [OUTETTS]
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//throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
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throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
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}
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}
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@ -7461,6 +7462,7 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
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{LLM_TENSOR_CONV_NEXT_PW1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_CONV_NEXT_PW2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_CONV_NEXT_GAMMA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
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{LLM_TENSOR_HANN_WINDOW, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
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};
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// checks if the weight tensor can be used with the specified buffer type and device
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@ -9634,8 +9636,10 @@ static bool llm_load_tensors(
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model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
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model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {768}, 0);
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model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
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model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
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model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, 0);
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model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {1282}, 0);
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model.hann_window = create_tensor(tn(LLM_TENSOR_HANN_WINDOW, "weight"), {1280}, 0);
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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