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MM% EX]XbXb c ekk+6 D cc cs*A  model_type num_layers qual_namec trtr ddlm}m}n t j dtd||jjn|}||jjn|}|dk(rd}n|dk(rd }d}|jD]\}}||k(s |}n|td |d |||} i} |j} | jD]} |d k(rd | vrtj d d | } | d}} | j#ds| j#dr| j%dd\} }d|z}| j'| }|w|| z| ||z<|j)x}rW|D]R\} }t+||||| d}|jDcic] \}}|| vs ||}}}| j-|T| Scc}}w)aY GGUF uses this naming convention for their tensors from HF checkpoint: `blk.N.BB.weight` and `blk.N.BB.bias` where N signifies the block number of a layer, and BB signifies the attention/mlp layer components. See "Standardized tensor names" in https://github.com/ggerganov/ggml/blob/master/docs/gguf.md for details. r)MODEL_ARCH_NAMESget_tensor_name_mapLoading a GGUF checkpoint in PyTorch, requires both PyTorch and GGUF>=0.10.0 to be installed. 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Not doing so is faster and only loads the metadata in memory. r) GGUFReader dequantizerrzgeneral.architecturez general.nameNrmistralrrTr is_gated_actT5EncoderModel architecturesrrstablelm> attn_k.bias attn_q.bias attn_v.biasffn_normc3HK|]}D]}||jvywr+r).0tensor bias_nameattn_bias_names r' z'load_gguf_checkpoint..s)mF^lmQZyFKK/m/ms"c3:K|]}|jvywr+r)rr ffn_norm_names r'rz'load_gguf_checkpoint..s#^VMV[[$@#^s use_qkv_biasuse_parallel_residualzGGUF model with architecture z is not supported yet.falconrc3:K|]}d|jk7yw)rNr)rrs r'rz'load_gguf_checkpoint..s HvOv{{ * Hstie_word_embeddingsr`rz1Some keys were not parsed and added into account z | vocab_sizertokenszCan't find a way to retrieve missing config vocab_size from tokenizer parameters. 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