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mpt : do not duplicate token_embd.weight on disk (#5670)
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@ -622,11 +622,6 @@ class MPTModel(Model):
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self.gguf_writer.add_tensor(new_name, data)
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# note: MPT output is tied to (same as) wte in original model;
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# for easier implementation in llama.cpp it's duplicated in GGUF, though :/
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if new_name == "token_embd.weight":
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self.gguf_writer.add_tensor("output.weight", data)
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class OrionModel(Model):
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def set_vocab(self):
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@ -509,7 +509,6 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
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@ -4056,7 +4055,10 @@ static bool llm_load_tensors(
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false);
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
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// same as tok_embd, duplicated to allow offloading
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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ml.n_created--; // artificial tensor
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ml.size_data += ggml_nbytes(model.output);
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}
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for (int i = 0; i < n_layer; ++i) {
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