ollama run qwen2:72b-instruct-q2_K but Error: llama runner process has terminated: signal: aborted (core dumped)

holgip5t  于 2个月前  发布在  其他
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问题：服务器启动过程中出现的问题。

6月11日01:17:54,Venue-vPro ollama[2760]:时间=2024-06-11T01:17:54.332+08:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="ll>
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_vocab: special tokens cache size = 421
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_vocab: token to piece cache size = 1.8703 MB
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: format = GGUF V3 (latest)
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: arch = qwen2
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: vocab type = BPE
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_vocab = 152064
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_merges = 151387
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_ctx_train = 32768
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_embd = 8192
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_head = 64
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_head_kv = 8
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_layer = 80
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_rot = 128
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_embd_head_k = 128
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_embd_head_v = 128
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_gqa = 8
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: n_embd_k_gqa = 1024
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: f_norm_eps = 0.0e+00
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load_print_meta: f_norm_rms_eps = 1.0e-06
6月11日01:17:54,Venue-vPro ollama[2760]: llm_load
这段文本是一段日志，记录了在6月11日01:18:56到01:18:04之间，Venue-vPro ollama服务器的一些状态和信息。具体内容包括：

  1. 在这段时间内，服务器成功地将80个重复的层加载到了GPU上。
  2. 服务器的CPU缓冲区大小为389.81 MiB,CUDA0缓冲区大小为13868.58 MiB,CUDA1缓冲区大小为14166.62 MiB。
  3. 服务器使用GGML_ASSERT检查dmmv.cu文件中的某个条件是否为false,但没有给出具体的错误信息。
  4. 在等待服务器变为可用的过程中，有多个线程被创建和销毁。
  5. 服务器使用了libthread_db库进行线程调试。
    6月11日 01:18:05 Venue-vPro ollama[2849]: #0 0x00007f4b23780c7f in __GI___wait4 (pid=2849, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.>
    6月11日 01:18:05 Venue-vPro ollama[2849]: 27 in ../sysdeps/unix/sysv/linux/wait4.c
    6月11日 01:18:05 Venue-vPro ollama[2849]: #1 0x00000000005febbb in ggml_print_backtrace ()
    6月11日 01:18:05 Venue-vPro ollama[2849]: #2 0x00000000006b5dbc in ggml_cuda_op_dequantize_mul_mat_vec(ggml_backend_cuda_context&, ggml_tensor const*, ggml_tensor >
    6月11日 01:18:05 Venue-vPro ollama[2849]: #3 0x000000000068356a in ggml_cuda_op_mul_mat(ggml_backend_cuda_context&, ggml_tensor const*, ggml_tensor const*, ggml_te>
    6月11日 01:18:05 Venue-vPro ollama[2849]: #4 0x00000000006866db in ggml_backend_cuda_graph_compute(ggml_backend*, ggml_cgraph*) ()
    6月11日 01:18:05 Venue-vPro ollama[2849]: #5 0x000000000064a42b in ggml_backend_sched_graph_compute_async ()
    6月11日 01:18:05 Venue-vPro ollama[2849]: #6 0x000000000055c91f in llama_decode ()
    6月11日 01:18:05 Venue-vPro ollama[2849]: #7 0x00000000004ffbe4 in llama_init_from_gpt_params(gpt_params&) ()
    6月11日 01:18:
ollama

5条答案

按热度按时间
nnvyjq4y

nnvyjq4y1#

我可以使用模型qwen7b:16bf,也许你可以尝试一下......

---原始内容--- 发件人：***@***.***> 日期：周二，2024年6月11日 下午2点58分收件人：***@***.***>;抄送：***@***.******@***.***>;主题：回复：[ollama/ollama]运行qwen2:72b-instruct-q2_K时出错：llama runner进程已终止：信号：中止(核心转储)(问题#4964)我对qwen2:7b也遇到了同样的问题——直接回复此电子邮件，查看GitHub上的内容，或取消订阅。你收到这封邮件是因为你创建了这个主题。消息ID:***@***.***>

赞(0）分享  回复(0）举报  2个月前
h9a6wy2h

h9a6wy2h2#

在运行CLI时遇到了相同的错误

$ ollama run qwen2
Error: llama runner process has terminated: signal: aborted (core dumped)

ollama版本是0.1.36,
在Ubuntu上运行，配备了NVIDIA GeForce RTX 4060(8GB)的GPU
llama3、codegemma、gemma、phi3、mistral和aya没有问题

赞(0）分享  回复(0）举报  2个月前
y4ekin9u

y4ekin9u3#

在运行phi3时，在ollama docker上遇到了相同的错误，仅支持CPU。

root@0fe89d3262cb:/# ollama run phi3
Error: llama runner process has terminated: signal: aborted (core dumped) 
root@0fe89d3262cb:/# ollama -v
ollama version is 0.1.44

日志：

llama_model_load: error loading model: mmap failed: No such device
llama_load_model_from_file: exception loading model
terminate called after throwing an instance of 'std::runtime_error'
  what():  mmap failed: No such device
time=2024-06-14T02:16:51.468Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
time=2024-06-14T02:16:51.719Z level=ERROR source=sched.go:344 msg="error loading llama server" error="llama runner process has terminated: signal: aborted (core dumped) "
赞(0）分享  回复(0）举报  2个月前
disbfnqx

disbfnqx4#

同样的问题，你处理了吗？

赞(0）分享  回复(0）举报  2个月前
ocebsuys

ocebsuys5#

你好，我遇到了与camembert-large嵌入模型(最新或q8)相同的问题。我使用另一个模型(nomic embed text)没有问题：

POST /api/embeddings HTTP/1.1
host: 172.17.0.1:11434
connection: keep-alive
content-type: text/plain;charset=UTF-8
accept: */*
accept-language: *
sec-fetch-mode: cors
user-agent: node
accept-encoding: gzip, deflate
content-length: 623

{"model":"royalpha/sentence-camembert-large:latest","prompt":"<document_metadata>\nsourceDocument: Guide technique AREA MANAGER - partie 1.md\npublished: 7/1/2024, 6:44:11 AM\n</document_metadata>\n\n# Guide Technique Note - Guide Area Manager.Docx Alain Sabourdy 20 Juin 2024\r\n\r\n## Contexte\r\n\r\n![0_Image_0.Png](0_Image_0.Png)\r\n\r\nCe document est r..dig.. dans l'unique objectif d'..tre une source d'information pour notre d..veloppement autour d'une Intelligence artificielle d'assistance au commissionnement d'un BMS IoT. Il doit ..tre traduit au format \"markdown\" pour ..tre communiqu.. .. l'IA Attention,"}HTTP/1.1 500 Internal Server Error
Content-Type: application/json; charset=utf-8
Date: Mon, 01 Jul 2024 08:00:04 GMT
Content-Length: 79

{"error":"llama runner process has terminated: signal: aborted (core dumped) "}

我使用的是版本0.1.48
我在Ubuntu上使用2个GPU(A4000)
日志：

juil. 01 10:30:30 system-Precision-Tower-5810 ollama[1517977]: [GIN] 2024/07/01 - 10:30:30 | 500 | 43.218860116s |      172.17.0.2 | POST     "/api/embeddings"
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:31.379+02:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=25 layers.of>
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:31.380+02:00 level=INFO source=server.go:368 msg="starting llama server" cmd="/tmp/ollama3128957023/runners/cuda>
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1564266]: INFO [main] build info | build=1 commit="7c26775" tid="126336302825472" timestamp=1719822631
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1564266]: INFO [main] system info | n_threads=4 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0>
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1564266]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="3" port="34659" tid="126336302825472" timestamp=1719822631
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: loaded meta data with 19 key-value pairs and 389 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-3f6b8a>
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   0:                       general.architecture str              = bert
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   1:                               general.name str              = sentence-camembert-large
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   2:                           bert.block_count u32              = 24
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   3:                        bert.context_length u32              = 514
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   4:                      bert.embedding_length u32              = 1024
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   5:                   bert.feed_forward_length u32              = 4096
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   6:                  bert.attention.head_count u32              = 16
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   7:          bert.attention.layer_norm_epsilon f32              = 0.000010
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   8:                          general.file_type u32              = 7
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv   9:                      bert.attention.causal bool             = false
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  10:            tokenizer.ggml.token_type_count u32              = 2
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  11:                tokenizer.ggml.bos_token_id u32              = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  12:                tokenizer.ggml.eos_token_id u32              = 2
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = bert
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  14:                      tokenizer.ggml.tokens arr[str,32005]   = ["▁<s>NOTUSED", "▁<pad>", "▁</s...
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  15:                      tokenizer.ggml.scores arr[f32,32005]   = [-1000.000000, -1000.000000, -1000.00...
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,32005]   = [3, 3, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, ...
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  17:            tokenizer.ggml.padding_token_id u32              = 1
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - kv  18:               general.quantization_version u32              = 2
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - type  f32:  244 tensors
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_loader: - type q8_0:  145 tensors
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_vocab: special tokens cache size = 7
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_vocab: token to piece cache size = 0.2572 MB
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: format           = GGUF V3 (latest)
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: arch             = bert
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: vocab type       = WPM
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_vocab          = 32005
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_merges         = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_ctx_train      = 514
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_embd           = 1024
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_head           = 16
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_head_kv        = 16
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_layer          = 24
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_rot            = 64
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_embd_head_k    = 64
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_embd_head_v    = 64
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_gqa            = 1
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_embd_k_gqa     = 1024
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_embd_v_gqa     = 1024
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: f_norm_eps       = 1.0e-05
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: f_norm_rms_eps   = 0.0e+00
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: f_clamp_kqv      = 0.0e+00
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: f_logit_scale    = 0.0e+00
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_ff             = 4096
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_expert         = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_expert_used    = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: causal attn      = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: pooling type     = -1
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: rope type        = 2
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: rope scaling     = linear
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: freq_base_train  = 10000.0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: freq_scale_train = 1
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: n_ctx_orig_yarn  = 514
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: rope_finetuned   = unknown
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: ssm_d_conv       = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: ssm_d_inner      = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: ssm_d_state      = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: ssm_dt_rank      = 0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: model type       = 335M
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: model ftype      = Q8_0
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: model params     = 335.61 M
uil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: model size       = 342.45 MiB (8.56 BPW)
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: general.name     = sentence-camembert-large
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: BOS token        = 0 '▁<s>NOTUSED'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: EOS token        = 2 '▁</s>NOTUSED'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: UNK token        = 100 '▁▁on'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: SEP token        = 102 '▁h'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: PAD token        = 1 '▁<pad>'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: CLS token        = 101 '▁▁–'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: MASK token       = 103 '▁y'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_print_meta: LF token         = 0 '▁<s>NOTUSED'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   yes
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: ggml_cuda_init: CUDA_USE_TENSOR_CORES: no
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: ggml_cuda_init: found 1 CUDA devices:
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]:   Device 0: NVIDIA RTX A4000, compute capability 8.6, VMM: yes
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llm_load_tensors: ggml ctx size =    0.35 MiB
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_model_load: error loading model: check_tensor_dims: tensor 'token_types.weight' has wrong shape; expected  1024,     2, got  102>
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: llama_load_model_from_file: exception loading model
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: terminate called after throwing an instance of 'std::runtime_error'
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]:   what():  check_tensor_dims: tensor 'token_types.weight' has wrong shape; expected  1024,     2, got  1024,     1,     1,     1
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:31.905+02:00 level=INFO source=sched.go:382 msg="loaded runners" count=1
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:31.905+02:00 level=INFO source=server.go:556 msg="waiting for llama runner to start responding"
juil. 01 10:30:31 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:31.961+02:00 level=INFO source=server.go:594 msg="waiting for server to become available" status="llm server err>
juil. 01 10:30:32 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:32.212+02:00 level=ERROR source=sched.go:388 msg="error loading llama server" error="llama runner process has te>
juil. 01 10:30:32 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:32.212+02:00 level=WARN source=server.go:475 msg="llama runner process no longer running" sys=134 string="signal>
juil. 01 10:30:32 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:32.212+02:00 level=WARN source=server.go:475 msg="llama runner process no longer running" sys=134 string="signal>
juil. 01 10:30:32 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:32.212+02:00 level=WARN source=server.go:475 msg="llama runner process no longer running" sys=134 string="signal>
juil. 01 10:30:32 system-Precision-Tower-5810 ollama[1517977]: time=2024-07-01T10:30:32.212+02:00 level=WARN source=server.go:475 msg="llama runner process no longer running" sys=134 string="signal>
juil. 01 10:30:32 system-Precision-Tower-5810 ollama[1517977]: [GIN] 2024/07/01 - 10:30:32 | 500 | 44.450877468s |      172.17.0.2 | POST     "/api/embeddings"
juil. 01 10:30:33 system-Precision-Tower-5810 systemd[1]: Stopping Ollama Service...
juil. 01 10:30:33 system-Precision-Tower-5810 systemd[1]: ollama.service: Deactivated successfully.
juil. 01 10:30:33 system-Precision-Tower-5810 systemd[1]: Stopped Ollama Service.
juil. 01 10:30:33 system-Precision-Tower-5810 systemd[1]: ollama.service: Consumed 51.896s CPU time.
juil. 01 10:30:33 system-Precision-Tower-5810 systemd[1]: Started Ollama Service.
juil. 01 10:30:33 system-Precision-Tower-5810 ollama[1564664]: 2024/07/01 10:30:33 routes.go:1064: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVE>
juil. 01 10:30:33 system-Precision-Tower-5810 ollama[1564664]: time=2024-07-01T10:30:33.807+02:00 level=INFO source=images.go:730 msg="total blobs: 39"
juil. 01 10:30:33 system-Precision-Tower-5810 ollama[1564664]: time=2024-07-01T10:30:33.808+02:00 level=INFO source=images.go:737 msg="total unused blobs removed: 0"
juil. 01 10:30:33 system-Precision-Tower-5810 ollama[1564664]: time=2024-07-01T10:30:33.809+02:00 level=INFO source=routes.go:1111 msg="Listening on [::]:11434 (version 0.1.48)"
uil. 01 10:30:33 system-Precision-Tower-5810 ollama[1564664]: time=2024-07-01T10:30:33.809+02:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama2775426305/runners
juil. 01 10:30:37 system-Precision-Tower-5810 ollama[1564664]: time=2024-07-01T10:30:37.668+02:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [rocm_v60101 cpu cpu_avx cpu_avx2 cuda_v>
juil. 01 10:30:38 system-Precision-Tower-5810 ollama[1564664]: time=2024-07-01T10:30:38.176+02:00 level=INFO source=types.go:98 msg="inference compute" id=GPU-ab19754d-e86c-547d-899d-945082a2536f l>
juil. 01 10:30:38 system-Precision-Tower-5810 ollama[1564664]: time=2024-07-01T10:30:38.177+02:00 level=INFO source=types.go:98 msg="inference compute" id=GPU-de5ead24-ca41-5a85-3046-6e331171011d l>
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