special_sep_id != LLAMA_TOKEN_NULL %s: MASK token = %d '%s'
%s: FIM SUF token = %d '%s'
%s: FIM PAD token = %d '%s'
vocab.token_bos() != LLAMA_TOKEN_NULL [^\r\n\p{L}\p{N}]?((?=[\p{L}])([^a-z]))*((?=[\p{L}])([^A-Z]))+|[^\r\n\p{L}\p{N}]?((?=[\p{L}])([^a-z]))+((?=[\p{L}])([^A-Z]))*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+ --batch (OW > 0) && "b too small compared to a" C->nb[0] == ggml_type_size(C->type) A->ne[1] == n_head ggml_is_contiguous(k) TYPE_IQ4_NL_4_4 REMOVED, use IQ4_NL with runtime repacking COS ggml_malloc /proc/sys/kernel/numa_balancing tensor kernel->nb[0] >= kernel->nb[2] && kernel->nb[1] >= kernel->nb[0] i11 >= 0 && i11 < ne11 sect_dims <= ne0 gemm_output + patch_n * c_out <= (float*)tmp + params->wsize ctx->kv[key_id].is_array data.size() % type_size == 0 %s: duplicate key '%s' for tensors %zu and %li
qwen3 olmoe glm4moe general.architecture %s.logit_scale %s.attention.layer_norm_rms_epsilon %s.attention.decay_lora_rank tokenizer.ggml.add_eos_token blk.%d.attn_norm blk.%d.ssm_out blk.%d.nextn.hnorm dec.blk.%d.attn_v blk.%d.time_mix_decay_w2 %s: the tokens of sequence %d in the input batch have inconsistent sequence positions:
- the last position stored in the memory module of the context (i.e. the KV cache) for sequence %d is X = %d
- the tokens for sequence %d in the input batch have a starting position of Y = %d
it is required that the sequence positions remain consecutive: Y = X + 1
%s: embd = %p
<|im_sep|> system <|im_system|>tool<|im_middle|> STX ETX N ffn_moe_weighted %s: using full-size SWA cache (ref: %s)
%s: layer %3d: dev = %s
map_layer_ids.find(il_reuse) != map_layer_ids.end() %s: non-consecutive token position %d after %d for sequence %d with %u new tokens
cells[head + cell_count - 1].has_seq_id(dest_seq_id) %s: file format = %s
%s: Using metadata override (%5s) '%s' = str 160M 1.6B 17Bx16E (Scout) A13B %s: n_lora_q = %d
%s: missing test for op %s for tensor %s softmax ffn_norm mamba_out_proj predictions ffn_sub_norm n_embd == hparams.n_embd_r() model.layers.{}.conv.conv llama-v3 kimi-k2 ▁ <0x %s: no backends are loaded. hint: use ggml_backend_load() or ggml_backend_load_all() to load a backend before calling this function
buffer_id >= 0 && buffer_id < galloc->n_buffers Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!
unknown allocation error ggml_aligned_malloc ggml_can_repeat(a, b) ggml_nelements(a) == (ne0*ne1*ne2*ne3) ggml_nelements(a) == ne0*ne1*ne2*ne3 b->ne[1] == a->ne[1] ggml_is_contiguous(v) a->ne[0] == S && a->ne[1] == H && a->ne[2] == n_tokens mxfp4 CONCAT cplan ggml_validate_row_data: found nan value at block %zu
ggml_backend_buffer_is_multi_buffer(buffer) patches_per_batch > 0 && batch_size >= 1 ggml_are_same_shape(src0, src0_grad) ggml_are_same_shape(src0, src0_grad_m) ctx->kv[key_id].get_type() == GGUF_TYPE_STRING %s: invalid magic characters: '%c%c%c%c', expected 'GGUF'
data.size() >= (i+1)*type_size grok wavtokenizer-dec %s.expert_weights_scale %s.time_mix_extra_dim split.tensors.count tokenizer.ggml.remove_extra_whitespaces tokenizer.ggml.suffix_token_id token_embd blk.%d.attn_q_norm blk.%d.attn_k_b blk.%d.time_mix_w2 blk.%d.time_mix_lerp_fused blk.%d.time_mix_a0 %s: n_seqs = %d
%s: token = %p
<|extra_4|> |>
|>