load_backend: loaded RPC backend from /var/home/rybens/.hermes/runtime/b11026-linux-x64-cpu/libggml-rpc.so
load_backend: loaded CPU backend from /var/home/rybens/.hermes/runtime/b11026-linux-x64-cpu/libggml-cpu-haswell.so
llama_model_loader: loaded meta data with 46 key-value pairs and 320 tensors from /var/home/rybens/.cache/llama.cpp/Qwen3.5-0.8B-UD-Q4_K_XL.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = qwen35
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen3.5-0.8B
llama_model_loader: - kv   3:                           general.basename str              = Qwen3.5-0.8B
llama_model_loader: - kv   4:                       general.quantized_by str              = Unsloth
llama_model_loader: - kv   5:                         general.size_label str              = 0.8B
llama_model_loader: - kv   6:                            general.license str              = apache-2.0
llama_model_loader: - kv   7:                       general.license.link str              = https://huggingface.co/Qwen/Qwen3.5-0...
llama_model_loader: - kv   8:                           general.repo_url str              = https://huggingface.co/unsloth
llama_model_loader: - kv   9:                   general.base_model.count u32              = 1
llama_model_loader: - kv  10:                  general.base_model.0.name str              = Qwen3.5 0.8B
llama_model_loader: - kv  11:          general.base_model.0.organization str              = Qwen
llama_model_loader: - kv  12:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen3.5-0.8B
llama_model_loader: - kv  13:                               general.tags arr[str,1]       = ["image-text-to-text"]
llama_model_loader: - kv  14:                         qwen35.block_count u32              = 24
llama_model_loader: - kv  15:                      qwen35.context_length u32              = 262144
llama_model_loader: - kv  16:                    qwen35.embedding_length u32              = 1024
llama_model_loader: - kv  17:                 qwen35.feed_forward_length u32              = 3584
llama_model_loader: - kv  18:                qwen35.attention.head_count u32              = 8
llama_model_loader: - kv  19:             qwen35.attention.head_count_kv u32              = 2
llama_model_loader: - kv  20:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
llama_model_loader: - kv  21:                      qwen35.rope.freq_base f32              = 10000000.000000
llama_model_loader: - kv  22:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  23:                qwen35.attention.key_length u32              = 256
llama_model_loader: - kv  24:              qwen35.attention.value_length u32              = 256
llama_model_loader: - kv  25:                     qwen35.ssm.conv_kernel u32              = 4
llama_model_loader: - kv  26:                      qwen35.ssm.state_size u32              = 128
llama_model_loader: - kv  27:                     qwen35.ssm.group_count u32              = 16
llama_model_loader: - kv  28:                  qwen35.ssm.time_step_rank u32              = 16
llama_model_loader: - kv  29:                      qwen35.ssm.inner_size u32              = 2048
llama_model_loader: - kv  30:             qwen35.full_attention_interval u32              = 4
llama_model_loader: - kv  31:                qwen35.rope.dimension_count u32              = 64
llama_model_loader: - kv  32:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  33:                         tokenizer.ggml.pre str              = qwen35
llama_model_loader: - kv  34:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  35:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  36:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  37:                tokenizer.ggml.eos_token_id u32              = 248046
llama_model_loader: - kv  38:            tokenizer.ggml.padding_token_id u32              = 248055
llama_model_loader: - kv  39:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
llama_model_loader: - kv  40:               general.quantization_version u32              = 2
llama_model_loader: - kv  41:                          general.file_type u32              = 15
llama_model_loader: - kv  42:                      quantize.imatrix.file str              = Qwen3.5-0.8B-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv  43:                   quantize.imatrix.dataset str              = unsloth_calibration_Qwen3.5-0.8B.txt
llama_model_loader: - kv  44:             quantize.imatrix.entries_count u32              = 186
llama_model_loader: - kv  45:              quantize.imatrix.chunks_count u32              = 80
llama_model_loader: - type  f32:  133 tensors
llama_model_loader: - type  f16:   36 tensors
llama_model_loader: - type q8_0:   18 tensors
llama_model_loader: - type q4_K:   48 tensors
llama_model_loader: - type q5_K:   57 tensors
llama_model_loader: - type q6_K:   18 tensors
llama_model_loader: - type iq4_xs:   10 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 522.43 MiB (5.82 BPW) 
init_tokenizer: initializing tokenizer for type 2
load: 0 unused tokens
load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
load: control token: 248073 '<tts_text_bos>' is not marked as EOG
load: control token: 248072 '<tts_pad>' is not marked as EOG
load: control token: 248071 '<|audio_end|>' is not marked as EOG
load: control token: 248061 '<|fim_middle|>' is not marked as EOG
load: control token: 248055 '<|vision_pad|>' is not marked as EOG
load: control token: 248052 '<|quad_end|>' is not marked as EOG
load: control token: 248049 '<|box_start|>' is not marked as EOG
load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
load: control token: 248045 '<|im_start|>' is not marked as EOG
load: control token: 248057 '<|video_pad|>' is not marked as EOG
load: control token: 248070 '<|audio_start|>' is not marked as EOG
load: control token: 248056 '<|image_pad|>' is not marked as EOG
load: control token: 248054 '<|vision_end|>' is not marked as EOG
load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
load: control token: 248050 '<|box_end|>' is not marked as EOG
load: control token: 248074 '<tts_text_eod>' is not marked as EOG
load: control token: 248053 '<|vision_start|>' is not marked as EOG
load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
load: control token: 248051 '<|quad_start|>' is not marked as EOG
load: control token: 248076 '<|audio_pad|>' is not marked as EOG
load: printing all EOG tokens:
load:   - 248044 ('<|endoftext|>')
load:   - 248046 ('<|im_end|>')
load:   - 248063 ('<|fim_pad|>')
load:   - 248064 ('<|repo_name|>')
load:   - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch                  = qwen35
print_info: vocab_only            = 0
print_info: no_alloc              = 0
print_info: n_ctx_train           = 262144
print_info: n_embd_inp            = 1024
print_info: n_embd                = 1024
print_info: n_embd_out            = 1024
print_info: n_layer               = 24
print_info: n_layer_all           = 24
print_info: n_head                = 8
print_info: n_head_kv             = 2
print_info: n_rot                 = 64
print_info: n_swa                 = 0
print_info: is_swa_any            = 0
print_info: non_causal_type       = 0
print_info: n_embd_head_k         = 256
print_info: n_embd_head_v         = 256
print_info: n_gqa                 = 4
print_info: n_embd_k_gqa          = 512
print_info: n_embd_v_gqa          = 512
print_info: f_norm_eps            = 0.0e+00
print_info: f_norm_rms_eps        = 1.0e-06
print_info: f_clamp_kqv           = 0.0e+00
print_info: f_max_alibi_bias      = 0.0e+00
print_info: f_logit_scale         = 0.0e+00
print_info: f_attn_scale          = 0.0e+00
print_info: f_attn_value_scale    = 0.0000
print_info: n_ff                  = 3584
print_info: n_expert              = 0
print_info: n_expert_used         = 0
print_info: n_expert_groups       = 0
print_info: n_group_used          = 0
print_info: causal attn           = 1
print_info: pooling type          = -1
print_info: rope type             = 40
print_info: rope scaling          = linear
print_info: freq_base_train       = 10000000.0
print_info: freq_scale_train      = 1
print_info: n_ctx_orig_yarn       = 262144
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: mrope sections        = [11, 11, 10, 0]
print_info: ssm_d_conv            = 4
print_info: ssm_d_inner           = 2048
print_info: ssm_d_state           = 128
print_info: ssm_dt_rank           = 16
print_info: ssm_n_group           = 16
print_info: ssm_dt_b_c_rms        = 0
print_info: model type            = 0.8B
print_info: model params          = 752.39 M
print_info: general.name          = Qwen3.5-0.8B
print_info: vocab type            = BPE
print_info: n_vocab               = 248320
print_info: n_merges              = 247587
print_info: BOS token             = 11 ','
print_info: EOS token             = 248046 '<|im_end|>'
print_info: EOT token             = 248046 '<|im_end|>'
print_info: PAD token             = 248055 '<|vision_pad|>'
print_info: LF token              = 198 'Ċ'
print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
print_info: FIM MID token         = 248061 '<|fim_middle|>'
print_info: FIM PAD token         = 248063 '<|fim_pad|>'
print_info: FIM REP token         = 248064 '<|repo_name|>'
print_info: FIM SEP token         = 248065 '<|file_sep|>'
print_info: EOG token             = 248044 '<|endoftext|>'
print_info: EOG token             = 248046 '<|im_end|>'
print_info: EOG token             = 248063 '<|fim_pad|>'
print_info: EOG token             = 248064 '<|repo_name|>'
print_info: EOG token             = 248065 '<|file_sep|>'
print_info: max token length      = 256
load_tensors: loading model tensors, this can take a while... (load_mode = mmap)
load_tensors: layer   0 assigned to device CPU, is_swa = 0
load_tensors: layer   1 assigned to device CPU, is_swa = 0
load_tensors: layer   2 assigned to device CPU, is_swa = 0
load_tensors: layer   3 assigned to device CPU, is_swa = 0
load_tensors: layer   4 assigned to device CPU, is_swa = 0
load_tensors: layer   5 assigned to device CPU, is_swa = 0
load_tensors: layer   6 assigned to device CPU, is_swa = 0
load_tensors: layer   7 assigned to device CPU, is_swa = 0
load_tensors: layer   8 assigned to device CPU, is_swa = 0
load_tensors: layer   9 assigned to device CPU, is_swa = 0
load_tensors: layer  10 assigned to device CPU, is_swa = 0
load_tensors: layer  11 assigned to device CPU, is_swa = 0
load_tensors: layer  12 assigned to device CPU, is_swa = 0
load_tensors: layer  13 assigned to device CPU, is_swa = 0
load_tensors: layer  14 assigned to device CPU, is_swa = 0
load_tensors: layer  15 assigned to device CPU, is_swa = 0
load_tensors: layer  16 assigned to device CPU, is_swa = 0
load_tensors: layer  17 assigned to device CPU, is_swa = 0
load_tensors: layer  18 assigned to device CPU, is_swa = 0
load_tensors: layer  19 assigned to device CPU, is_swa = 0
load_tensors: layer  20 assigned to device CPU, is_swa = 0
load_tensors: layer  21 assigned to device CPU, is_swa = 0
load_tensors: layer  22 assigned to device CPU, is_swa = 0
load_tensors: layer  23 assigned to device CPU, is_swa = 0
load_tensors: layer  24 assigned to device CPU, is_swa = 0
create_tensor: loading tensor token_embd.weight
create_tensor: loading tensor output_norm.weight
create_tensor: loading tensor output.weight
create_tensor: loading tensor token_embd.weight
create_tensor: loading tensor blk.0.attn_norm.weight
create_tensor: loading tensor blk.0.post_attention_norm.weight
create_tensor: loading tensor blk.0.attn_qkv.weight
create_tensor: loading tensor blk.0.attn_gate.weight
create_tensor: loading tensor blk.0.ssm_conv1d.weight
create_tensor: loading tensor blk.0.ssm_dt.bias
create_tensor: loading tensor blk.0.ssm_a
create_tensor: loading tensor blk.0.ssm_beta.weight
create_tensor: loading tensor blk.0.ssm_alpha.weight
create_tensor: loading tensor blk.0.ssm_norm.weight
create_tensor: loading tensor blk.0.ssm_out.weight
create_tensor: loading tensor blk.0.ffn_gate.weight
create_tensor: loading tensor blk.0.ffn_down.weight
create_tensor: loading tensor blk.0.ffn_up.weight
create_tensor: loading tensor blk.1.attn_norm.weight
create_tensor: loading tensor blk.1.post_attention_norm.weight
create_tensor: loading tensor blk.1.attn_qkv.weight
create_tensor: loading tensor blk.1.attn_gate.weight
create_tensor: loading tensor blk.1.ssm_conv1d.weight
create_tensor: loading tensor blk.1.ssm_dt.bias
create_tensor: loading tensor blk.1.ssm_a
create_tensor: loading tensor blk.1.ssm_beta.weight
create_tensor: loading tensor blk.1.ssm_alpha.weight
create_tensor: loading tensor blk.1.ssm_norm.weight
create_tensor: loading tensor blk.1.ssm_out.weight
create_tensor: loading tensor blk.1.ffn_gate.weight
create_tensor: loading tensor blk.1.ffn_down.weight
create_tensor: loading tensor blk.1.ffn_up.weight
create_tensor: loading tensor blk.2.attn_norm.weight
create_tensor: loading tensor blk.2.post_attention_norm.weight
create_tensor: loading tensor blk.2.attn_qkv.weight
create_tensor: loading tensor blk.2.attn_gate.weight
create_tensor: loading tensor blk.2.ssm_conv1d.weight
create_tensor: loading tensor blk.2.ssm_dt.bias
create_tensor: loading tensor blk.2.ssm_a
create_tensor: loading tensor blk.2.ssm_beta.weight
create_tensor: loading tensor blk.2.ssm_alpha.weight
create_tensor: loading tensor blk.2.ssm_norm.weight
create_tensor: loading tensor blk.2.ssm_out.weight
create_tensor: loading tensor blk.2.ffn_gate.weight
create_tensor: loading tensor blk.2.ffn_down.weight
create_tensor: loading tensor blk.2.ffn_up.weight
create_tensor: loading tensor blk.3.attn_norm.weight
create_tensor: loading tensor blk.3.post_attention_norm.weight
create_tensor: loading tensor blk.3.attn_qkv.weight
create_tensor: loading tensor blk.3.attn_q.weight
create_tensor: loading tensor blk.3.attn_k.weight
create_tensor: loading tensor blk.3.attn_v.weight
create_tensor: loading tensor blk.3.attn_q.bias
create_tensor: loading tensor blk.3.attn_k.bias
create_tensor: loading tensor blk.3.attn_v.bias
create_tensor: loading tensor blk.3.attn_output.weight
create_tensor: loading tensor blk.3.attn_q_norm.weight
create_tensor: loading tensor blk.3.attn_k_norm.weight
create_tensor: loading tensor blk.3.ffn_gate.weight
create_tensor: loading tensor blk.3.ffn_down.weight
create_tensor: loading tensor blk.3.ffn_up.weight
create_tensor: loading tensor blk.4.attn_norm.weight
create_tensor: loading tensor blk.4.post_attention_norm.weight
create_tensor: loading tensor blk.4.attn_qkv.weight
create_tensor: loading tensor blk.4.attn_gate.weight
create_tensor: loading tensor blk.4.ssm_conv1d.weight
create_tensor: loading tensor blk.4.ssm_dt.bias
create_tensor: loading tensor blk.4.ssm_a
create_tensor: loading tensor blk.4.ssm_beta.weight
create_tensor: loading tensor blk.4.ssm_alpha.weight
create_tensor: loading tensor blk.4.ssm_norm.weight
create_tensor: loading tensor blk.4.ssm_out.weight
create_tensor: loading tensor blk.4.ffn_gate.weight
create_tensor: loading tensor blk.4.ffn_down.weight
create_tensor: loading tensor blk.4.ffn_up.weight
create_tensor: loading tensor blk.5.attn_norm.weight
create_tensor: loading tensor blk.5.post_attention_norm.weight
create_tensor: loading tensor blk.5.attn_qkv.weight
create_tensor: loading tensor blk.5.attn_gate.weight
create_tensor: loading tensor blk.5.ssm_conv1d.weight
create_tensor: loading tensor blk.5.ssm_dt.bias
create_tensor: loading tensor blk.5.ssm_a
create_tensor: loading tensor blk.5.ssm_beta.weight
create_tensor: loading tensor blk.5.ssm_alpha.weight
create_tensor: loading tensor blk.5.ssm_norm.weight
create_tensor: loading tensor blk.5.ssm_out.weight
create_tensor: loading tensor blk.5.ffn_gate.weight
create_tensor: loading tensor blk.5.ffn_down.weight
create_tensor: loading tensor blk.5.ffn_up.weight
create_tensor: loading tensor blk.6.attn_norm.weight
create_tensor: loading tensor blk.6.post_attention_norm.weight
create_tensor: loading tensor blk.6.attn_qkv.weight
create_tensor: loading tensor blk.6.attn_gate.weight
create_tensor: loading tensor blk.6.ssm_conv1d.weight
create_tensor: loading tensor blk.6.ssm_dt.bias
create_tensor: loading tensor blk.6.ssm_a
create_tensor: loading tensor blk.6.ssm_beta.weight
create_tensor: loading tensor blk.6.ssm_alpha.weight
create_tensor: loading tensor blk.6.ssm_norm.weight
create_tensor: loading tensor blk.6.ssm_out.weight
create_tensor: loading tensor blk.6.ffn_gate.weight
create_tensor: loading tensor blk.6.ffn_down.weight
create_tensor: loading tensor blk.6.ffn_up.weight
create_tensor: loading tensor blk.7.attn_norm.weight
create_tensor: loading tensor blk.7.post_attention_norm.weight
create_tensor: loading tensor blk.7.attn_qkv.weight
create_tensor: loading tensor blk.7.attn_q.weight
create_tensor: loading tensor blk.7.attn_k.weight
create_tensor: loading tensor blk.7.attn_v.weight
create_tensor: loading tensor blk.7.attn_q.bias
create_tensor: loading tensor blk.7.attn_k.bias
create_tensor: loading tensor blk.7.attn_v.bias
create_tensor: loading tensor blk.7.attn_output.weight
create_tensor: loading tensor blk.7.attn_q_norm.weight
create_tensor: loading tensor blk.7.attn_k_norm.weight
create_tensor: loading tensor blk.7.ffn_gate.weight
create_tensor: loading tensor blk.7.ffn_down.weight
create_tensor: loading tensor blk.7.ffn_up.weight
create_tensor: loading tensor blk.8.attn_norm.weight
create_tensor: loading tensor blk.8.post_attention_norm.weight
create_tensor: loading tensor blk.8.attn_qkv.weight
create_tensor: loading tensor blk.8.attn_gate.weight
create_tensor: loading tensor blk.8.ssm_conv1d.weight
create_tensor: loading tensor blk.8.ssm_dt.bias
create_tensor: loading tensor blk.8.ssm_a
create_tensor: loading tensor blk.8.ssm_beta.weight
create_tensor: loading tensor blk.8.ssm_alpha.weight
create_tensor: loading tensor blk.8.ssm_norm.weight
create_tensor: loading tensor blk.8.ssm_out.weight
create_tensor: loading tensor blk.8.ffn_gate.weight
create_tensor: loading tensor blk.8.ffn_down.weight
create_tensor: loading tensor blk.8.ffn_up.weight
create_tensor: loading tensor blk.9.attn_norm.weight
create_tensor: loading tensor blk.9.post_attention_norm.weight
create_tensor: loading tensor blk.9.attn_qkv.weight
create_tensor: loading tensor blk.9.attn_gate.weight
create_tensor: loading tensor blk.9.ssm_conv1d.weight
create_tensor: loading tensor blk.9.ssm_dt.bias
create_tensor: loading tensor blk.9.ssm_a
create_tensor: loading tensor blk.9.ssm_beta.weight
create_tensor: loading tensor blk.9.ssm_alpha.weight
create_tensor: loading tensor blk.9.ssm_norm.weight
create_tensor: loading tensor blk.9.ssm_out.weight
create_tensor: loading tensor blk.9.ffn_gate.weight
create_tensor: loading tensor blk.9.ffn_down.weight
create_tensor: loading tensor blk.9.ffn_up.weight
create_tensor: loading tensor blk.10.attn_norm.weight
create_tensor: loading tensor blk.10.post_attention_norm.weight
create_tensor: loading tensor blk.10.attn_qkv.weight
create_tensor: loading tensor blk.10.attn_gate.weight
create_tensor: loading tensor blk.10.ssm_conv1d.weight
create_tensor: loading tensor blk.10.ssm_dt.bias
create_tensor: loading tensor blk.10.ssm_a
create_tensor: loading tensor blk.10.ssm_beta.weight
create_tensor: loading tensor blk.10.ssm_alpha.weight
create_tensor: loading tensor blk.10.ssm_norm.weight
create_tensor: loading tensor blk.10.ssm_out.weight
create_tensor: loading tensor blk.10.ffn_gate.weight
create_tensor: loading tensor blk.10.ffn_down.weight
create_tensor: loading tensor blk.10.ffn_up.weight
create_tensor: loading tensor blk.11.attn_norm.weight
create_tensor: loading tensor blk.11.post_attention_norm.weight
create_tensor: loading tensor blk.11.attn_qkv.weight
create_tensor: loading tensor blk.11.attn_q.weight
create_tensor: loading tensor blk.11.attn_k.weight
create_tensor: loading tensor blk.11.attn_v.weight
create_tensor: loading tensor blk.11.attn_q.bias
create_tensor: loading tensor blk.11.attn_k.bias
create_tensor: loading tensor blk.11.attn_v.bias
create_tensor: loading tensor blk.11.attn_output.weight
create_tensor: loading tensor blk.11.attn_q_norm.weight
create_tensor: loading tensor blk.11.attn_k_norm.weight
create_tensor: loading tensor blk.11.ffn_gate.weight
create_tensor: loading tensor blk.11.ffn_down.weight
create_tensor: loading tensor blk.11.ffn_up.weight
create_tensor: loading tensor blk.12.attn_norm.weight
create_tensor: loading tensor blk.12.post_attention_norm.weight
create_tensor: loading tensor blk.12.attn_qkv.weight
create_tensor: loading tensor blk.12.attn_gate.weight
create_tensor: loading tensor blk.12.ssm_conv1d.weight
create_tensor: loading tensor blk.12.ssm_dt.bias
create_tensor: loading tensor blk.12.ssm_a
create_tensor: loading tensor blk.12.ssm_beta.weight
create_tensor: loading tensor blk.12.ssm_alpha.weight
create_tensor: loading tensor blk.12.ssm_norm.weight
create_tensor: loading tensor blk.12.ssm_out.weight
create_tensor: loading tensor blk.12.ffn_gate.weight
create_tensor: loading tensor blk.12.ffn_down.weight
create_tensor: loading tensor blk.12.ffn_up.weight
create_tensor: loading tensor blk.13.attn_norm.weight
create_tensor: loading tensor blk.13.post_attention_norm.weight
create_tensor: loading tensor blk.13.attn_qkv.weight
create_tensor: loading tensor blk.13.attn_gate.weight
create_tensor: loading tensor blk.13.ssm_conv1d.weight
create_tensor: loading tensor blk.13.ssm_dt.bias
create_tensor: loading tensor blk.13.ssm_a
create_tensor: loading tensor blk.13.ssm_beta.weight
create_tensor: loading tensor blk.13.ssm_alpha.weight
create_tensor: loading tensor blk.13.ssm_norm.weight
create_tensor: loading tensor blk.13.ssm_out.weight
create_tensor: loading tensor blk.13.ffn_gate.weight
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create_tensor: loading tensor blk.17.ssm_out.scale
create_tensor: loading tensor blk.17.ssm_alpha.scale
create_tensor: loading tensor blk.17.ssm_beta.scale
create_tensor: loading tensor blk.17.attn_qkv.input_scale
create_tensor: loading tensor blk.17.attn_gate.input_scale
create_tensor: loading tensor blk.17.ffn_gate.input_scale
create_tensor: loading tensor blk.17.ffn_down.input_scale
create_tensor: loading tensor blk.17.ffn_up.input_scale
create_tensor: loading tensor blk.17.ssm_out.input_scale
create_tensor: loading tensor blk.17.ssm_alpha.input_scale
create_tensor: loading tensor blk.17.ssm_beta.input_scale
create_tensor: loading tensor blk.18.attn_qkv.scale
create_tensor: loading tensor blk.18.attn_gate.scale
create_tensor: loading tensor blk.18.ffn_gate.scale
create_tensor: loading tensor blk.18.ffn_down.scale
create_tensor: loading tensor blk.18.ffn_up.scale
create_tensor: loading tensor blk.18.ssm_out.scale
create_tensor: loading tensor blk.18.ssm_alpha.scale
create_tensor: loading tensor blk.18.ssm_beta.scale
create_tensor: loading tensor blk.18.attn_qkv.input_scale
create_tensor: loading tensor blk.18.attn_gate.input_scale
create_tensor: loading tensor blk.18.ffn_gate.input_scale
create_tensor: loading tensor blk.18.ffn_down.input_scale
create_tensor: loading tensor blk.18.ffn_up.input_scale
create_tensor: loading tensor blk.18.ssm_out.input_scale
create_tensor: loading tensor blk.18.ssm_alpha.input_scale
create_tensor: loading tensor blk.18.ssm_beta.input_scale
create_tensor: loading tensor blk.19.attn_q.scale
create_tensor: loading tensor blk.19.attn_k.scale
create_tensor: loading tensor blk.19.attn_v.scale
create_tensor: loading tensor blk.19.attn_output.scale
create_tensor: loading tensor blk.19.ffn_gate.scale
create_tensor: loading tensor blk.19.ffn_down.scale
create_tensor: loading tensor blk.19.ffn_up.scale
create_tensor: loading tensor blk.19.attn_q.input_scale
create_tensor: loading tensor blk.19.attn_k.input_scale
create_tensor: loading tensor blk.19.attn_v.input_scale
create_tensor: loading tensor blk.19.attn_output.input_scale
create_tensor: loading tensor blk.19.ffn_gate.input_scale
create_tensor: loading tensor blk.19.ffn_down.input_scale
create_tensor: loading tensor blk.19.ffn_up.input_scale
create_tensor: loading tensor blk.20.attn_qkv.scale
create_tensor: loading tensor blk.20.attn_gate.scale
create_tensor: loading tensor blk.20.ffn_gate.scale
create_tensor: loading tensor blk.20.ffn_down.scale
create_tensor: loading tensor blk.20.ffn_up.scale
create_tensor: loading tensor blk.20.ssm_out.scale
create_tensor: loading tensor blk.20.ssm_alpha.scale
create_tensor: loading tensor blk.20.ssm_beta.scale
create_tensor: loading tensor blk.20.attn_qkv.input_scale
create_tensor: loading tensor blk.20.attn_gate.input_scale
create_tensor: loading tensor blk.20.ffn_gate.input_scale
create_tensor: loading tensor blk.20.ffn_down.input_scale
create_tensor: loading tensor blk.20.ffn_up.input_scale
create_tensor: loading tensor blk.20.ssm_out.input_scale
create_tensor: loading tensor blk.20.ssm_alpha.input_scale
create_tensor: loading tensor blk.20.ssm_beta.input_scale
create_tensor: loading tensor blk.21.attn_qkv.scale
create_tensor: loading tensor blk.21.attn_gate.scale
create_tensor: loading tensor blk.21.ffn_gate.scale
create_tensor: loading tensor blk.21.ffn_down.scale
create_tensor: loading tensor blk.21.ffn_up.scale
create_tensor: loading tensor blk.21.ssm_out.scale
create_tensor: loading tensor blk.21.ssm_alpha.scale
create_tensor: loading tensor blk.21.ssm_beta.scale
create_tensor: loading tensor blk.21.attn_qkv.input_scale
create_tensor: loading tensor blk.21.attn_gate.input_scale
create_tensor: loading tensor blk.21.ffn_gate.input_scale
create_tensor: loading tensor blk.21.ffn_down.input_scale
create_tensor: loading tensor blk.21.ffn_up.input_scale
create_tensor: loading tensor blk.21.ssm_out.input_scale
create_tensor: loading tensor blk.21.ssm_alpha.input_scale
create_tensor: loading tensor blk.21.ssm_beta.input_scale
create_tensor: loading tensor blk.22.attn_qkv.scale
create_tensor: loading tensor blk.22.attn_gate.scale
create_tensor: loading tensor blk.22.ffn_gate.scale
create_tensor: loading tensor blk.22.ffn_down.scale
create_tensor: loading tensor blk.22.ffn_up.scale
create_tensor: loading tensor blk.22.ssm_out.scale
create_tensor: loading tensor blk.22.ssm_alpha.scale
create_tensor: loading tensor blk.22.ssm_beta.scale
create_tensor: loading tensor blk.22.attn_qkv.input_scale
create_tensor: loading tensor blk.22.attn_gate.input_scale
create_tensor: loading tensor blk.22.ffn_gate.input_scale
create_tensor: loading tensor blk.22.ffn_down.input_scale
create_tensor: loading tensor blk.22.ffn_up.input_scale
create_tensor: loading tensor blk.22.ssm_out.input_scale
create_tensor: loading tensor blk.22.ssm_alpha.input_scale
create_tensor: loading tensor blk.22.ssm_beta.input_scale
create_tensor: loading tensor blk.23.attn_q.scale
create_tensor: loading tensor blk.23.attn_k.scale
create_tensor: loading tensor blk.23.attn_v.scale
create_tensor: loading tensor blk.23.attn_output.scale
create_tensor: loading tensor blk.23.ffn_gate.scale
create_tensor: loading tensor blk.23.ffn_down.scale
create_tensor: loading tensor blk.23.ffn_up.scale
create_tensor: loading tensor blk.23.attn_q.input_scale
create_tensor: loading tensor blk.23.attn_k.input_scale
create_tensor: loading tensor blk.23.attn_v.input_scale
create_tensor: loading tensor blk.23.attn_output.input_scale
create_tensor: loading tensor blk.23.ffn_gate.input_scale
create_tensor: loading tensor blk.23.ffn_down.input_scale
create_tensor: loading tensor blk.23.ffn_up.input_scale
done_getting_tensors: tensor 'token_embd.weight' (q6_K) (and 272 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
load_tensors: offloading 0 repeating layers to GPU
load_tensors: offloaded 0/25 layers to GPU
load_tensors:   CPU_Mapped model buffer size =   522.43 MiB
load_tensors:   CPU_REPACK model buffer size =    85.22 MiB
..............................................repack: repack tensor blk.0.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.0.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.3.attn_output.weight with q4_K_8x8
repack: repack tensor blk.4.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.4.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.5.ffn_gate.weight with q4_K_8x8
repack: repack tensor blk.5.ffn_up.weight with q4_K_8x8
.repack: repack tensor blk.6.ffn_gate.weight with q4_K_8x8
repack: repack tensor blk.6.ffn_down.weight with q4_K_8x8
repack: repack tensor blk.6.ffn_up.weight with q4_K_8x8
.repack: repack tensor blk.7.attn_q.weight with q4_K_8x8
repack: repack tensor blk.7.attn_k.weight with q4_K_8x8
repack: repack tensor blk.7.attn_output.weight with q4_K_8x8
repack: repack tensor blk.7.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.7.ffn_down.weight with q4_K_8x8
repack: repack tensor blk.7.ffn_up.weight with q4_K_8x8
.repack: repack tensor blk.11.attn_q.weight with q4_K_8x8
repack: repack tensor blk.11.attn_k.weight with q4_K_8x8
repack: repack tensor blk.11.attn_output.weight with q4_K_8x8
repack: repack tensor blk.11.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.11.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.12.ffn_gate.weight with q4_K_8x8
repack: repack tensor blk.12.ffn_down.weight with q4_K_8x8
.repack: repack tensor blk.12.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.13.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.13.ffn_down.weight with q4_K_8x8
repack: repack tensor blk.13.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.14.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.14.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.15.attn_output.weight with q4_K_8x8
repack: repack tensor blk.16.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.16.ffn_down.weight with q4_K_8x8
repack: repack tensor blk.16.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.18.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.18.ffn_down.weight with q4_K_8x8
repack: repack tensor blk.18.ffn_up.weight with q4_K_8x8
.repack: repack tensor blk.19.attn_q.weight with q4_K_8x8
repack: repack tensor blk.19.attn_k.weight with q4_K_8x8
repack: repack tensor blk.19.attn_output.weight with q4_K_8x8
repack: repack tensor blk.19.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.19.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.20.ffn_gate.weight with q4_K_8x8
repack: repack tensor blk.20.ffn_down.weight with q4_K_8x8
.repack: repack tensor blk.20.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.22.ffn_gate.weight with q4_K_8x8
.repack: repack tensor blk.22.ffn_down.weight with q4_K_8x8
repack: repack tensor blk.22.ffn_up.weight with q4_K_8x8
repack: repack tensor blk.23.attn_output.weight with q4_K_8x8
.
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 124
llama_context: n_ubatch              = 124
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 10000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 124
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     4.74 MiB
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache:        CPU KV buffer size =     3.00 MiB
llama_kv_cache: size =    3.00 MiB (   256 cells,   6 layers,  5/1 seqs), K (f16):    1.50 MiB, V (f16):    1.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent, layer   0: dev = CPU
llama_memory_recurrent, layer   1: dev = CPU
llama_memory_recurrent, layer   2: dev = CPU
llama_memory_recurrent: layer   3: skipped
llama_memory_recurrent, layer   4: dev = CPU
llama_memory_recurrent, layer   5: dev = CPU
llama_memory_recurrent, layer   6: dev = CPU
llama_memory_recurrent: layer   7: skipped
llama_memory_recurrent, layer   8: dev = CPU
llama_memory_recurrent, layer   9: dev = CPU
llama_memory_recurrent, layer  10: dev = CPU
llama_memory_recurrent: layer  11: skipped
llama_memory_recurrent, layer  12: dev = CPU
llama_memory_recurrent, layer  13: dev = CPU
llama_memory_recurrent, layer  14: dev = CPU
llama_memory_recurrent: layer  15: skipped
llama_memory_recurrent, layer  16: dev = CPU
llama_memory_recurrent, layer  17: dev = CPU
llama_memory_recurrent, layer  18: dev = CPU
llama_memory_recurrent: layer  19: skipped
llama_memory_recurrent, layer  20: dev = CPU
llama_memory_recurrent, layer  21: dev = CPU
llama_memory_recurrent, layer  22: dev = CPU
llama_memory_recurrent: layer  23: skipped
llama_memory_recurrent:        CPU RS buffer size =    96.33 MiB
llama_memory_recurrent: size =   96.33 MiB (     5 cells,  24 layers,  5 seqs  0 rs_seq), R (f32):    6.33 MiB, S (f32):   90.00 MiB, P (f32):    0.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 10240
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 124, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
sched_reserve:        CPU compute buffer size =   118.99 MiB
sched_reserve: graph nodes  = 1411
sched_reserve: graph splits = 1
sched_reserve: reserve took 5.89 ms, sched copies = 1
~llama_context:        CPU compute buffer size is 118.9878 MiB, matches expectation of 118.9878 MiB
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 124
llama_context: n_ubatch              = 124
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 10000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 124
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     4.74 MiB
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache:        CPU KV buffer size =     3.00 MiB
llama_kv_cache: size =    3.00 MiB (   256 cells,   6 layers,  5/1 seqs), K (f16):    1.50 MiB, V (f16):    1.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent, layer   0: dev = CPU
llama_memory_recurrent, layer   1: dev = CPU
llama_memory_recurrent, layer   2: dev = CPU
llama_memory_recurrent: layer   3: skipped
llama_memory_recurrent, layer   4: dev = CPU
llama_memory_recurrent, layer   5: dev = CPU
llama_memory_recurrent, layer   6: dev = CPU
llama_memory_recurrent: layer   7: skipped
llama_memory_recurrent, layer   8: dev = CPU
llama_memory_recurrent, layer   9: dev = CPU
llama_memory_recurrent, layer  10: dev = CPU
llama_memory_recurrent: layer  11: skipped
llama_memory_recurrent, layer  12: dev = CPU
llama_memory_recurrent, layer  13: dev = CPU
llama_memory_recurrent, layer  14: dev = CPU
llama_memory_recurrent: layer  15: skipped
llama_memory_recurrent, layer  16: dev = CPU
llama_memory_recurrent, layer  17: dev = CPU
llama_memory_recurrent, layer  18: dev = CPU
llama_memory_recurrent: layer  19: skipped
llama_memory_recurrent, layer  20: dev = CPU
llama_memory_recurrent, layer  21: dev = CPU
llama_memory_recurrent, layer  22: dev = CPU
llama_memory_recurrent: layer  23: skipped
llama_memory_recurrent:        CPU RS buffer size =    96.33 MiB
llama_memory_recurrent: size =   96.33 MiB (     5 cells,  24 layers,  5 seqs  0 rs_seq), R (f32):    6.33 MiB, S (f32):   90.00 MiB, P (f32):    0.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 10240
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 124, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
sched_reserve:        CPU compute buffer size =   118.99 MiB
sched_reserve: graph nodes  = 1411
sched_reserve: graph splits = 1
sched_reserve: reserve took 7.44 ms, sched copies = 1
state_read_meta: cell_count = 59, dest_seq_id = 0
~llama_context:        CPU compute buffer size is 118.9878 MiB, matches expectation of 118.9878 MiB
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 124
llama_context: n_ubatch              = 124
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 10000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 124
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     4.74 MiB
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache:        CPU KV buffer size =     3.00 MiB
llama_kv_cache: size =    3.00 MiB (   256 cells,   6 layers,  5/1 seqs), K (f16):    1.50 MiB, V (f16):    1.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent, layer   0: dev = CPU
llama_memory_recurrent, layer   1: dev = CPU
llama_memory_recurrent, layer   2: dev = CPU
llama_memory_recurrent: layer   3: skipped
llama_memory_recurrent, layer   4: dev = CPU
llama_memory_recurrent, layer   5: dev = CPU
llama_memory_recurrent, layer   6: dev = CPU
llama_memory_recurrent: layer   7: skipped
llama_memory_recurrent, layer   8: dev = CPU
llama_memory_recurrent, layer   9: dev = CPU
llama_memory_recurrent, layer  10: dev = CPU
llama_memory_recurrent: layer  11: skipped
llama_memory_recurrent, layer  12: dev = CPU
llama_memory_recurrent, layer  13: dev = CPU
llama_memory_recurrent, layer  14: dev = CPU
llama_memory_recurrent: layer  15: skipped
llama_memory_recurrent, layer  16: dev = CPU
llama_memory_recurrent, layer  17: dev = CPU
llama_memory_recurrent, layer  18: dev = CPU
llama_memory_recurrent: layer  19: skipped
llama_memory_recurrent, layer  20: dev = CPU
llama_memory_recurrent, layer  21: dev = CPU
llama_memory_recurrent, layer  22: dev = CPU
llama_memory_recurrent: layer  23: skipped
llama_memory_recurrent:        CPU RS buffer size =    96.33 MiB
llama_memory_recurrent: size =   96.33 MiB (     5 cells,  24 layers,  5 seqs  0 rs_seq), R (f32):    6.33 MiB, S (f32):   90.00 MiB, P (f32):    0.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 10240
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 124, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
sched_reserve:        CPU compute buffer size =   118.99 MiB
sched_reserve: graph nodes  = 1411
sched_reserve: graph splits = 1
sched_reserve: reserve took 107.23 ms, sched copies = 1
state_read_meta: cell_count = 59, dest_seq_id = 0
~llama_context:        CPU compute buffer size is 118.9878 MiB, matches expectation of 118.9878 MiB
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 124
llama_context: n_ubatch              = 124
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 10000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 124
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     4.74 MiB
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache:        CPU KV buffer size =     3.00 MiB
llama_kv_cache: size =    3.00 MiB (   256 cells,   6 layers,  5/1 seqs), K (f16):    1.50 MiB, V (f16):    1.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent, layer   0: dev = CPU
llama_memory_recurrent, layer   1: dev = CPU
llama_memory_recurrent, layer   2: dev = CPU
llama_memory_recurrent: layer   3: skipped
llama_memory_recurrent, layer   4: dev = CPU
llama_memory_recurrent, layer   5: dev = CPU
llama_memory_recurrent, layer   6: dev = CPU
llama_memory_recurrent: layer   7: skipped
llama_memory_recurrent, layer   8: dev = CPU
llama_memory_recurrent, layer   9: dev = CPU
llama_memory_recurrent, layer  10: dev = CPU
llama_memory_recurrent: layer  11: skipped
llama_memory_recurrent, layer  12: dev = CPU
llama_memory_recurrent, layer  13: dev = CPU
llama_memory_recurrent, layer  14: dev = CPU
llama_memory_recurrent: layer  15: skipped
llama_memory_recurrent, layer  16: dev = CPU
llama_memory_recurrent, layer  17: dev = CPU
llama_memory_recurrent, layer  18: dev = CPU
llama_memory_recurrent: layer  19: skipped
llama_memory_recurrent, layer  20: dev = CPU
llama_memory_recurrent, layer  21: dev = CPU
llama_memory_recurrent, layer  22: dev = CPU
llama_memory_recurrent: layer  23: skipped
llama_memory_recurrent:        CPU RS buffer size =    96.33 MiB
llama_memory_recurrent: size =   96.33 MiB (     5 cells,  24 layers,  5 seqs  0 rs_seq), R (f32):    6.33 MiB, S (f32):   90.00 MiB, P (f32):    0.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 10240
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 124, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  124, n_seqs =  5, n_outputs =  124
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 125, n_seqs = 5, n_outputs = 124
sched_reserve:        CPU compute buffer size =   118.99 MiB
sched_reserve: graph nodes  = 1411
sched_reserve: graph splits = 1
sched_reserve: reserve took 6.19 ms, sched copies = 1
state_read_meta: cell_count = 59, dest_seq_id = 0
~llama_context:        CPU compute buffer size is 118.9878 MiB, matches expectation of 118.9878 MiB
llama_model_loader: loaded meta data with 37 key-value pairs and 290 tensors from /var/home/rybens/.hermes/models/Spark-X2.5-4B-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = spark2_5
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                     general.sampling.top_k i32              = -1
llama_model_loader: - kv   3:                     general.sampling.top_p f32              = 0.950000
llama_model_loader: - kv   4:                      general.sampling.temp f32              = 1.000000
llama_model_loader: - kv   5:                               general.name str              = Hf_Format
llama_model_loader: - kv   6:                         general.size_label str              = 4.1B
llama_model_loader: - kv   7:                       spark2_5.block_count u32              = 36
llama_model_loader: - kv   8:                    spark2_5.context_length u32              = 1048576
llama_model_loader: - kv   9:                  spark2_5.embedding_length u32              = 2560
llama_model_loader: - kv  10:               spark2_5.feed_forward_length u32              = 10240
llama_model_loader: - kv  11:              spark2_5.attention.head_count u32              = 16
llama_model_loader: - kv  12:           spark2_5.attention.head_count_kv u32              = 4
llama_model_loader: - kv  13:                    spark2_5.rope.freq_base f32              = 5000000.000000
llama_model_loader: - kv  14:                spark2_5.rope.freq_base_swa f32              = 10000.000000
llama_model_loader: - kv  15:  spark2_5.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  16:              spark2_5.attention.key_length u32              = 256
llama_model_loader: - kv  17:            spark2_5.attention.value_length u32              = 256
llama_model_loader: - kv  18:                        spark2_5.vocab_size u32              = 131072
llama_model_loader: - kv  19:          spark2_5.attention.sliding_window u32              = 512
llama_model_loader: - kv  20:  spark2_5.attention.sliding_window_pattern arr[bool,36]     = [true, true, true, false, true, true,...
llama_model_loader: - kv  21:              spark2_5.rope.dimension_count u32              = 64
llama_model_loader: - kv  22:          spark2_5.rope.dimension_count_swa u32              = 256
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = spark2_5
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,131072]  = ["<｜start▁of▁sentence｜>", "<...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,131072]  = [3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 4, 3, ...
llama_model_loader: - kv  27:                      tokenizer.ggml.merges arr[str,130716]  = ["Ġ Ġ", "Ġ t", "i n", "Ġ a", "Ġ...
llama_model_loader: - kv  28:                tokenizer.ggml.bos_token_id u32              = 0
llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  30:            tokenizer.ggml.unknown_token_id u32              = 5
llama_model_loader: - kv  31:            tokenizer.ggml.padding_token_id u32              = 2
llama_model_loader: - kv  32:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  33:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  34:                    tokenizer.chat_template str              = {#- 0826版本 -#}\n{%- if not message...
llama_model_loader: - kv  35:               general.quantization_version u32              = 2
llama_model_loader: - kv  36:                          general.file_type u32              = 7
llama_model_loader: - type  f32:   73 tensors
llama_model_loader: - type q8_0:  217 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q8_0
print_info: file size   = 4.07 GiB (8.50 BPW) 
init_tokenizer: initializing tokenizer for type 2
load: 86 unused tokens
load: control token: 130976 '<|Bot|>' is not marked as EOG
load: control token: 130975 '<|Developer|>' is not marked as EOG
load: control token: 130973 '<|User|>' is not marked as EOG
load: control token: 130974 '<|Tool|>' is not marked as EOG
load: control token:     13 '<｜fim▁end｜>' is not marked as EOG
load: control token:      0 '<｜start▁of▁sentence｜>' is not marked as EOG
load: control token: 130972 '<|System|>' is not marked as EOG
load: control token:      2 '<｜▁pad▁｜>' is not marked as EOG
load: control token:      5 '<unk>' is not marked as EOG
load: control token:     12 '<｜fim▁hole｜>' is not marked as EOG
load: control token:      6 '<｜start▁of▁text｜>' is not marked as EOG
load: control token:      7 '<｜end▁of▁text｜>' is not marked as EOG
load: control token:     11 '<｜fim▁begin｜>' is not marked as EOG
load: printing all EOG tokens:
load:   - 1 ('<｜end▁of▁sentence｜>')
load: special tokens cache size = 113
load: token to piece cache size = 0.7975 MB
print_info: arch                  = spark2_5
print_info: vocab_only            = 0
print_info: no_alloc              = 0
print_info: n_ctx_train           = 1048576
print_info: n_embd_inp            = 2560
print_info: n_embd                = 2560
print_info: n_embd_out            = 2560
print_info: n_layer               = 36
print_info: n_layer_all           = 36
print_info: n_head                = 16
print_info: n_head_kv             = 4
print_info: n_rot                 = 64
print_info: n_swa                 = 512
print_info: is_swa_any            = 1
print_info: non_causal_type       = 0
print_info: n_embd_head_k         = 256
print_info: n_embd_head_v         = 256
print_info: n_gqa                 = 4
print_info: n_embd_k_gqa          = 1024
print_info: n_embd_v_gqa          = 1024
print_info: f_norm_eps            = 0.0e+00
print_info: f_norm_rms_eps        = 1.0e-06
print_info: f_clamp_kqv           = 0.0e+00
print_info: f_max_alibi_bias      = 0.0e+00
print_info: f_logit_scale         = 0.0e+00
print_info: f_attn_scale          = 0.0e+00
print_info: f_attn_value_scale    = 0.0000
print_info: n_ff                  = 10240
print_info: n_expert              = 0
print_info: n_expert_used         = 0
print_info: n_expert_groups       = 0
print_info: n_group_used          = 0
print_info: causal attn           = 1
print_info: pooling type          = -1
print_info: rope type             = 2
print_info: rope scaling          = linear
print_info: freq_base_train       = 5000000.0
print_info: freq_scale_train      = 1
print_info: freq_base_swa         = 10000.0
print_info: freq_scale_swa        = 1
print_info: n_embd_head_k_swa     = 256
print_info: n_embd_head_v_swa     = 256
print_info: n_rot_swa             = 256
print_info: n_ctx_orig_yarn       = 1048576
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: model type            = ?B
print_info: model params          = 4.11 B
print_info: general.name          = Hf_Format
print_info: vocab type            = BPE
print_info: n_vocab               = 131072
print_info: n_merges              = 130716
print_info: BOS token             = 0 '<｜start▁of▁sentence｜>'
print_info: EOS token             = 1 '<｜end▁of▁sentence｜>'
print_info: EOT token             = 1 '<｜end▁of▁sentence｜>'
print_info: UNK token             = 5 '<unk>'
print_info: PAD token             = 2 '<｜▁pad▁｜>'
print_info: LF token              = 198 'Ċ'
print_info: FIM PRE token         = 11 '<｜fim▁begin｜>'
print_info: FIM SUF token         = 12 '<｜fim▁hole｜>'
print_info: FIM MID token         = 13 '<｜fim▁end｜>'
print_info: EOG token             = 1 '<｜end▁of▁sentence｜>'
print_info: max token length      = 210
load_tensors: loading model tensors, this can take a while... (load_mode = mmap)
load_tensors: layer   0 assigned to device CPU, is_swa = 1
load_tensors: layer   1 assigned to device CPU, is_swa = 1
load_tensors: layer   2 assigned to device CPU, is_swa = 1
load_tensors: layer   3 assigned to device CPU, is_swa = 0
load_tensors: layer   4 assigned to device CPU, is_swa = 1
load_tensors: layer   5 assigned to device CPU, is_swa = 1
load_tensors: layer   6 assigned to device CPU, is_swa = 1
load_tensors: layer   7 assigned to device CPU, is_swa = 0
load_tensors: layer   8 assigned to device CPU, is_swa = 1
load_tensors: layer   9 assigned to device CPU, is_swa = 1
load_tensors: layer  10 assigned to device CPU, is_swa = 1
load_tensors: layer  11 assigned to device CPU, is_swa = 0
load_tensors: layer  12 assigned to device CPU, is_swa = 1
load_tensors: layer  13 assigned to device CPU, is_swa = 1
load_tensors: layer  14 assigned to device CPU, is_swa = 1
load_tensors: layer  15 assigned to device CPU, is_swa = 0
load_tensors: layer  16 assigned to device CPU, is_swa = 1
load_tensors: layer  17 assigned to device CPU, is_swa = 1
load_tensors: layer  18 assigned to device CPU, is_swa = 1
load_tensors: layer  19 assigned to device CPU, is_swa = 0
load_tensors: layer  20 assigned to device CPU, is_swa = 1
load_tensors: layer  21 assigned to device CPU, is_swa = 1
load_tensors: layer  22 assigned to device CPU, is_swa = 1
load_tensors: layer  23 assigned to device CPU, is_swa = 0
load_tensors: layer  24 assigned to device CPU, is_swa = 1
load_tensors: layer  25 assigned to device CPU, is_swa = 1
load_tensors: layer  26 assigned to device CPU, is_swa = 1
load_tensors: layer  27 assigned to device CPU, is_swa = 0
load_tensors: layer  28 assigned to device CPU, is_swa = 1
load_tensors: layer  29 assigned to device CPU, is_swa = 1
load_tensors: layer  30 assigned to device CPU, is_swa = 1
load_tensors: layer  31 assigned to device CPU, is_swa = 0
load_tensors: layer  32 assigned to device CPU, is_swa = 1
load_tensors: layer  33 assigned to device CPU, is_swa = 1
load_tensors: layer  34 assigned to device CPU, is_swa = 1
load_tensors: layer  35 assigned to device CPU, is_swa = 0
load_tensors: layer  36 assigned to device CPU, is_swa = 0
create_tensor: loading tensor token_embd.weight
create_tensor: loading tensor output_norm.weight
create_tensor: loading tensor output.weight
create_tensor: loading tensor token_embd.weight
create_tensor: loading tensor blk.0.attn_norm.weight
create_tensor: loading tensor blk.0.attn_qkv.weight
create_tensor: loading tensor blk.0.attn_qkv.bias
create_tensor: loading tensor blk.0.attn_q.bias
create_tensor: loading tensor blk.0.attn_k.bias
create_tensor: loading tensor blk.0.attn_v.bias
create_tensor: loading tensor blk.0.attn_gate.weight
create_tensor: loading tensor blk.0.attn_output.weight
create_tensor: loading tensor blk.0.ffn_norm.weight
create_tensor: loading tensor blk.0.ffn_gate.weight
create_tensor: loading tensor blk.0.ffn_up.weight
create_tensor: loading tensor blk.0.ffn_down.weight
create_tensor: loading tensor blk.1.attn_norm.weight
create_tensor: loading tensor blk.1.attn_qkv.weight
create_tensor: loading tensor blk.1.attn_qkv.bias
create_tensor: loading tensor blk.1.attn_q.bias
create_tensor: loading tensor blk.1.attn_k.bias
create_tensor: loading tensor blk.1.attn_v.bias
create_tensor: loading tensor blk.1.attn_gate.weight
create_tensor: loading tensor blk.1.attn_output.weight
create_tensor: loading tensor blk.1.ffn_norm.weight
create_tensor: loading tensor blk.1.ffn_gate.weight
create_tensor: loading tensor blk.1.ffn_up.weight
create_tensor: loading tensor blk.1.ffn_down.weight
create_tensor: loading tensor blk.2.attn_norm.weight
create_tensor: loading tensor blk.2.attn_qkv.weight
create_tensor: loading tensor blk.2.attn_qkv.bias
create_tensor: loading tensor blk.2.attn_q.bias
create_tensor: loading tensor blk.2.attn_k.bias
create_tensor: loading tensor blk.2.attn_v.bias
create_tensor: loading tensor blk.2.attn_gate.weight
create_tensor: loading tensor blk.2.attn_output.weight
create_tensor: loading tensor blk.2.ffn_norm.weight
create_tensor: loading tensor blk.2.ffn_gate.weight
create_tensor: loading tensor blk.2.ffn_up.weight
create_tensor: loading tensor blk.2.ffn_down.weight
create_tensor: loading tensor blk.3.attn_norm.weight
create_tensor: loading tensor blk.3.attn_qkv.weight
create_tensor: loading tensor blk.3.attn_qkv.bias
create_tensor: loading tensor blk.3.attn_q.bias
create_tensor: loading tensor blk.3.attn_k.bias
create_tensor: loading tensor blk.3.attn_v.bias
create_tensor: loading tensor blk.3.attn_gate.weight
create_tensor: loading tensor blk.3.attn_output.weight
create_tensor: loading tensor blk.3.ffn_norm.weight
create_tensor: loading tensor blk.3.ffn_gate.weight
create_tensor: loading tensor blk.3.ffn_up.weight
create_tensor: loading tensor blk.3.ffn_down.weight
create_tensor: loading tensor blk.4.attn_norm.weight
create_tensor: loading tensor blk.4.attn_qkv.weight
create_tensor: loading tensor blk.4.attn_qkv.bias
create_tensor: loading tensor blk.4.attn_q.bias
create_tensor: loading tensor blk.4.attn_k.bias
create_tensor: loading tensor blk.4.attn_v.bias
create_tensor: loading tensor blk.4.attn_gate.weight
create_tensor: loading tensor blk.4.attn_output.weight
create_tensor: loading tensor blk.4.ffn_norm.weight
create_tensor: loading tensor blk.4.ffn_gate.weight
create_tensor: loading tensor blk.4.ffn_up.weight
create_tensor: loading tensor blk.4.ffn_down.weight
create_tensor: loading tensor blk.5.attn_norm.weight
create_tensor: loading tensor blk.5.attn_qkv.weight
create_tensor: loading tensor blk.5.attn_qkv.bias
create_tensor: loading tensor blk.5.attn_q.bias
create_tensor: loading tensor blk.5.attn_k.bias
create_tensor: loading tensor blk.5.attn_v.bias
create_tensor: loading tensor blk.5.attn_gate.weight
create_tensor: loading tensor blk.5.attn_output.weight
create_tensor: loading tensor blk.5.ffn_norm.weight
create_tensor: loading tensor blk.5.ffn_gate.weight
create_tensor: loading tensor blk.5.ffn_up.weight
create_tensor: loading tensor blk.5.ffn_down.weight
create_tensor: loading tensor blk.6.attn_norm.weight
create_tensor: loading tensor blk.6.attn_qkv.weight
create_tensor: loading tensor blk.6.attn_qkv.bias
create_tensor: loading tensor blk.6.attn_q.bias
create_tensor: loading tensor blk.6.attn_k.bias
create_tensor: loading tensor blk.6.attn_v.bias
create_tensor: loading tensor blk.6.attn_gate.weight
create_tensor: loading tensor blk.6.attn_output.weight
create_tensor: loading tensor blk.6.ffn_norm.weight
create_tensor: loading tensor blk.6.ffn_gate.weight
create_tensor: loading tensor blk.6.ffn_up.weight
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create_tensor: loading tensor blk.6.attn_gate.scale
create_tensor: loading tensor blk.6.ffn_gate.scale
create_tensor: loading tensor blk.6.ffn_down.scale
create_tensor: loading tensor blk.6.ffn_up.scale
create_tensor: loading tensor blk.6.attn_output.input_scale
create_tensor: loading tensor blk.6.attn_qkv.input_scale
create_tensor: loading tensor blk.6.attn_gate.input_scale
create_tensor: loading tensor blk.6.ffn_gate.input_scale
create_tensor: loading tensor blk.6.ffn_down.input_scale
create_tensor: loading tensor blk.6.ffn_up.input_scale
create_tensor: loading tensor blk.7.attn_output.scale
create_tensor: loading tensor blk.7.attn_qkv.scale
create_tensor: loading tensor blk.7.attn_gate.scale
create_tensor: loading tensor blk.7.ffn_gate.scale
create_tensor: loading tensor blk.7.ffn_down.scale
create_tensor: loading tensor blk.7.ffn_up.scale
create_tensor: loading tensor blk.7.attn_output.input_scale
create_tensor: loading tensor blk.7.attn_qkv.input_scale
create_tensor: loading tensor blk.7.attn_gate.input_scale
create_tensor: loading tensor blk.7.ffn_gate.input_scale
create_tensor: loading tensor blk.7.ffn_down.input_scale
create_tensor: loading tensor blk.7.ffn_up.input_scale
create_tensor: loading tensor blk.8.attn_output.scale
create_tensor: loading tensor blk.8.attn_qkv.scale
create_tensor: loading tensor blk.8.attn_gate.scale
create_tensor: loading tensor blk.8.ffn_gate.scale
create_tensor: loading tensor blk.8.ffn_down.scale
create_tensor: loading tensor blk.8.ffn_up.scale
create_tensor: loading tensor blk.8.attn_output.input_scale
create_tensor: loading tensor blk.8.attn_qkv.input_scale
create_tensor: loading tensor blk.8.attn_gate.input_scale
create_tensor: loading tensor blk.8.ffn_gate.input_scale
create_tensor: loading tensor blk.8.ffn_down.input_scale
create_tensor: loading tensor blk.8.ffn_up.input_scale
create_tensor: loading tensor blk.9.attn_output.scale
create_tensor: loading tensor blk.9.attn_qkv.scale
create_tensor: loading tensor blk.9.attn_gate.scale
create_tensor: loading tensor blk.9.ffn_gate.scale
create_tensor: loading tensor blk.9.ffn_down.scale
create_tensor: loading tensor blk.9.ffn_up.scale
create_tensor: loading tensor blk.9.attn_output.input_scale
create_tensor: loading tensor blk.9.attn_qkv.input_scale
create_tensor: loading tensor blk.9.attn_gate.input_scale
create_tensor: loading tensor blk.9.ffn_gate.input_scale
create_tensor: loading tensor blk.9.ffn_down.input_scale
create_tensor: loading tensor blk.9.ffn_up.input_scale
create_tensor: loading tensor blk.10.attn_output.scale
create_tensor: loading tensor blk.10.attn_qkv.scale
create_tensor: loading tensor blk.10.attn_gate.scale
create_tensor: loading tensor blk.10.ffn_gate.scale
create_tensor: loading tensor blk.10.ffn_down.scale
create_tensor: loading tensor blk.10.ffn_up.scale
create_tensor: loading tensor blk.10.attn_output.input_scale
create_tensor: loading tensor blk.10.attn_qkv.input_scale
create_tensor: loading tensor blk.10.attn_gate.input_scale
create_tensor: loading tensor blk.10.ffn_gate.input_scale
create_tensor: loading tensor blk.10.ffn_down.input_scale
create_tensor: loading tensor blk.10.ffn_up.input_scale
create_tensor: loading tensor blk.11.attn_output.scale
create_tensor: loading tensor blk.11.attn_qkv.scale
create_tensor: loading tensor blk.11.attn_gate.scale
create_tensor: loading tensor blk.11.ffn_gate.scale
create_tensor: loading tensor blk.11.ffn_down.scale
create_tensor: loading tensor blk.11.ffn_up.scale
create_tensor: loading tensor blk.11.attn_output.input_scale
create_tensor: loading tensor blk.11.attn_qkv.input_scale
create_tensor: loading tensor blk.11.attn_gate.input_scale
create_tensor: loading tensor blk.11.ffn_gate.input_scale
create_tensor: loading tensor blk.11.ffn_down.input_scale
create_tensor: loading tensor blk.11.ffn_up.input_scale
create_tensor: loading tensor blk.12.attn_output.scale
create_tensor: loading tensor blk.12.attn_qkv.scale
create_tensor: loading tensor blk.12.attn_gate.scale
create_tensor: loading tensor blk.12.ffn_gate.scale
create_tensor: loading tensor blk.12.ffn_down.scale
create_tensor: loading tensor blk.12.ffn_up.scale
create_tensor: loading tensor blk.12.attn_output.input_scale
create_tensor: loading tensor blk.12.attn_qkv.input_scale
create_tensor: loading tensor blk.12.attn_gate.input_scale
create_tensor: loading tensor blk.12.ffn_gate.input_scale
create_tensor: loading tensor blk.12.ffn_down.input_scale
create_tensor: loading tensor blk.12.ffn_up.input_scale
create_tensor: loading tensor blk.13.attn_output.scale
create_tensor: loading tensor blk.13.attn_qkv.scale
create_tensor: loading tensor blk.13.attn_gate.scale
create_tensor: loading tensor blk.13.ffn_gate.scale
create_tensor: loading tensor blk.13.ffn_down.scale
create_tensor: loading tensor blk.13.ffn_up.scale
create_tensor: loading tensor blk.13.attn_output.input_scale
create_tensor: loading tensor blk.13.attn_qkv.input_scale
create_tensor: loading tensor blk.13.attn_gate.input_scale
create_tensor: loading tensor blk.13.ffn_gate.input_scale
create_tensor: loading tensor blk.13.ffn_down.input_scale
create_tensor: loading tensor blk.13.ffn_up.input_scale
create_tensor: loading tensor blk.14.attn_output.scale
create_tensor: loading tensor blk.14.attn_qkv.scale
create_tensor: loading tensor blk.14.attn_gate.scale
create_tensor: loading tensor blk.14.ffn_gate.scale
create_tensor: loading tensor blk.14.ffn_down.scale
create_tensor: loading tensor blk.14.ffn_up.scale
create_tensor: loading tensor blk.14.attn_output.input_scale
create_tensor: loading tensor blk.14.attn_qkv.input_scale
create_tensor: loading tensor blk.14.attn_gate.input_scale
create_tensor: loading tensor blk.14.ffn_gate.input_scale
create_tensor: loading tensor blk.14.ffn_down.input_scale
create_tensor: loading tensor blk.14.ffn_up.input_scale
create_tensor: loading tensor blk.15.attn_output.scale
create_tensor: loading tensor blk.15.attn_qkv.scale
create_tensor: loading tensor blk.15.attn_gate.scale
create_tensor: loading tensor blk.15.ffn_gate.scale
create_tensor: loading tensor blk.15.ffn_down.scale
create_tensor: loading tensor blk.15.ffn_up.scale
create_tensor: loading tensor blk.15.attn_output.input_scale
create_tensor: loading tensor blk.15.attn_qkv.input_scale
create_tensor: loading tensor blk.15.attn_gate.input_scale
create_tensor: loading tensor blk.15.ffn_gate.input_scale
create_tensor: loading tensor blk.15.ffn_down.input_scale
create_tensor: loading tensor blk.15.ffn_up.input_scale
create_tensor: loading tensor blk.16.attn_output.scale
create_tensor: loading tensor blk.16.attn_qkv.scale
create_tensor: loading tensor blk.16.attn_gate.scale
create_tensor: loading tensor blk.16.ffn_gate.scale
create_tensor: loading tensor blk.16.ffn_down.scale
create_tensor: loading tensor blk.16.ffn_up.scale
create_tensor: loading tensor blk.16.attn_output.input_scale
create_tensor: loading tensor blk.16.attn_qkv.input_scale
create_tensor: loading tensor blk.16.attn_gate.input_scale
create_tensor: loading tensor blk.16.ffn_gate.input_scale
create_tensor: loading tensor blk.16.ffn_down.input_scale
create_tensor: loading tensor blk.16.ffn_up.input_scale
create_tensor: loading tensor blk.17.attn_output.scale
create_tensor: loading tensor blk.17.attn_qkv.scale
create_tensor: loading tensor blk.17.attn_gate.scale
create_tensor: loading tensor blk.17.ffn_gate.scale
create_tensor: loading tensor blk.17.ffn_down.scale
create_tensor: loading tensor blk.17.ffn_up.scale
create_tensor: loading tensor blk.17.attn_output.input_scale
create_tensor: loading tensor blk.17.attn_qkv.input_scale
create_tensor: loading tensor blk.17.attn_gate.input_scale
create_tensor: loading tensor blk.17.ffn_gate.input_scale
create_tensor: loading tensor blk.17.ffn_down.input_scale
create_tensor: loading tensor blk.17.ffn_up.input_scale
create_tensor: loading tensor blk.18.attn_output.scale
create_tensor: loading tensor blk.18.attn_qkv.scale
create_tensor: loading tensor blk.18.attn_gate.scale
create_tensor: loading tensor blk.18.ffn_gate.scale
create_tensor: loading tensor blk.18.ffn_down.scale
create_tensor: loading tensor blk.18.ffn_up.scale
create_tensor: loading tensor blk.18.attn_output.input_scale
create_tensor: loading tensor blk.18.attn_qkv.input_scale
create_tensor: loading tensor blk.18.attn_gate.input_scale
create_tensor: loading tensor blk.18.ffn_gate.input_scale
create_tensor: loading tensor blk.18.ffn_down.input_scale
create_tensor: loading tensor blk.18.ffn_up.input_scale
create_tensor: loading tensor blk.19.attn_output.scale
create_tensor: loading tensor blk.19.attn_qkv.scale
create_tensor: loading tensor blk.19.attn_gate.scale
create_tensor: loading tensor blk.19.ffn_gate.scale
create_tensor: loading tensor blk.19.ffn_down.scale
create_tensor: loading tensor blk.19.ffn_up.scale
create_tensor: loading tensor blk.19.attn_output.input_scale
create_tensor: loading tensor blk.19.attn_qkv.input_scale
create_tensor: loading tensor blk.19.attn_gate.input_scale
create_tensor: loading tensor blk.19.ffn_gate.input_scale
create_tensor: loading tensor blk.19.ffn_down.input_scale
create_tensor: loading tensor blk.19.ffn_up.input_scale
create_tensor: loading tensor blk.20.attn_output.scale
create_tensor: loading tensor blk.20.attn_qkv.scale
create_tensor: loading tensor blk.20.attn_gate.scale
create_tensor: loading tensor blk.20.ffn_gate.scale
create_tensor: loading tensor blk.20.ffn_down.scale
create_tensor: loading tensor blk.20.ffn_up.scale
create_tensor: loading tensor blk.20.attn_output.input_scale
create_tensor: loading tensor blk.20.attn_qkv.input_scale
create_tensor: loading tensor blk.20.attn_gate.input_scale
create_tensor: loading tensor blk.20.ffn_gate.input_scale
create_tensor: loading tensor blk.20.ffn_down.input_scale
create_tensor: loading tensor blk.20.ffn_up.input_scale
create_tensor: loading tensor blk.21.attn_output.scale
create_tensor: loading tensor blk.21.attn_qkv.scale
create_tensor: loading tensor blk.21.attn_gate.scale
create_tensor: loading tensor blk.21.ffn_gate.scale
create_tensor: loading tensor blk.21.ffn_down.scale
create_tensor: loading tensor blk.21.ffn_up.scale
create_tensor: loading tensor blk.21.attn_output.input_scale
create_tensor: loading tensor blk.21.attn_qkv.input_scale
create_tensor: loading tensor blk.21.attn_gate.input_scale
create_tensor: loading tensor blk.21.ffn_gate.input_scale
create_tensor: loading tensor blk.21.ffn_down.input_scale
create_tensor: loading tensor blk.21.ffn_up.input_scale
create_tensor: loading tensor blk.22.attn_output.scale
create_tensor: loading tensor blk.22.attn_qkv.scale
create_tensor: loading tensor blk.22.attn_gate.scale
create_tensor: loading tensor blk.22.ffn_gate.scale
create_tensor: loading tensor blk.22.ffn_down.scale
create_tensor: loading tensor blk.22.ffn_up.scale
create_tensor: loading tensor blk.22.attn_output.input_scale
create_tensor: loading tensor blk.22.attn_qkv.input_scale
create_tensor: loading tensor blk.22.attn_gate.input_scale
create_tensor: loading tensor blk.22.ffn_gate.input_scale
create_tensor: loading tensor blk.22.ffn_down.input_scale
create_tensor: loading tensor blk.22.ffn_up.input_scale
create_tensor: loading tensor blk.23.attn_output.scale
create_tensor: loading tensor blk.23.attn_qkv.scale
create_tensor: loading tensor blk.23.attn_gate.scale
create_tensor: loading tensor blk.23.ffn_gate.scale
create_tensor: loading tensor blk.23.ffn_down.scale
create_tensor: loading tensor blk.23.ffn_up.scale
create_tensor: loading tensor blk.23.attn_output.input_scale
create_tensor: loading tensor blk.23.attn_qkv.input_scale
create_tensor: loading tensor blk.23.attn_gate.input_scale
create_tensor: loading tensor blk.23.ffn_gate.input_scale
create_tensor: loading tensor blk.23.ffn_down.input_scale
create_tensor: loading tensor blk.23.ffn_up.input_scale
create_tensor: loading tensor blk.24.attn_output.scale
create_tensor: loading tensor blk.24.attn_qkv.scale
create_tensor: loading tensor blk.24.attn_gate.scale
create_tensor: loading tensor blk.24.ffn_gate.scale
create_tensor: loading tensor blk.24.ffn_down.scale
create_tensor: loading tensor blk.24.ffn_up.scale
create_tensor: loading tensor blk.24.attn_output.input_scale
create_tensor: loading tensor blk.24.attn_qkv.input_scale
create_tensor: loading tensor blk.24.attn_gate.input_scale
create_tensor: loading tensor blk.24.ffn_gate.input_scale
create_tensor: loading tensor blk.24.ffn_down.input_scale
create_tensor: loading tensor blk.24.ffn_up.input_scale
create_tensor: loading tensor blk.25.attn_output.scale
create_tensor: loading tensor blk.25.attn_qkv.scale
create_tensor: loading tensor blk.25.attn_gate.scale
create_tensor: loading tensor blk.25.ffn_gate.scale
create_tensor: loading tensor blk.25.ffn_down.scale
create_tensor: loading tensor blk.25.ffn_up.scale
create_tensor: loading tensor blk.25.attn_output.input_scale
create_tensor: loading tensor blk.25.attn_qkv.input_scale
create_tensor: loading tensor blk.25.attn_gate.input_scale
create_tensor: loading tensor blk.25.ffn_gate.input_scale
create_tensor: loading tensor blk.25.ffn_down.input_scale
create_tensor: loading tensor blk.25.ffn_up.input_scale
create_tensor: loading tensor blk.26.attn_output.scale
create_tensor: loading tensor blk.26.attn_qkv.scale
create_tensor: loading tensor blk.26.attn_gate.scale
create_tensor: loading tensor blk.26.ffn_gate.scale
create_tensor: loading tensor blk.26.ffn_down.scale
create_tensor: loading tensor blk.26.ffn_up.scale
create_tensor: loading tensor blk.26.attn_output.input_scale
create_tensor: loading tensor blk.26.attn_qkv.input_scale
create_tensor: loading tensor blk.26.attn_gate.input_scale
create_tensor: loading tensor blk.26.ffn_gate.input_scale
create_tensor: loading tensor blk.26.ffn_down.input_scale
create_tensor: loading tensor blk.26.ffn_up.input_scale
create_tensor: loading tensor blk.27.attn_output.scale
create_tensor: loading tensor blk.27.attn_qkv.scale
create_tensor: loading tensor blk.27.attn_gate.scale
create_tensor: loading tensor blk.27.ffn_gate.scale
create_tensor: loading tensor blk.27.ffn_down.scale
create_tensor: loading tensor blk.27.ffn_up.scale
create_tensor: loading tensor blk.27.attn_output.input_scale
create_tensor: loading tensor blk.27.attn_qkv.input_scale
create_tensor: loading tensor blk.27.attn_gate.input_scale
create_tensor: loading tensor blk.27.ffn_gate.input_scale
create_tensor: loading tensor blk.27.ffn_down.input_scale
create_tensor: loading tensor blk.27.ffn_up.input_scale
create_tensor: loading tensor blk.28.attn_output.scale
create_tensor: loading tensor blk.28.attn_qkv.scale
create_tensor: loading tensor blk.28.attn_gate.scale
create_tensor: loading tensor blk.28.ffn_gate.scale
create_tensor: loading tensor blk.28.ffn_down.scale
create_tensor: loading tensor blk.28.ffn_up.scale
create_tensor: loading tensor blk.28.attn_output.input_scale
create_tensor: loading tensor blk.28.attn_qkv.input_scale
create_tensor: loading tensor blk.28.attn_gate.input_scale
create_tensor: loading tensor blk.28.ffn_gate.input_scale
create_tensor: loading tensor blk.28.ffn_down.input_scale
create_tensor: loading tensor blk.28.ffn_up.input_scale
create_tensor: loading tensor blk.29.attn_output.scale
create_tensor: loading tensor blk.29.attn_qkv.scale
create_tensor: loading tensor blk.29.attn_gate.scale
create_tensor: loading tensor blk.29.ffn_gate.scale
create_tensor: loading tensor blk.29.ffn_down.scale
create_tensor: loading tensor blk.29.ffn_up.scale
create_tensor: loading tensor blk.29.attn_output.input_scale
create_tensor: loading tensor blk.29.attn_qkv.input_scale
create_tensor: loading tensor blk.29.attn_gate.input_scale
create_tensor: loading tensor blk.29.ffn_gate.input_scale
create_tensor: loading tensor blk.29.ffn_down.input_scale
create_tensor: loading tensor blk.29.ffn_up.input_scale
create_tensor: loading tensor blk.30.attn_output.scale
create_tensor: loading tensor blk.30.attn_qkv.scale
create_tensor: loading tensor blk.30.attn_gate.scale
create_tensor: loading tensor blk.30.ffn_gate.scale
create_tensor: loading tensor blk.30.ffn_down.scale
create_tensor: loading tensor blk.30.ffn_up.scale
create_tensor: loading tensor blk.30.attn_output.input_scale
create_tensor: loading tensor blk.30.attn_qkv.input_scale
create_tensor: loading tensor blk.30.attn_gate.input_scale
create_tensor: loading tensor blk.30.ffn_gate.input_scale
create_tensor: loading tensor blk.30.ffn_down.input_scale
create_tensor: loading tensor blk.30.ffn_up.input_scale
create_tensor: loading tensor blk.31.attn_output.scale
create_tensor: loading tensor blk.31.attn_qkv.scale
create_tensor: loading tensor blk.31.attn_gate.scale
create_tensor: loading tensor blk.31.ffn_gate.scale
create_tensor: loading tensor blk.31.ffn_down.scale
create_tensor: loading tensor blk.31.ffn_up.scale
create_tensor: loading tensor blk.31.attn_output.input_scale
create_tensor: loading tensor blk.31.attn_qkv.input_scale
create_tensor: loading tensor blk.31.attn_gate.input_scale
create_tensor: loading tensor blk.31.ffn_gate.input_scale
create_tensor: loading tensor blk.31.ffn_down.input_scale
create_tensor: loading tensor blk.31.ffn_up.input_scale
create_tensor: loading tensor blk.32.attn_output.scale
create_tensor: loading tensor blk.32.attn_qkv.scale
create_tensor: loading tensor blk.32.attn_gate.scale
create_tensor: loading tensor blk.32.ffn_gate.scale
create_tensor: loading tensor blk.32.ffn_down.scale
create_tensor: loading tensor blk.32.ffn_up.scale
create_tensor: loading tensor blk.32.attn_output.input_scale
create_tensor: loading tensor blk.32.attn_qkv.input_scale
create_tensor: loading tensor blk.32.attn_gate.input_scale
create_tensor: loading tensor blk.32.ffn_gate.input_scale
create_tensor: loading tensor blk.32.ffn_down.input_scale
create_tensor: loading tensor blk.32.ffn_up.input_scale
create_tensor: loading tensor blk.33.attn_output.scale
create_tensor: loading tensor blk.33.attn_qkv.scale
create_tensor: loading tensor blk.33.attn_gate.scale
create_tensor: loading tensor blk.33.ffn_gate.scale
create_tensor: loading tensor blk.33.ffn_down.scale
create_tensor: loading tensor blk.33.ffn_up.scale
create_tensor: loading tensor blk.33.attn_output.input_scale
create_tensor: loading tensor blk.33.attn_qkv.input_scale
create_tensor: loading tensor blk.33.attn_gate.input_scale
create_tensor: loading tensor blk.33.ffn_gate.input_scale
create_tensor: loading tensor blk.33.ffn_down.input_scale
create_tensor: loading tensor blk.33.ffn_up.input_scale
create_tensor: loading tensor blk.34.attn_output.scale
create_tensor: loading tensor blk.34.attn_qkv.scale
create_tensor: loading tensor blk.34.attn_gate.scale
create_tensor: loading tensor blk.34.ffn_gate.scale
create_tensor: loading tensor blk.34.ffn_down.scale
create_tensor: loading tensor blk.34.ffn_up.scale
create_tensor: loading tensor blk.34.attn_output.input_scale
create_tensor: loading tensor blk.34.attn_qkv.input_scale
create_tensor: loading tensor blk.34.attn_gate.input_scale
create_tensor: loading tensor blk.34.ffn_gate.input_scale
create_tensor: loading tensor blk.34.ffn_down.input_scale
create_tensor: loading tensor blk.34.ffn_up.input_scale
create_tensor: loading tensor blk.35.attn_output.scale
create_tensor: loading tensor blk.35.attn_qkv.scale
create_tensor: loading tensor blk.35.attn_gate.scale
create_tensor: loading tensor blk.35.ffn_gate.scale
create_tensor: loading tensor blk.35.ffn_down.scale
create_tensor: loading tensor blk.35.ffn_up.scale
create_tensor: loading tensor blk.35.attn_output.input_scale
create_tensor: loading tensor blk.35.attn_qkv.input_scale
create_tensor: loading tensor blk.35.attn_gate.input_scale
create_tensor: loading tensor blk.35.ffn_gate.input_scale
create_tensor: loading tensor blk.35.ffn_down.input_scale
create_tensor: loading tensor blk.35.ffn_up.input_scale
done_getting_tensors: tensor 'token_embd.weight' (q8_0) (and 290 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
load_tensors: offloading 0 repeating layers to GPU
load_tensors: offloaded 0/37 layers to GPU
load_tensors:   CPU_Mapped model buffer size =  4167.21 MiB
.............................................................................................
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 129
llama_context: n_ubatch              = 129
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 5000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 129
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (1048576) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     2.50 MiB
llama_kv_cache_iswa: using full-size SWA cache (ref: https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
llama_kv_cache_iswa: creating non-SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache: layer  24: filtered
llama_kv_cache: layer  25: filtered
llama_kv_cache: layer  26: filtered
llama_kv_cache: layer  27: dev = CPU
llama_kv_cache: layer  28: filtered
llama_kv_cache: layer  29: filtered
llama_kv_cache: layer  30: filtered
llama_kv_cache: layer  31: dev = CPU
llama_kv_cache: layer  32: filtered
llama_kv_cache: layer  33: filtered
llama_kv_cache: layer  34: filtered
llama_kv_cache: layer  35: dev = CPU
llama_kv_cache:        CPU KV buffer size =     9.00 MiB
llama_kv_cache: size =    9.00 MiB (   256 cells,   9 layers,  5/1 seqs), K (f16):    4.50 MiB, V (f16):    4.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_kv_cache_iswa: creating     SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: dev = CPU
llama_kv_cache: layer   1: dev = CPU
llama_kv_cache: layer   2: dev = CPU
llama_kv_cache: layer   3: filtered
llama_kv_cache: layer   4: dev = CPU
llama_kv_cache: layer   5: dev = CPU
llama_kv_cache: layer   6: dev = CPU
llama_kv_cache: layer   7: filtered
llama_kv_cache: layer   8: dev = CPU
llama_kv_cache: layer   9: dev = CPU
llama_kv_cache: layer  10: dev = CPU
llama_kv_cache: layer  11: filtered
llama_kv_cache: layer  12: dev = CPU
llama_kv_cache: layer  13: dev = CPU
llama_kv_cache: layer  14: dev = CPU
llama_kv_cache: layer  15: filtered
llama_kv_cache: layer  16: dev = CPU
llama_kv_cache: layer  17: dev = CPU
llama_kv_cache: layer  18: dev = CPU
llama_kv_cache: layer  19: filtered
llama_kv_cache: layer  20: dev = CPU
llama_kv_cache: layer  21: dev = CPU
llama_kv_cache: layer  22: dev = CPU
llama_kv_cache: layer  23: filtered
llama_kv_cache: layer  24: dev = CPU
llama_kv_cache: layer  25: dev = CPU
llama_kv_cache: layer  26: dev = CPU
llama_kv_cache: layer  27: filtered
llama_kv_cache: layer  28: dev = CPU
llama_kv_cache: layer  29: dev = CPU
llama_kv_cache: layer  30: dev = CPU
llama_kv_cache: layer  31: filtered
llama_kv_cache: layer  32: dev = CPU
llama_kv_cache: layer  33: dev = CPU
llama_kv_cache: layer  34: dev = CPU
llama_kv_cache: layer  35: filtered
llama_kv_cache:        CPU KV buffer size =    27.00 MiB
llama_kv_cache: size =   27.00 MiB (   256 cells,  27 layers,  5/1 seqs), K (f16):   13.50 MiB, V (f16):   13.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 2320
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 129, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
sched_reserve:        CPU compute buffer size =    67.03 MiB
sched_reserve: graph nodes  = 1266
sched_reserve: graph splits = 1
sched_reserve: reserve took 3.72 ms, sched copies = 1
~llama_context:        CPU compute buffer size is  67.0298 MiB, matches expectation of  67.0298 MiB
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 129
llama_context: n_ubatch              = 129
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 5000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 129
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (1048576) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     2.50 MiB
llama_kv_cache_iswa: using full-size SWA cache (ref: https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
llama_kv_cache_iswa: creating non-SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache: layer  24: filtered
llama_kv_cache: layer  25: filtered
llama_kv_cache: layer  26: filtered
llama_kv_cache: layer  27: dev = CPU
llama_kv_cache: layer  28: filtered
llama_kv_cache: layer  29: filtered
llama_kv_cache: layer  30: filtered
llama_kv_cache: layer  31: dev = CPU
llama_kv_cache: layer  32: filtered
llama_kv_cache: layer  33: filtered
llama_kv_cache: layer  34: filtered
llama_kv_cache: layer  35: dev = CPU
llama_kv_cache:        CPU KV buffer size =     9.00 MiB
llama_kv_cache: size =    9.00 MiB (   256 cells,   9 layers,  5/1 seqs), K (f16):    4.50 MiB, V (f16):    4.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_kv_cache_iswa: creating     SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: dev = CPU
llama_kv_cache: layer   1: dev = CPU
llama_kv_cache: layer   2: dev = CPU
llama_kv_cache: layer   3: filtered
llama_kv_cache: layer   4: dev = CPU
llama_kv_cache: layer   5: dev = CPU
llama_kv_cache: layer   6: dev = CPU
llama_kv_cache: layer   7: filtered
llama_kv_cache: layer   8: dev = CPU
llama_kv_cache: layer   9: dev = CPU
llama_kv_cache: layer  10: dev = CPU
llama_kv_cache: layer  11: filtered
llama_kv_cache: layer  12: dev = CPU
llama_kv_cache: layer  13: dev = CPU
llama_kv_cache: layer  14: dev = CPU
llama_kv_cache: layer  15: filtered
llama_kv_cache: layer  16: dev = CPU
llama_kv_cache: layer  17: dev = CPU
llama_kv_cache: layer  18: dev = CPU
llama_kv_cache: layer  19: filtered
llama_kv_cache: layer  20: dev = CPU
llama_kv_cache: layer  21: dev = CPU
llama_kv_cache: layer  22: dev = CPU
llama_kv_cache: layer  23: filtered
llama_kv_cache: layer  24: dev = CPU
llama_kv_cache: layer  25: dev = CPU
llama_kv_cache: layer  26: dev = CPU
llama_kv_cache: layer  27: filtered
llama_kv_cache: layer  28: dev = CPU
llama_kv_cache: layer  29: dev = CPU
llama_kv_cache: layer  30: dev = CPU
llama_kv_cache: layer  31: filtered
llama_kv_cache: layer  32: dev = CPU
llama_kv_cache: layer  33: dev = CPU
llama_kv_cache: layer  34: dev = CPU
llama_kv_cache: layer  35: filtered
llama_kv_cache:        CPU KV buffer size =    27.00 MiB
llama_kv_cache: size =   27.00 MiB (   256 cells,  27 layers,  5/1 seqs), K (f16):   13.50 MiB, V (f16):   13.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 2320
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 129, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
sched_reserve:        CPU compute buffer size =    67.03 MiB
sched_reserve: graph nodes  = 1266
sched_reserve: graph splits = 1
sched_reserve: reserve took 3.73 ms, sched copies = 1
state_read_meta: cell_count = 61, dest_seq_id = 0
state_read_meta: cell_count = 61, dest_seq_id = 0
~llama_context:        CPU compute buffer size is  67.0298 MiB, matches expectation of  67.0298 MiB
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 129
llama_context: n_ubatch              = 129
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 5000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 129
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (1048576) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     2.50 MiB
llama_kv_cache_iswa: using full-size SWA cache (ref: https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
llama_kv_cache_iswa: creating non-SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache: layer  24: filtered
llama_kv_cache: layer  25: filtered
llama_kv_cache: layer  26: filtered
llama_kv_cache: layer  27: dev = CPU
llama_kv_cache: layer  28: filtered
llama_kv_cache: layer  29: filtered
llama_kv_cache: layer  30: filtered
llama_kv_cache: layer  31: dev = CPU
llama_kv_cache: layer  32: filtered
llama_kv_cache: layer  33: filtered
llama_kv_cache: layer  34: filtered
llama_kv_cache: layer  35: dev = CPU
llama_kv_cache:        CPU KV buffer size =     9.00 MiB
llama_kv_cache: size =    9.00 MiB (   256 cells,   9 layers,  5/1 seqs), K (f16):    4.50 MiB, V (f16):    4.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_kv_cache_iswa: creating     SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: dev = CPU
llama_kv_cache: layer   1: dev = CPU
llama_kv_cache: layer   2: dev = CPU
llama_kv_cache: layer   3: filtered
llama_kv_cache: layer   4: dev = CPU
llama_kv_cache: layer   5: dev = CPU
llama_kv_cache: layer   6: dev = CPU
llama_kv_cache: layer   7: filtered
llama_kv_cache: layer   8: dev = CPU
llama_kv_cache: layer   9: dev = CPU
llama_kv_cache: layer  10: dev = CPU
llama_kv_cache: layer  11: filtered
llama_kv_cache: layer  12: dev = CPU
llama_kv_cache: layer  13: dev = CPU
llama_kv_cache: layer  14: dev = CPU
llama_kv_cache: layer  15: filtered
llama_kv_cache: layer  16: dev = CPU
llama_kv_cache: layer  17: dev = CPU
llama_kv_cache: layer  18: dev = CPU
llama_kv_cache: layer  19: filtered
llama_kv_cache: layer  20: dev = CPU
llama_kv_cache: layer  21: dev = CPU
llama_kv_cache: layer  22: dev = CPU
llama_kv_cache: layer  23: filtered
llama_kv_cache: layer  24: dev = CPU
llama_kv_cache: layer  25: dev = CPU
llama_kv_cache: layer  26: dev = CPU
llama_kv_cache: layer  27: filtered
llama_kv_cache: layer  28: dev = CPU
llama_kv_cache: layer  29: dev = CPU
llama_kv_cache: layer  30: dev = CPU
llama_kv_cache: layer  31: filtered
llama_kv_cache: layer  32: dev = CPU
llama_kv_cache: layer  33: dev = CPU
llama_kv_cache: layer  34: dev = CPU
llama_kv_cache: layer  35: filtered
llama_kv_cache:        CPU KV buffer size =    27.00 MiB
llama_kv_cache: size =   27.00 MiB (   256 cells,  27 layers,  5/1 seqs), K (f16):   13.50 MiB, V (f16):   13.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 2320
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 129, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
sched_reserve:        CPU compute buffer size =    67.03 MiB
sched_reserve: graph nodes  = 1266
sched_reserve: graph splits = 1
sched_reserve: reserve took 4.45 ms, sched copies = 1
state_read_meta: cell_count = 61, dest_seq_id = 0
state_read_meta: cell_count = 61, dest_seq_id = 0
~llama_context:        CPU compute buffer size is  67.0298 MiB, matches expectation of  67.0298 MiB
llama_context: constructing llama_context
llama_context: n_seq_max             = 5
llama_context: n_ctx                 = 256
llama_context: n_ctx_seq             = 256
llama_context: n_batch               = 129
llama_context: n_ubatch              = 129
llama_context: causal_attn           = 1
llama_context: flash_attn            = auto
llama_context: kv_unified            = true
llama_context: freq_base             = 5000000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 129
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (1048576) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     2.50 MiB
llama_kv_cache_iswa: using full-size SWA cache (ref: https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
llama_kv_cache_iswa: creating non-SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache: layer  24: filtered
llama_kv_cache: layer  25: filtered
llama_kv_cache: layer  26: filtered
llama_kv_cache: layer  27: dev = CPU
llama_kv_cache: layer  28: filtered
llama_kv_cache: layer  29: filtered
llama_kv_cache: layer  30: filtered
llama_kv_cache: layer  31: dev = CPU
llama_kv_cache: layer  32: filtered
llama_kv_cache: layer  33: filtered
llama_kv_cache: layer  34: filtered
llama_kv_cache: layer  35: dev = CPU
llama_kv_cache:        CPU KV buffer size =     9.00 MiB
llama_kv_cache: size =    9.00 MiB (   256 cells,   9 layers,  5/1 seqs), K (f16):    4.50 MiB, V (f16):    4.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_kv_cache_iswa: creating     SWA KV cache, size = 256 cells
llama_kv_cache: layer   0: dev = CPU
llama_kv_cache: layer   1: dev = CPU
llama_kv_cache: layer   2: dev = CPU
llama_kv_cache: layer   3: filtered
llama_kv_cache: layer   4: dev = CPU
llama_kv_cache: layer   5: dev = CPU
llama_kv_cache: layer   6: dev = CPU
llama_kv_cache: layer   7: filtered
llama_kv_cache: layer   8: dev = CPU
llama_kv_cache: layer   9: dev = CPU
llama_kv_cache: layer  10: dev = CPU
llama_kv_cache: layer  11: filtered
llama_kv_cache: layer  12: dev = CPU
llama_kv_cache: layer  13: dev = CPU
llama_kv_cache: layer  14: dev = CPU
llama_kv_cache: layer  15: filtered
llama_kv_cache: layer  16: dev = CPU
llama_kv_cache: layer  17: dev = CPU
llama_kv_cache: layer  18: dev = CPU
llama_kv_cache: layer  19: filtered
llama_kv_cache: layer  20: dev = CPU
llama_kv_cache: layer  21: dev = CPU
llama_kv_cache: layer  22: dev = CPU
llama_kv_cache: layer  23: filtered
llama_kv_cache: layer  24: dev = CPU
llama_kv_cache: layer  25: dev = CPU
llama_kv_cache: layer  26: dev = CPU
llama_kv_cache: layer  27: filtered
llama_kv_cache: layer  28: dev = CPU
llama_kv_cache: layer  29: dev = CPU
llama_kv_cache: layer  30: dev = CPU
llama_kv_cache: layer  31: filtered
llama_kv_cache: layer  32: dev = CPU
llama_kv_cache: layer  33: dev = CPU
llama_kv_cache: layer  34: dev = CPU
llama_kv_cache: layer  35: filtered
llama_kv_cache:        CPU KV buffer size =    27.00 MiB
llama_kv_cache: size =   27.00 MiB (   256 cells,  27 layers,  5/1 seqs), K (f16):   13.50 MiB, V (f16):   13.50 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 2320
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 129, n_seqs = 5, n_outputs = 5
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
graph_reserve: reserving a graph for ubatch with n_tokens =    5, n_seqs =  5, n_outputs =    5
graph_reserve: reserving a graph for ubatch with n_tokens =  129, n_seqs =  5, n_outputs =  129
graph_reserve: making n_tokens a multiple of n_seqs - n_tokens = 130, n_seqs = 5, n_outputs = 129
sched_reserve:        CPU compute buffer size =    67.03 MiB
sched_reserve: graph nodes  = 1266
sched_reserve: graph splits = 1
sched_reserve: reserve took 4.46 ms, sched copies = 1
state_read_meta: cell_count = 61, dest_seq_id = 0
state_read_meta: cell_count = 61, dest_seq_id = 0
~llama_context:        CPU compute buffer size is  67.0298 MiB, matches expectation of  67.0298 MiB
{
  "schema": "ggufone.evidence.e1b-perf/v1",
  "captured_at": "2026-09-17T21:04:01Z",
  "runtime_dir": "/var/home/rybens/.hermes/runtime/b11026-linux-x64-cpu",
  "cpu_count": 24,
  "gates": "report-only (A-E1b-14)",
  "rows": [
    {
      "model": "qwen35",
      "path": "/var/home/rybens/.cache/llama.cpp/Qwen3.5-0.8B-UD-Q4_K_XL.gguf",
      "threads": 8,
      "prefix_tokens": 59,
      "cold_prefill_ms": 1717.484,
      "cold_prefill_tok_per_s": 34.4,
      "cold_wall_ms": 3290.0,
      "warm_prefill_ms": 0.0,
      "warm_prefill_reused": true,
      "warm_questions_ms": 1397.3,
      "warm_wall_ms": 1681.1,
      "warm_wall_ms_all": [
        1342.2,
        1681.1,
        2676.7
      ],
      "note": "4 candidates, 1 question, CPU (n_gpu_layers=0); the recon reference point is 14-20 ms warm on Vulkan with a warm state cache [recon]",
      "answers": {
        "area": {
          "type": "choice",
          "choice": "billing",
          "probabilities": {
            "billing": 0.9930168965046084,
            "technical": 0.00038700282979208965,
            "platform": 0.0018696190799282952,
            "support": 0.004726481585671062
          },
          "confidence": 0.990689195339478,
          "coverage": 0.05605553022585685,
          "reliability": "low_mass",
          "decode_steps": 4
        }
      }
    },
    {
      "model": "spark2_5",
      "path": "/var/home/rybens/.hermes/models/Spark-X2.5-4B-Q8_0.gguf",
      "threads": 8,
      "prefix_tokens": 61,
      "cold_prefill_ms": 11905.282,
      "cold_prefill_tok_per_s": 5.1,
      "cold_wall_ms": 22118.5,
      "warm_prefill_ms": 0.0,
      "warm_prefill_reused": true,
      "warm_questions_ms": 10192.7,
      "warm_wall_ms": 10384.9,
      "warm_wall_ms_all": [
        10384.9,
        11108.0,
        9286.2
      ],
      "note": "4 candidates, 1 question, CPU (n_gpu_layers=0); the recon reference point is 14-20 ms warm on Vulkan with a warm state cache [recon]",
      "answers": {
        "area": {
          "type": "choice",
          "choice": "billing",
          "probabilities": {
            "billing": 0.9360850057054314,
            "technical": 0.06391077353291336,
            "platform": 4.0646913070412924e-08,
            "support": 4.180114742187016e-06
          },
          "confidence": 0.9147800076072419,
          "coverage": 0.005079755611859894,
          "reliability": "low_mass",
          "decode_steps": 6
        }
      }
    }
  ]
}

written: docs/evidence/e1b_perf.json
exit=0
