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"$D & +> C 161A1A"'''BR1S i ( .D'4,,r&r+rrXs@r'rrs (-r&rc&eZdZdfd ZdZxZS)MambaTensorProcessorc&t||yrArBrDs r'r.zMambaTensorProcessor.__init__rFr&c d|vrtj|d}d|vrtj| }t||iS)Nzssm_conv1d.weightrrjssm_a)r rnlogrr;s r'r=zMambaTensorProcessor.processsD $ &nnW15G d?ffgX&G'4,,r&r+rrXs@r'rrs ( -r&rc&eZdZdfd ZdZxZS)NemotronTensorProcessorc&t||yrArBrDs r'r.z NemotronTensorProcessor.__init__ rFr&c .d|vr|dz }t||iSNz norm.weightrr:r;s r'r=zNemotronTensorProcessor.process" D kG'4,,r&r+rrXs@r'rr s (-r&rc&eZdZdfd ZdZxZS)Gemma2TensorProcessorc&t||yrArBrDs r'r.zGemma2TensorProcessor.__init__rFr&c .d|vr|dz }t||iSrr:r;s r'r=zGemma2TensorProcessor.processrr&r+rrXs@r'rrs ( -r&rc&eZdZdfd ZdZxZS)Lfm2TensorProcessorc&t||yrArBrDs r'r.zLfm2TensorProcessor.__init__$rFr&c Rd|vrtj|d}t||iS)Nzshortconv.conv.weightrrj)r rnrr;s r'r=zLfm2TensorProcessor.process's) "d *nnW15G'4,,r&r+rrXs@r'rr#s (-r&r) llamaqwen2moeqwen3moebloomt5 t5encodergpt2mambanemotrongemma2gemma3lfm2c||jvrgS|j|}|jDcgc]%}t|j||j'c}Scc}wr+)fieldsdatar partstypes)readerfieldvalue _data_indexs r' read_fieldr>sP FMM! MM% EX]XbXb c ekk+6 D cc cs*A processor model_type num_layersr6c trtr ddlm}m}n t j dtd||jjn|}||jjn|}|dk(rd}n|dk(rd }n|d k(rd }n|d k(rd }n|dk(rd}d}|jD]\}} | |k(s |}n|td|d|||} i} |j} | D]} |j| } | d}}| jds| jdr| j!dd\}}d|z}| j#|}||j%| ||| ||| z| ||z<|j'x}rX|D]S\}}t)||||||d}|jDcic] \}}|| vs ||}}}| j+|U| 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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