Name and Version
version: 7767 (10c98cb)
built with Clang 19.1.5 for Windows x86_64
Operating systems
Windows
GGML backends
CPU
Hardware
10th core 10400
Models
qwen3 VL embedding 2B
https://huggingface.co/DevQuasar/Qwen.Qwen3-VL-Embedding-2B-GGUF/blob/main/Qwen.Qwen3-VL-Embedding-2B.Q4_K_M.gguf
Problem description & steps to reproduce
llama-server.exe -m Qwen3-VL-Embedding-2B.Q4_K_M.gguf --embedding --pooling last -c 256 -ub 256 --host 0.0.0.0 --port 8081 --cache-ram 0
error occurred in warming up, then program quit
……
llama_kv_cache: size = 28.00 MiB ( 256 cells, 28 layers, 4/1 seqs), K (f16): 14.00 MiB, V (f16): 14.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: CPU compute buffer size = 150.63 MiB
sched_reserve: graph nodes = 988
sched_reserve: graph splits = 1
sched_reserve: reserve took 4.79 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
�[0mD:/a/llama.cpp/llama.cpp/ggml/src/ggml-backend.cpp:271: GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds") failed
If use --no-warmup, the error occurred at the first request, and the error is the same as above.
First Bad Commit
No response
Relevant log output
llama-server.exe -m G:\ai\Qwen3-VL-Embedding-2B.Q4_K_M.gguf --embedding --pooling last -c 256 -ub 256 --host 0.0.0.0 --port 8081 --cache-ram 0 >llamalog.txt
load_backend: loaded RPC backend from W:\ai\llama-b7767-bin-win-cpu-x64\ggml-rpc.dll
load_backend: loaded CPU backend from W:\ai\llama-b7767-bin-win-cpu-x64\ggml-cpu-haswell.dll
main: embeddings enabled with n_batch (2048) > n_ubatch (256)
main: setting n_batch = n_ubatch = 256 to avoid assertion failure
main: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true
build: 7767 (10c98cb) with Clang 19.1.5 for Windows x86_64
system info: n_threads = 6, n_threads_batch = 6, total_threads = 12
system_info: n_threads = 6 (n_threads_batch = 6) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
Running without SSL
init: using 11 threads for HTTP server
start: binding port with default address family
main: loading model
srv load_model: loading model 'G:\ai\Qwen3-VL-Embedding-2B.Q4_K_M.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: no devices with dedicated memory found
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.30 seconds
llama_model_loader: direct I/O is enabled, disabling mmap
llama_model_loader: loaded meta data with 37 key-value pairs and 310 tensors from G:\ai\Qwen3-VL-Embedding-2B.Q4_K_M.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 = qwen3vl
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen.Qwen3 VL Embedding 2B
llama_model_loader: - kv 3: general.basename str = Qwen.Qwen3-VL-Embedding
llama_model_loader: - kv 4: general.size_label str = 2B
llama_model_loader: - kv 5: general.license str = apache-2.0
llama_model_loader: - kv 6: general.base_model.count u32 = 1
llama_model_loader: - kv 7: general.base_model.0.name str = Qwen3 VL 2B Instruct
llama_model_loader: - kv 8: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 9: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3-VL-...
llama_model_loader: - kv 10: general.tags arr[str,2] = ["transformers", "multimodal embedding"]
llama_model_loader: - kv 11: qwen3vl.block_count u32 = 28
llama_model_loader: - kv 12: qwen3vl.context_length u32 = 262144
llama_model_loader: - kv 13: qwen3vl.embedding_length u32 = 2048
llama_model_loader: - kv 14: qwen3vl.feed_forward_length u32 = 6144
llama_model_loader: - kv 15: qwen3vl.attention.head_count u32 = 16
llama_model_loader: - kv 16: qwen3vl.attention.head_count_kv u32 = 8
llama_model_loader: - kv 17: qwen3vl.rope.dimension_sections arr[i32,4] = [24, 20, 20, 0]
llama_model_loader: - kv 18: qwen3vl.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 19: qwen3vl.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 20: qwen3vl.attention.key_length u32 = 128
llama_model_loader: - kv 21: qwen3vl.attention.value_length u32 = 128
llama_model_loader: - kv 22: qwen3vl.n_deepstack_layers u32 = 3
llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 24: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,151936] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,151387] = ["臓 臓", "臓臓 臓臓", "i n", "臓 t",...
llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 30: tokenizer.ggml.eot_token_id u32 = 151645
llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 32: tokenizer.ggml.add_eos_token bool = true
llama_model_loader: - kv 33: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 34: tokenizer.chat_template str = {%- set default_system_message = 'Rep...
llama_model_loader: - kv 35: general.quantization_version u32 = 2
llama_model_loader: - kv 36: general.file_type u32 = 15
llama_model_loader: - type f32: 113 tensors
llama_model_loader: - type q4_K: 168 tensors
llama_model_loader: - type q6_K: 29 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 1.03 GiB (5.12 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 151643 ('<|endoftext|>')
load: - 151645 ('<|im_end|>')
load: - 151662 ('<|fim_pad|>')
load: - 151663 ('<|repo_name|>')
load: - 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3vl
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 8192
print_info: n_layer = 28
print_info: n_head = 16
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 2
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: n_ff = 6144
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 = 0
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
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 = [24, 20, 20, 0]
print_info: model type = 1.7B
print_info: model params = 1.72 B
print_info: general.name = Qwen.Qwen3 VL Embedding 2B
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151643 '<|endoftext|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 '膴'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = true)
load_tensors: offloading output layer to GPU
load_tensors: offloading 27 repeating layers to GPU
load_tensors: offloaded 29/29 layers to GPU
load_tensors: CPU model buffer size = 404.68 MiB
load_tensors: CPU_REPACK model buffer size = 645.75 MiB
..............................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_init_from_model: model default pooling_type is [0], but [3] was specified
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 256
llama_context: n_ctx_seq = 256
llama_context: n_batch = 256
llama_context: n_ubatch = 256
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_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CPU output buffer size = 2.35 MiB
llama_kv_cache: CPU KV buffer size = 28.00 MiB
llama_kv_cache: size = 28.00 MiB ( 256 cells, 28 layers, 4/1 seqs), K (f16): 14.00 MiB, V (f16): 14.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: CPU compute buffer size = 150.63 MiB
sched_reserve: graph nodes = 988
sched_reserve: graph splits = 1
sched_reserve: reserve took 3.79 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
D:/a/llama.cpp/llama.cpp/ggml/src/ggml-backend.cpp:271: GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds") failed
Name and Version
version: 7767 (10c98cb)
built with Clang 19.1.5 for Windows x86_64
Operating systems
Windows
GGML backends
CPU
Hardware
10th core 10400
Models
qwen3 VL embedding 2B
https://huggingface.co/DevQuasar/Qwen.Qwen3-VL-Embedding-2B-GGUF/blob/main/Qwen.Qwen3-VL-Embedding-2B.Q4_K_M.gguf
Problem description & steps to reproduce
llama-server.exe -m Qwen3-VL-Embedding-2B.Q4_K_M.gguf --embedding --pooling last -c 256 -ub 256 --host 0.0.0.0 --port 8081 --cache-ram 0
error occurred in warming up, then program quit
……
llama_kv_cache: size = 28.00 MiB ( 256 cells, 28 layers, 4/1 seqs), K (f16): 14.00 MiB, V (f16): 14.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: CPU compute buffer size = 150.63 MiB
sched_reserve: graph nodes = 988
sched_reserve: graph splits = 1
sched_reserve: reserve took 4.79 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
�[0mD:/a/llama.cpp/llama.cpp/ggml/src/ggml-backend.cpp:271: GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds") failed
If use --no-warmup, the error occurred at the first request, and the error is the same as above.
First Bad Commit
No response
Relevant log output
llama-server.exe -m G:\ai\Qwen3-VL-Embedding-2B.Q4_K_M.gguf --embedding --pooling last -c 256 -ub 256 --host 0.0.0.0 --port 8081 --cache-ram 0 >llamalog.txt
load_backend: loaded RPC backend from W:\ai\llama-b7767-bin-win-cpu-x64\ggml-rpc.dll
load_backend: loaded CPU backend from W:\ai\llama-b7767-bin-win-cpu-x64\ggml-cpu-haswell.dll
main: embeddings enabled with n_batch (2048) > n_ubatch (256)
main: setting n_batch = n_ubatch = 256 to avoid assertion failure
main: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true
build: 7767 (10c98cb) with Clang 19.1.5 for Windows x86_64
system info: n_threads = 6, n_threads_batch = 6, total_threads = 12
system_info: n_threads = 6 (n_threads_batch = 6) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
Running without SSL
init: using 11 threads for HTTP server
start: binding port with default address family
main: loading model
srv load_model: loading model 'G:\ai\Qwen3-VL-Embedding-2B.Q4_K_M.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: no devices with dedicated memory found
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.30 seconds
llama_model_loader: direct I/O is enabled, disabling mmap
llama_model_loader: loaded meta data with 37 key-value pairs and 310 tensors from G:\ai\Qwen3-VL-Embedding-2B.Q4_K_M.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 = qwen3vl
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen.Qwen3 VL Embedding 2B
llama_model_loader: - kv 3: general.basename str = Qwen.Qwen3-VL-Embedding
llama_model_loader: - kv 4: general.size_label str = 2B
llama_model_loader: - kv 5: general.license str = apache-2.0
llama_model_loader: - kv 6: general.base_model.count u32 = 1
llama_model_loader: - kv 7: general.base_model.0.name str = Qwen3 VL 2B Instruct
llama_model_loader: - kv 8: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 9: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3-VL-...
llama_model_loader: - kv 10: general.tags arr[str,2] = ["transformers", "multimodal embedding"]
llama_model_loader: - kv 11: qwen3vl.block_count u32 = 28
llama_model_loader: - kv 12: qwen3vl.context_length u32 = 262144
llama_model_loader: - kv 13: qwen3vl.embedding_length u32 = 2048
llama_model_loader: - kv 14: qwen3vl.feed_forward_length u32 = 6144
llama_model_loader: - kv 15: qwen3vl.attention.head_count u32 = 16
llama_model_loader: - kv 16: qwen3vl.attention.head_count_kv u32 = 8
llama_model_loader: - kv 17: qwen3vl.rope.dimension_sections arr[i32,4] = [24, 20, 20, 0]
llama_model_loader: - kv 18: qwen3vl.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 19: qwen3vl.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 20: qwen3vl.attention.key_length u32 = 128
llama_model_loader: - kv 21: qwen3vl.attention.value_length u32 = 128
llama_model_loader: - kv 22: qwen3vl.n_deepstack_layers u32 = 3
llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 24: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,151936] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,151387] = ["臓 臓", "臓臓 臓臓", "i n", "臓 t",...
llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 30: tokenizer.ggml.eot_token_id u32 = 151645
llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 32: tokenizer.ggml.add_eos_token bool = true
llama_model_loader: - kv 33: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 34: tokenizer.chat_template str = {%- set default_system_message = 'Rep...
llama_model_loader: - kv 35: general.quantization_version u32 = 2
llama_model_loader: - kv 36: general.file_type u32 = 15
llama_model_loader: - type f32: 113 tensors
llama_model_loader: - type q4_K: 168 tensors
llama_model_loader: - type q6_K: 29 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 1.03 GiB (5.12 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 151643 ('<|endoftext|>')
load: - 151645 ('<|im_end|>')
load: - 151662 ('<|fim_pad|>')
load: - 151663 ('<|repo_name|>')
load: - 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3vl
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 8192
print_info: n_layer = 28
print_info: n_head = 16
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 2
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: n_ff = 6144
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 = 0
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
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 = [24, 20, 20, 0]
print_info: model type = 1.7B
print_info: model params = 1.72 B
print_info: general.name = Qwen.Qwen3 VL Embedding 2B
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151643 '<|endoftext|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 '膴'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = true)
load_tensors: offloading output layer to GPU
load_tensors: offloading 27 repeating layers to GPU
load_tensors: offloaded 29/29 layers to GPU
load_tensors: CPU model buffer size = 404.68 MiB
load_tensors: CPU_REPACK model buffer size = 645.75 MiB
..............................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_init_from_model: model default pooling_type is [0], but [3] was specified
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 256
llama_context: n_ctx_seq = 256
llama_context: n_batch = 256
llama_context: n_ubatch = 256
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_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CPU output buffer size = 2.35 MiB
llama_kv_cache: CPU KV buffer size = 28.00 MiB
llama_kv_cache: size = 28.00 MiB ( 256 cells, 28 layers, 4/1 seqs), K (f16): 14.00 MiB, V (f16): 14.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: CPU compute buffer size = 150.63 MiB
sched_reserve: graph nodes = 988
sched_reserve: graph splits = 1
sched_reserve: reserve took 3.79 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
D:/a/llama.cpp/llama.cpp/ggml/src/ggml-backend.cpp:271: GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds") failed