github-starred/ollama
2
0
Fork 0
mirror of https://github.com/ollama/ollama.git synced 2026-05-23 14:13:13 -05:00
Code Issues 16.9k Packages Projects Releases 122 Wiki Activity

[GH-ISSUE #6434] error loading model "Xiaobu Embedding v2" :error="llama runner process has terminated: signal: segmentation fault (core dumped)" #29804

New Issue
Closed
opened 2026-04-22 09:03:32 -05:00 by GiteaMirror · 7 comments
GiteaMirror commented 2026-04-22 09:03:32 -05:00
Owner
Copy Link

Originally created by @AAEE86 on GitHub (Aug 20, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/6434

What is the issue?

ollama-1 | time=2024-08-20T02:46:33.204Z level=INFO source=memory.go:309 msg="offload to cpu" layers.requested=-1 layers.model=25 layers.offload=0 layers.split="" memory.available="[22.2 GiB]" memory.required.full="820.5 MiB" memory.required.partial="0 B" memory.required.kv="48.0 MiB" memory.required.allocations="[820.5 MiB]" memory.weights.total="625.2 MiB" memory.weights.repeating="584.0 MiB" memory.weights.nonrepeating="41.3 MiB" memory.graph.full="128.0 MiB" memory.graph.partial="128.0 MiB"
ollama-1 | time=2024-08-20T02:46:33.206Z level=INFO source=server.go:393 msg="starting llama server" cmd="/tmp/ollama1960294902/runners/cpu_avx2/ollama_llama_server --model /root/.ollama/models/blobs/sha256-85df6dbe02a3bfb67f24400c4d56ba8bd1a8a19a14450761b65ce17fe1d5064a --ctx-size 8192 --batch-size 512 --embedding --log-disable --no-mmap --parallel 4 --port 46451"
ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=sched.go:445 msg="loaded runners" count=1
ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=server.go:593 msg="waiting for llama runner to start responding"
ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server error"
ollama-1 | INFO [main] build info | build=1 commit="1e6f655" tid="127020122728320" timestamp=1724121993
ollama-1 | INFO [main] system info | n_threads=16 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="127020122728320" timestamp=1724121993 total_threads=32
ollama-1 | INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="31" port="46451" tid="127020122728320" timestamp=1724121993
ollama-1 | llama_model_loader: loaded meta data with 27 key-value pairs and 389 tensors from /root/.ollama/models/blobs/sha256-85df6dbe02a3bfb67f24400c4d56ba8bd1a8a19a14450761b65ce17fe1d5064a (version GGUF V3 (latest))
ollama-1 | llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
ollama-1 | llama_model_loader: - kv 0: general.architecture str = bert
ollama-1 | llama_model_loader: - kv 1: general.type str = model
ollama-1 | llama_model_loader: - kv 2: general.name str = Xiaobu Embedding v2
ollama-1 | llama_model_loader: - kv 3: general.version str = v2
ollama-1 | llama_model_loader: - kv 4: general.basename str = xiaobu-embedding
ollama-1 | llama_model_loader: - kv 5: general.size_label str = 324M
ollama-1 | llama_model_loader: - kv 6: general.tags arr[str,1] = ["mteb"]
ollama-1 | llama_model_loader: - kv 7: bert.block_count u32 = 24
ollama-1 | llama_model_loader: - kv 8: bert.context_length u32 = 512
ollama-1 | llama_model_loader: - kv 9: bert.embedding_length u32 = 1024
ollama-1 | llama_model_loader: - kv 10: bert.feed_forward_length u32 = 4096
ollama-1 | llama_model_loader: - kv 11: bert.attention.head_count u32 = 16
ollama-1 | llama_model_loader: - kv 12: bert.attention.layer_norm_epsilon f32 = 0.000000
ollama-1 | llama_model_loader: - kv 13: general.file_type u32 = 1
ollama-1 | llama_model_loader: - kv 14: bert.attention.causal bool = false
ollama-1 | llama_model_loader: - kv 15: bert.pooling_type u32 = 1
ollama-1 | llama_model_loader: - kv 16: tokenizer.ggml.token_type_count u32 = 2
ollama-1 | llama_model_loader: - kv 17: tokenizer.ggml.model str = bert
ollama-1 | llama_model_loader: - kv 18: tokenizer.ggml.pre str = xiaobu
ollama-1 | llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "...
ollama-1 | llama_model_loader: - kv 20: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
ollama-1 | llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 100
ollama-1 | llama_model_loader: - kv 22: tokenizer.ggml.seperator_token_id u32 = 102
ollama-1 | llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 0
ollama-1 | llama_model_loader: - kv 24: tokenizer.ggml.cls_token_id u32 = 101
ollama-1 | llama_model_loader: - kv 25: tokenizer.ggml.mask_token_id u32 = 103
ollama-1 | llama_model_loader: - kv 26: general.quantization_version u32 = 2
ollama-1 | llama_model_loader: - type f32: 244 tensors
ollama-1 | llama_model_loader: - type f16: 145 tensors
ollama-1 | llm_load_vocab: special tokens cache size = 5
ollama-1 | llm_load_vocab: token to piece cache size = 0.0769 MB
ollama-1 | llm_load_print_meta: format = GGUF V3 (latest)
ollama-1 | llm_load_print_meta: arch = bert
ollama-1 | llm_load_print_meta: vocab type = WPM
ollama-1 | llm_load_print_meta: n_vocab = 21128
ollama-1 | llm_load_print_meta: n_merges = 0
ollama-1 | llm_load_print_meta: vocab_only = 0
ollama-1 | llm_load_print_meta: n_ctx_train = 512
ollama-1 | llm_load_print_meta: n_embd = 1024
ollama-1 | llm_load_print_meta: n_layer = 24
ollama-1 | llm_load_print_meta: n_head = 16
ollama-1 | llm_load_print_meta: n_head_kv = 16
ollama-1 | llm_load_print_meta: n_rot = 64
ollama-1 | llm_load_print_meta: n_swa = 0
ollama-1 | llm_load_print_meta: n_embd_head_k = 64
ollama-1 | llm_load_print_meta: n_embd_head_v = 64
ollama-1 | llm_load_print_meta: n_gqa = 1
ollama-1 | llm_load_print_meta: n_embd_k_gqa = 1024
ollama-1 | llm_load_print_meta: n_embd_v_gqa = 1024
ollama-1 | llm_load_print_meta: f_norm_eps = 1.0e-12
ollama-1 | llm_load_print_meta: f_norm_rms_eps = 0.0e+00
ollama-1 | llm_load_print_meta: f_clamp_kqv = 0.0e+00
ollama-1 | llm_load_print_meta: f_max_alibi_bias = 0.0e+00
ollama-1 | llm_load_print_meta: f_logit_scale = 0.0e+00
ollama-1 | llm_load_print_meta: n_ff = 4096
ollama-1 | llm_load_print_meta: n_expert = 0
ollama-1 | llm_load_print_meta: n_expert_used = 0
ollama-1 | llm_load_print_meta: causal attn = 0
ollama-1 | llm_load_print_meta: pooling type = 1
ollama-1 | llm_load_print_meta: rope type = 2
ollama-1 | llm_load_print_meta: rope scaling = linear
ollama-1 | llm_load_print_meta: freq_base_train = 10000.0
ollama-1 | llm_load_print_meta: freq_scale_train = 1
ollama-1 | llm_load_print_meta: n_ctx_orig_yarn = 512
ollama-1 | llm_load_print_meta: rope_finetuned = unknown
ollama-1 | llm_load_print_meta: ssm_d_conv = 0
ollama-1 | llm_load_print_meta: ssm_d_inner = 0
ollama-1 | llm_load_print_meta: ssm_d_state = 0
ollama-1 | llm_load_print_meta: ssm_dt_rank = 0
ollama-1 | llm_load_print_meta: model type = 335M
ollama-1 | llm_load_print_meta: model ftype = F16
ollama-1 | llm_load_print_meta: model params = 324.47 M
ollama-1 | llm_load_print_meta: model size = 620.50 MiB (16.04 BPW)
ollama-1 | llm_load_print_meta: general.name = Xiaobu Embedding v2
ollama-1 | llm_load_print_meta: UNK token = 100 '[UNK]'
ollama-1 | llm_load_print_meta: SEP token = 102 '[SEP]'
ollama-1 | llm_load_print_meta: PAD token = 0 '[PAD]'
ollama-1 | llm_load_print_meta: CLS token = 101 '[CLS]'
ollama-1 | llm_load_print_meta: MASK token = 103 '[MASK]'
ollama-1 | llm_load_print_meta: LF token = 0 '[PAD]'
ollama-1 | llm_load_print_meta: max token length = 48
ollama-1 | llm_load_tensors: ggml ctx size = 0.16 MiB
ollama-1 | llm_load_tensors: CPU buffer size = 620.50 MiB
ollama-1 | time=2024-08-20T02:46:33.458Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server loading model"
ollama-1 | llama_new_context_with_model: n_ctx = 8192
ollama-1 | llama_new_context_with_model: n_batch = 512
ollama-1 | llama_new_context_with_model: n_ubatch = 512
ollama-1 | llama_new_context_with_model: flash_attn = 0
ollama-1 | llama_new_context_with_model: freq_base = 10000.0
ollama-1 | llama_new_context_with_model: freq_scale = 1
ollama-1 | llama_kv_cache_init: CPU KV buffer size = 768.00 MiB
ollama-1 | llama_new_context_with_model: KV self size = 768.00 MiB, K (f16): 384.00 MiB, V (f16): 384.00 MiB
ollama-1 | llama_new_context_with_model: CPU output buffer size = 0.00 MiB
ollama-1 | llama_new_context_with_model: CPU compute buffer size = 25.01 MiB
ollama-1 | llama_new_context_with_model: graph nodes = 851
ollama-1 | llama_new_context_with_model: graph splits = 1
ollama-1 | time=2024-08-20T02:46:34.161Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server not responding"
ollama-1 | time=2024-08-20T02:46:35.314Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server error"
ollama-1 | [GIN] 2024/08/20 - 02:46:36 | 500 | 3.116673729s | 172.17.0.1 | POST "/v1/embeddings"
ollama-1 | time=2024-08-20T02:46:36.317Z level=ERROR source=sched.go:451 msg="error loading llama server" error="llama runner process has terminated: signal: segmentation fault (core dumped)"

OS

Docker

GPU

Other

CPU

Intel

Ollama version

0.3.5

Originally created by @AAEE86 on GitHub (Aug 20, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/6434 ### What is the issue? ollama-1 | time=2024-08-20T02:46:33.204Z level=INFO source=memory.go:309 msg="offload to cpu" layers.requested=-1 layers.model=25 layers.offload=0 layers.split="" memory.available="[22.2 GiB]" memory.required.full="820.5 MiB" memory.required.partial="0 B" memory.required.kv="48.0 MiB" memory.required.allocations="[820.5 MiB]" memory.weights.total="625.2 MiB" memory.weights.repeating="584.0 MiB" memory.weights.nonrepeating="41.3 MiB" memory.graph.full="128.0 MiB" memory.graph.partial="128.0 MiB" ollama-1 | time=2024-08-20T02:46:33.206Z level=INFO source=server.go:393 msg="starting llama server" cmd="/tmp/ollama1960294902/runners/cpu_avx2/ollama_llama_server --model /root/.ollama/models/blobs/sha256-85df6dbe02a3bfb67f24400c4d56ba8bd1a8a19a14450761b65ce17fe1d5064a --ctx-size 8192 --batch-size 512 --embedding --log-disable --no-mmap --parallel 4 --port 46451" ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=sched.go:445 msg="loaded runners" count=1 ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=server.go:593 msg="waiting for llama runner to start responding" ollama-1 | time=2024-08-20T02:46:33.207Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server error" ollama-1 | INFO [main] build info | build=1 commit="1e6f655" tid="127020122728320" timestamp=1724121993 ollama-1 | INFO [main] system info | n_threads=16 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="127020122728320" timestamp=1724121993 total_threads=32 ollama-1 | INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="31" port="46451" tid="127020122728320" timestamp=1724121993 ollama-1 | llama_model_loader: loaded meta data with 27 key-value pairs and 389 tensors from /root/.ollama/models/blobs/sha256-85df6dbe02a3bfb67f24400c4d56ba8bd1a8a19a14450761b65ce17fe1d5064a (version GGUF V3 (latest)) ollama-1 | llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. ollama-1 | llama_model_loader: - kv 0: general.architecture str = bert ollama-1 | llama_model_loader: - kv 1: general.type str = model ollama-1 | llama_model_loader: - kv 2: general.name str = Xiaobu Embedding v2 ollama-1 | llama_model_loader: - kv 3: general.version str = v2 ollama-1 | llama_model_loader: - kv 4: general.basename str = xiaobu-embedding ollama-1 | llama_model_loader: - kv 5: general.size_label str = 324M ollama-1 | llama_model_loader: - kv 6: general.tags arr[str,1] = ["mteb"] ollama-1 | llama_model_loader: - kv 7: bert.block_count u32 = 24 ollama-1 | llama_model_loader: - kv 8: bert.context_length u32 = 512 ollama-1 | llama_model_loader: - kv 9: bert.embedding_length u32 = 1024 ollama-1 | llama_model_loader: - kv 10: bert.feed_forward_length u32 = 4096 ollama-1 | llama_model_loader: - kv 11: bert.attention.head_count u32 = 16 ollama-1 | llama_model_loader: - kv 12: bert.attention.layer_norm_epsilon f32 = 0.000000 ollama-1 | llama_model_loader: - kv 13: general.file_type u32 = 1 ollama-1 | llama_model_loader: - kv 14: bert.attention.causal bool = false ollama-1 | llama_model_loader: - kv 15: bert.pooling_type u32 = 1 ollama-1 | llama_model_loader: - kv 16: tokenizer.ggml.token_type_count u32 = 2 ollama-1 | llama_model_loader: - kv 17: tokenizer.ggml.model str = bert ollama-1 | llama_model_loader: - kv 18: tokenizer.ggml.pre str = xiaobu ollama-1 | llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "... ollama-1 | llama_model_loader: - kv 20: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... ollama-1 | llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 100 ollama-1 | llama_model_loader: - kv 22: tokenizer.ggml.seperator_token_id u32 = 102 ollama-1 | llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 0 ollama-1 | llama_model_loader: - kv 24: tokenizer.ggml.cls_token_id u32 = 101 ollama-1 | llama_model_loader: - kv 25: tokenizer.ggml.mask_token_id u32 = 103 ollama-1 | llama_model_loader: - kv 26: general.quantization_version u32 = 2 ollama-1 | llama_model_loader: - type f32: 244 tensors ollama-1 | llama_model_loader: - type f16: 145 tensors ollama-1 | llm_load_vocab: special tokens cache size = 5 ollama-1 | llm_load_vocab: token to piece cache size = 0.0769 MB ollama-1 | llm_load_print_meta: format = GGUF V3 (latest) ollama-1 | llm_load_print_meta: arch = bert ollama-1 | llm_load_print_meta: vocab type = WPM ollama-1 | llm_load_print_meta: n_vocab = 21128 ollama-1 | llm_load_print_meta: n_merges = 0 ollama-1 | llm_load_print_meta: vocab_only = 0 ollama-1 | llm_load_print_meta: n_ctx_train = 512 ollama-1 | llm_load_print_meta: n_embd = 1024 ollama-1 | llm_load_print_meta: n_layer = 24 ollama-1 | llm_load_print_meta: n_head = 16 ollama-1 | llm_load_print_meta: n_head_kv = 16 ollama-1 | llm_load_print_meta: n_rot = 64 ollama-1 | llm_load_print_meta: n_swa = 0 ollama-1 | llm_load_print_meta: n_embd_head_k = 64 ollama-1 | llm_load_print_meta: n_embd_head_v = 64 ollama-1 | llm_load_print_meta: n_gqa = 1 ollama-1 | llm_load_print_meta: n_embd_k_gqa = 1024 ollama-1 | llm_load_print_meta: n_embd_v_gqa = 1024 ollama-1 | llm_load_print_meta: f_norm_eps = 1.0e-12 ollama-1 | llm_load_print_meta: f_norm_rms_eps = 0.0e+00 ollama-1 | llm_load_print_meta: f_clamp_kqv = 0.0e+00 ollama-1 | llm_load_print_meta: f_max_alibi_bias = 0.0e+00 ollama-1 | llm_load_print_meta: f_logit_scale = 0.0e+00 ollama-1 | llm_load_print_meta: n_ff = 4096 ollama-1 | llm_load_print_meta: n_expert = 0 ollama-1 | llm_load_print_meta: n_expert_used = 0 ollama-1 | llm_load_print_meta: causal attn = 0 ollama-1 | llm_load_print_meta: pooling type = 1 ollama-1 | llm_load_print_meta: rope type = 2 ollama-1 | llm_load_print_meta: rope scaling = linear ollama-1 | llm_load_print_meta: freq_base_train = 10000.0 ollama-1 | llm_load_print_meta: freq_scale_train = 1 ollama-1 | llm_load_print_meta: n_ctx_orig_yarn = 512 ollama-1 | llm_load_print_meta: rope_finetuned = unknown ollama-1 | llm_load_print_meta: ssm_d_conv = 0 ollama-1 | llm_load_print_meta: ssm_d_inner = 0 ollama-1 | llm_load_print_meta: ssm_d_state = 0 ollama-1 | llm_load_print_meta: ssm_dt_rank = 0 ollama-1 | llm_load_print_meta: model type = 335M ollama-1 | llm_load_print_meta: model ftype = F16 ollama-1 | llm_load_print_meta: model params = 324.47 M ollama-1 | llm_load_print_meta: model size = 620.50 MiB (16.04 BPW) ollama-1 | llm_load_print_meta: general.name = Xiaobu Embedding v2 ollama-1 | llm_load_print_meta: UNK token = 100 '[UNK]' ollama-1 | llm_load_print_meta: SEP token = 102 '[SEP]' ollama-1 | llm_load_print_meta: PAD token = 0 '[PAD]' ollama-1 | llm_load_print_meta: CLS token = 101 '[CLS]' ollama-1 | llm_load_print_meta: MASK token = 103 '[MASK]' ollama-1 | llm_load_print_meta: LF token = 0 '[PAD]' ollama-1 | llm_load_print_meta: max token length = 48 ollama-1 | llm_load_tensors: ggml ctx size = 0.16 MiB ollama-1 | llm_load_tensors: CPU buffer size = 620.50 MiB ollama-1 | time=2024-08-20T02:46:33.458Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server loading model" ollama-1 | llama_new_context_with_model: n_ctx = 8192 ollama-1 | llama_new_context_with_model: n_batch = 512 ollama-1 | llama_new_context_with_model: n_ubatch = 512 ollama-1 | llama_new_context_with_model: flash_attn = 0 ollama-1 | llama_new_context_with_model: freq_base = 10000.0 ollama-1 | llama_new_context_with_model: freq_scale = 1 ollama-1 | llama_kv_cache_init: CPU KV buffer size = 768.00 MiB ollama-1 | llama_new_context_with_model: KV self size = 768.00 MiB, K (f16): 384.00 MiB, V (f16): 384.00 MiB ollama-1 | llama_new_context_with_model: CPU output buffer size = 0.00 MiB ollama-1 | llama_new_context_with_model: CPU compute buffer size = 25.01 MiB ollama-1 | llama_new_context_with_model: graph nodes = 851 ollama-1 | llama_new_context_with_model: graph splits = 1 ollama-1 | time=2024-08-20T02:46:34.161Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server not responding" ollama-1 | time=2024-08-20T02:46:35.314Z level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server error" ollama-1 | [GIN] 2024/08/20 - 02:46:36 | 500 | 3.116673729s | 172.17.0.1 | POST "/v1/embeddings" ollama-1 | time=2024-08-20T02:46:36.317Z level=ERROR source=sched.go:451 msg="error loading llama server" error="llama runner process has terminated: signal: segmentation fault (core dumped)" ### OS Docker ### GPU Other ### CPU Intel ### Ollama version 0.3.5
GiteaMirror added the bug label 2026-04-22 09:03:33 -05:00
GiteaMirror commented 2026-04-22 09:03:35 -05:00
Author
Owner
Copy Link

@mxyng commented on GitHub (Aug 21, 2024):

Can you link where you got the model?

<!-- gh-comment-id:2302971883 --> @mxyng commented on GitHub (Aug 21, 2024): Can you link where you got the model?
GiteaMirror commented 2026-04-22 09:03:37 -05:00
Author
Owner
Copy Link

@AAEE86 commented on GitHub (Aug 22, 2024):

Can you link where you got the model?

https://ollama.com/search?q=xiaobu
I have tried both of these models and they both report the same error.

<!-- gh-comment-id:2303795615 --> @AAEE86 commented on GitHub (Aug 22, 2024): > Can you link where you got the model? https://ollama.com/search?q=xiaobu I have tried both of these models and they both report the same error.
GiteaMirror commented 2026-04-22 09:03:38 -05:00
Author
Owner
Copy Link

@mxyng commented on GitHub (Aug 23, 2024):

Thanks for the link. I'm able to reproduce it. I'll keep you posted

<!-- gh-comment-id:2307556471 --> @mxyng commented on GitHub (Aug 23, 2024): Thanks for the link. I'm able to reproduce it. I'll keep you posted
GiteaMirror commented 2026-04-22 09:03:40 -05:00
Author
Owner
Copy Link

@Lagstill commented on GitHub (Aug 23, 2024):

Same for me with llama3.1 on docker

<!-- gh-comment-id:2307594359 --> @Lagstill commented on GitHub (Aug 23, 2024): Same for me with llama3.1 on docker
GiteaMirror commented 2026-04-22 09:03:40 -05:00
Author
Owner
Copy Link

@mxyng commented on GitHub (Aug 26, 2024):

After some investigation, it seems to be an issue specific to this model (xiaobu embedding v2). For some reason, llama.cpp segfaults accessing inp_embd data around 50% of the time. Not sure what the root cause is. The tensor seems to be initialized correctly

You might have some luck cross posting this to the llama.cpp GitHub

<!-- gh-comment-id:2311220844 --> @mxyng commented on GitHub (Aug 26, 2024): After some investigation, it seems to be an issue specific to this model (xiaobu embedding v2). For some reason, llama.cpp segfaults accessing `inp_embd` data around 50% of the time. Not sure what the root cause is. The tensor seems to be initialized correctly You might have some luck cross posting this to the [llama.cpp](https://github.com/ggerganov/llama.cpp) GitHub
GiteaMirror commented 2026-04-22 09:03:41 -05:00
Author
Owner
Copy Link

@AAEE86 commented on GitHub (Aug 27, 2024):

After some investigation, it seems to be an issue specific to this model (xiaobu embedding v2). For some reason, llama.cpp segfaults accessing inp_embd data around 50% of the time. Not sure what the root cause is. The tensor seems to be initialized correctly

You might have some luck cross posting this to the llama.cpp GitHub

Thanks for the reply, I'll try to submit this to the llama.cpp GitHub.

<!-- gh-comment-id:2311743838 --> @AAEE86 commented on GitHub (Aug 27, 2024): > After some investigation, it seems to be an issue specific to this model (xiaobu embedding v2). For some reason, llama.cpp segfaults accessing `inp_embd` data around 50% of the time. Not sure what the root cause is. The tensor seems to be initialized correctly > > You might have some luck cross posting this to the [llama.cpp](https://github.com/ggerganov/llama.cpp) GitHub Thanks for the reply, I'll try to submit this to the llama.cpp GitHub.
GiteaMirror commented 2026-04-22 09:03:42 -05:00
Author
Owner
Copy Link

@AAEE86 commented on GitHub (Aug 28, 2024):

After some investigation, it seems to be an issue specific to this model (xiaobu embedding v2). For some reason, llama.cpp segfaults accessing inp_embd data around 50% of the time. Not sure what the root cause is. The tensor seems to be initialized correctly

You might have some luck cross posting this to the llama.cpp GitHub

After I installed the NVIDIA GPU, it stopped reporting errors.

<!-- gh-comment-id:2313837834 --> @AAEE86 commented on GitHub (Aug 28, 2024): > After some investigation, it seems to be an issue specific to this model (xiaobu embedding v2). For some reason, llama.cpp segfaults accessing `inp_embd` data around 50% of the time. Not sure what the root cause is. The tensor seems to be initialized correctly > > You might have some luck cross posting this to the [llama.cpp](https://github.com/ggerganov/llama.cpp) GitHub After I installed the NVIDIA GPU, it stopped reporting errors.
Sign in to join this conversation.
No Branch/Tag Specified
Branches Tags
Labels
Clear labels
amd
api
app
bug
build
cli
cloud
compatibility
context-length
create
docker
documentation
embeddings
feature request
feedback wanted
good first issue
gpt-oss
gpu
harmony
help wanted
image
install
intel
js
launch
linux
macos
memory
mlx
model
needs more info
networking
nvidia
ollama.com
performance
pull-request
Mirrored from GitHub Pull Request
 python
question
registry
rendering
thinking
tools
top
vulkan
windows
wsl
No Label bug
Milestone
No items
No Milestone
Projects
Clear projects
No project
Assignees
Clear assignees
GiteaMirror ninjasurge
No Assignees
1 Participants
Notifications
Due Date
No due date set.
Dependencies

No dependencies set.

Reference: github-starred/ollama#29804
Delete Branch "%!s()"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?