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[GH-ISSUE #7640] Error: POST predict: Post "http://127.0.0.1:42623/completion": EOF #51386
Closed
opened 2026-04-28 19:46:39 -05:00 by GiteaMirror
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Originally created by @phalexo on GitHub (Nov 13, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/7640
What is the issue?
(CodeLlama) developer@ai:~/PROJECTS/OllamaModelFiles$ ~/ollama/ollama run gemma-2-27b-it-Q8_0:latest
OS
Linux
GPU
Nvidia
CPU
Intel
Ollama version
Latest
@agreppin commented on GitHub (Nov 13, 2024):
had same issue and posted a log for that problem in #7638 (but AMD ROCm not Nvidia)
edit: my CPU does not support avx2, I just did go build .; now retrying with go build avx,rocm .; will confirm later ...
@rick-github commented on GitHub (Nov 13, 2024):
https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues
@phalexo commented on GitHub (Nov 13, 2024):
Did you resolve this issue? I noticed that the port number changes from one
attempt to another, as if it is being randomly overwritten.
On Wed, Nov 13, 2024, 1:24 AM Alain Greppin @.***>
wrote:
@rick-github commented on GitHub (Nov 13, 2024):
The port number changes because it's randomly selected when a new server starts.
@phalexo commented on GitHub (Nov 13, 2024):
Re: " The port number changes because it's randomly selected when a new
server starts."
If you mean that when I run
ollama serve
the port is selected at random, then it is really what I am saying.
it is when I run
ollama run "model_name" and after I prompt it with "Hello." that it fails
and returns that error message, and every time it reports a different port.
On Wed, Nov 13, 2024 at 10:40 AM frob @.***> wrote:
@phalexo commented on GitHub (Nov 13, 2024):
Re: " The port number changes because it's randomly selected when a new
server starts."
If you mean that when I run
ollama serve
the port is selected at random, then it is really NOT what I am saying.
it is when I run
ollama run "model_name" and after I prompt it with "Hello." that it fails
and returns that error message, and every time it reports a different port.
On Wed, Nov 13, 2024 at 10:40 AM frob @.***> wrote:
@rick-github commented on GitHub (Nov 13, 2024):
I should have been clearer about which server I was talking about. When you run
ollama run model_name, the ollama server creates a new process,ollama_llama_server, otherwise known as the runner. The ollama server communicates with the runner over TCP, using a port that is randomly selected when the runner is launched. When the runner crashes, the ollama server returns the error indicating that the attempt to communicate with the runner on the allocated port has failed:Error: POST predict: Post "http://127.0.0.1:42623/completion": EOF. The ollama server will then start a new runner, which gets a different port number.@fragelius1 commented on GitHub (Nov 13, 2024):
the old server is still running and when your trying to run new one it crashes is my guess. coz if I go to http://localhost:11434/ its says that ollama is running
is there a way to jump in already running process in linux?
@rick-github commented on GitHub (Nov 13, 2024):
The
EOFerror is from the runner crashing. The ollama server is not crashing. In Linux you can attach to a process withstraceto see what it is doing.@fragelius1 commented on GitHub (Nov 13, 2024):
Indeed.
@phalexo commented on GitHub (Nov 13, 2024):
It may have to do something either with model size or quantization. I am trying to run Qwen2.5-Coder-Instruct.Q8_0, which causes this issue.
I have 4 GPUs with with 12.2 GiB each, so the 8 bit model should fit without issues.
That said, I tried instead to pull the standard 4 bit model from Ollama repo, and it does not seem to have the same problem.
@phalexo commented on GitHub (Nov 13, 2024):
Forcing it to run with "cpu_avx" library gets rid of the error, but is extremely slow. So, appears to be GPU/CUDA related.
@phalexo commented on GitHub (Nov 13, 2024):
Dropping the version to V0.3.11 fixes this problem. The build process looked quite different, just by eyeballing it.
@fragelius1 commented on GitHub (Nov 14, 2024):
hmm seems to be only llama3.2 problem? pulled codegemma and it works okay... maybe coz its bigger than 5gb and dosnt go on gpu memory it works ok... i have old 4gb card... err nope its loaded in gpu 3,5gb size.
updt2: okay ran some more tests, and if you dont use AI for some time it will unload itself from gpu and then when trying to query it again it loaded only 1.8gb and crashed in this "error: post predict: ...." even on codegemma
updt3: even if it can load itself back to 3.6gb on card it still might crash
@phalexo commented on GitHub (Nov 14, 2024):
I dropped the version to v0.3.11 and now it works with
qwen2.5-coder-instruct-32b.Q8_0, which is about 34GB.
On Thu, Nov 14, 2024, 5:05 AM fragelius1 @.***> wrote:
@jessegross commented on GitHub (Nov 14, 2024):
If someone who is running into the issue can post server logs, that would be the next step in debugging.
@konrad0101 commented on GitHub (Nov 15, 2024):
@jessegross I'm getting the same error on ollama 0.4.1 running llama 3.1 70B Q4_K_M on Linux with Nvidia (2x3090). Full logs are below.
Application layer:
And from ollama logs:
@NWBx01 commented on GitHub (Nov 15, 2024):
@jessegross I am also experiencing this issue. My logs are nearly identical to @konrad0101. I'm using a Quadro P4000 and Qwen2.5 7b on Ollama 0.4.1
It seems like the issue is caused by a segmentation fault, perhaps?
I can post full logs, but as mentioned, they're nearly identical to what has already been posted above.
@NWBx01 commented on GitHub (Nov 15, 2024):
I decided to check the past several version of Ollama. It would appear that 0.4.2-rc1, 0.4.1, 0.4.0, and 0.3.14 are affected. The last version to function properly is 0.3.13. 0.3.12 also functioned properly in my testing, and it was mentioned earlier in this thread that 0.3.11 worked as well.
@S-yf commented on GitHub (Nov 15, 2024):
Have you solved it? I also had this problem
@padey commented on GitHub (Nov 16, 2024):
+1, same problem.
@agreppin commented on GitHub (Nov 16, 2024):
more logs with AMD/ROCm variant, Ubuntu 24.04.1, CPU no-avx2:
ollama-v0.3.13-rocm-v6.2.4.log
{"error":"llama runner process has terminated: signal: illegal instruction (core dumped)"}
ollama-v0.4.2-rocm-v6.2.4.log
{"error":"POST predict: Post "http://127.0.0.1:39843/completion": EOF"}
scripts used:
Edit: using your builds with your rocm version
@huskeyw commented on GitHub (Nov 22, 2024):
same issue here, only when a LLM extends over 1 GPU..
huskeyw@timmy:~$ [GIN] 2024/11/21 - 18:09:24 | 200 | 42.709µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/11/21 - 18:09:24 | 200 | 48.614366ms | 127.0.0.1 | POST "/api/show"
time=2024-11-21T18:09:24.743-08:00 level=INFO source=sched.go:730 msg="new model will fit in available VRAM, loading" model=/home/huskeyw/.ollama/models/blobs/sha256-a677b4a4b70c45e702b1d600f7905e367733c53898b8be60e3f29272cf334574 library=cuda parallel=4 required="43.2 GiB"
time=2024-11-21T18:09:24.919-08:00 level=INFO source=server.go:105 msg="system memory" total="251.6 GiB" free="246.9 GiB" free_swap="8.0 GiB"
time=2024-11-21T18:09:24.920-08:00 level=INFO source=memory.go:343 msg="offload to cuda" layers.requested=-1 layers.model=81 layers.offload=81 layers.split=41,40 memory.available="[22.3 GiB 22.3 GiB]" memory.gpu_overhead="0 B" memory.required.full="43.2 GiB" memory.required.partial="43.2 GiB" memory.required.kv="2.5 GiB" memory.required.allocations="[22.0 GiB 21.2 GiB]" memory.weights.total="38.4 GiB" memory.weights.repeating="37.6 GiB" memory.weights.nonrepeating="822.0 MiB" memory.graph.full="1.1 GiB" memory.graph.partial="1.1 GiB"
time=2024-11-21T18:09:24.923-08:00 level=INFO source=server.go:383 msg="starting llama server" cmd="/tmp/ollama1665379698/runners/cuda_v11/ollama_llama_server --model /home/huskeyw/.ollama/models/blobs/sha256-a677b4a4b70c45e702b1d600f7905e367733c53898b8be60e3f29272cf334574 --ctx-size 8192 --batch-size 512 --n-gpu-layers 81 --threads 24 --parallel 4 --tensor-split 41,40 --port 42669"
time=2024-11-21T18:09:24.924-08:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
time=2024-11-21T18:09:24.924-08:00 level=INFO source=server.go:562 msg="waiting for llama runner to start responding"
time=2024-11-21T18:09:24.924-08:00 level=INFO source=server.go:596 msg="waiting for server to become available" status="llm server error"
time=2024-11-21T18:09:24.942-08:00 level=INFO source=runner.go:883 msg="starting go runner"
time=2024-11-21T18:09:24.942-08:00 level=INFO source=runner.go:884 msg=system info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | cgo(gcc)" threads=24
time=2024-11-21T18:09:24.942-08:00 level=INFO source=.:0 msg="Server listening on 127.0.0.1:42669"
llama_model_loader: loaded meta data with 29 key-value pairs and 724 tensors from /home/huskeyw/.ollama/models/blobs/sha256-a677b4a4b70c45e702b1d600f7905e367733c53898b8be60e3f29272cf334574 (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 = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 70B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 70B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 9: llama.block_count u32 = 80
llama_model_loader: - kv 10: llama.context_length u32 = 131072
llama_model_loader: - kv 11: llama.embedding_length u32 = 8192
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 28672
llama_model_loader: - kv 13: llama.attention.head_count u32 = 64
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: general.file_type u32 = 2
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
time=2024-11-21T18:09:25.176-08:00 level=INFO source=server.go:596 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 28: general.quantization_version u32 = 2
llama_model_loader: - type f32: 162 tensors
llama_model_loader: - type q4_0: 561 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_layer = 80
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 28672
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 70B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 70.55 B
llm_load_print_meta: model size = 37.22 GiB (4.53 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 70B Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: EOM token = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 CUDA devices:
Device 0: Tesla M40 24GB, compute capability 5.2, VMM: yes
Device 1: Tesla M40 24GB, compute capability 5.2, VMM: yes
llm_load_tensors: ggml ctx size = 1.02 MiB
llm_load_tensors: offloading 80 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 81/81 layers to GPU
llm_load_tensors: CPU buffer size = 563.62 MiB
llm_load_tensors: CUDA0 buffer size = 18821.57 MiB
llm_load_tensors: CUDA1 buffer size = 18725.43 MiB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 1312.00 MiB
llama_kv_cache_init: CUDA1 KV buffer size = 1248.00 MiB
llama_new_context_with_model: KV self size = 2560.00 MiB, K (f16): 1280.00 MiB, V (f16): 1280.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 2.08 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=4)
llama_new_context_with_model: CUDA0 compute buffer size = 1216.01 MiB
llama_new_context_with_model: CUDA1 compute buffer size = 1216.02 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 80.02 MiB
llama_new_context_with_model: graph nodes = 2566
llama_new_context_with_model: graph splits = 3
time=2024-11-21T18:09:36.721-08:00 level=INFO source=server.go:601 msg="llama runner started in 11.80 seconds"
[GIN] 2024/11/21 - 18:09:36 | 200 | 12.287203955s | 127.0.0.1 | POST "/api/generate"
CUDA error: out of memory
current device: 1, in function alloc at ggml-cuda.cu:406
cuMemCreate(&handle, reserve_size, &prop, 0)
ggml-cuda.cu:132: CUDA error
Could not attach to process. If your uid matches the uid of the target
process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try
again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf
ptrace: Operation not permitted.
No stack.
The program is not being run.
SIGABRT: abort
PC=0x7ec7edc9eb1c m=5 sigcode=18446744073709551610
signal arrived during cgo execution
goroutine 7 gp=0xc0000f0380 m=5 mp=0xc000100008 [syscall]:
runtime.cgocall(0x5982c5435b70, 0xc0000b6b20)
runtime/cgocall.go:157 +0x4b fp=0xc0000b6af8 sp=0xc0000b6ac0 pc=0x5982c51b73cb
github.com/ollama/ollama/llama._Cfunc_llama_decode(0x7ec798006470, {0xf, 0x7ec79828bed0, 0x0, 0x0, 0x7ec798009bd0, 0x7ec7980070c0, 0x7ec798012260, 0x7ec798024950, 0x0, ...})
_cgo_gotypes.go:543 +0x52 fp=0xc0000b6b20 sp=0xc0000b6af8 pc=0x5982c52b4952
github.com/ollama/ollama/llama.(*Context).Decode.func1(0x5982c543186b?, 0x7ec798006470?)
github.com/ollama/ollama/llama/llama.go:169 +0xd8 fp=0xc0000b6c40 sp=0xc0000b6b20 pc=0x5982c52b6f18
github.com/ollama/ollama/llama.(*Context).Decode(0xc0000b6d28?, 0x0?)
github.com/ollama/ollama/llama/llama.go:169 +0x17 fp=0xc0000b6c88 sp=0xc0000b6c40 pc=0x5982c52b6d77
main.(*Server).processBatch(0xc000188120, 0xc0001861c0, 0xc0000b6f10)
github.com/ollama/ollama/llama/runner/runner.go:427 +0x38d fp=0xc0000b6ed0 sp=0xc0000b6c88 pc=0x5982c543080d
main.(*Server).run(0xc000188120, {0x5982c5774ea0, 0xc0000dc0f0})
github.com/ollama/ollama/llama/runner/runner.go:327 +0x1a5 fp=0xc0000b6fb8 sp=0xc0000b6ed0 pc=0x5982c5430105
main.main.gowrap2()
github.com/ollama/ollama/llama/runner/runner.go:922 +0x28 fp=0xc0000b6fe0 sp=0xc0000b6fb8 pc=0x5982c5434ba8
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000b6fe8 sp=0xc0000b6fe0 pc=0x5982c521fde1
created by main.main in goroutine 1
github.com/ollama/ollama/llama/runner/runner.go:922 +0xc52
goroutine 1 gp=0xc000006380 m=nil [IO wait]:
runtime.gopark(0xc000032008?, 0x0?, 0x80?, 0x63?, 0xc00002d8b8?)
runtime/proc.go:402 +0xce fp=0xc00002d880 sp=0xc00002d860 pc=0x5982c51ee00e
runtime.netpollblock(0xc00002d918?, 0xc51b6b26?, 0x82?)
runtime/netpoll.go:573 +0xf7 fp=0xc00002d8b8 sp=0xc00002d880 pc=0x5982c51e6257
internal/poll.runtime_pollWait(0x7ec806b89820, 0x72)
runtime/netpoll.go:345 +0x85 fp=0xc00002d8d8 sp=0xc00002d8b8 pc=0x5982c521aaa5
internal/poll.(*pollDesc).wait(0x3?, 0x3fe?, 0x0)
internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc00002d900 sp=0xc00002d8d8 pc=0x5982c526a9c7
internal/poll.(*pollDesc).waitRead(...)
internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Accept(0xc0001be100)
internal/poll/fd_unix.go:611 +0x2ac fp=0xc00002d9a8 sp=0xc00002d900 pc=0x5982c526be8c
net.(*netFD).accept(0xc0001be100)
net/fd_unix.go:172 +0x29 fp=0xc00002da60 sp=0xc00002d9a8 pc=0x5982c52da949
net.(*TCPListener).accept(0xc0000c6200)
net/tcpsock_posix.go:159 +0x1e fp=0xc00002da88 sp=0xc00002da60 pc=0x5982c52eb67e
net.(*TCPListener).Accept(0xc0000c6200)
net/tcpsock.go:327 +0x30 fp=0xc00002dab8 sp=0xc00002da88 pc=0x5982c52ea9d0
net/http.(*onceCloseListener).Accept(0xc0001881b0?)
:1 +0x24 fp=0xc00002dad0 sp=0xc00002dab8 pc=0x5982c5411be4
net/http.(*Server).Serve(0xc0001ce0f0, {0x5982c5774860, 0xc0000c6200})
net/http/server.go:3260 +0x33e fp=0xc00002dc00 sp=0xc00002dad0 pc=0x5982c54089fe
main.main()
github.com/ollama/ollama/llama/runner/runner.go:942 +0xfec fp=0xc00002df50 sp=0xc00002dc00 pc=0x5982c543492c
runtime.main()
runtime/proc.go:271 +0x29d fp=0xc00002dfe0 sp=0xc00002df50 pc=0x5982c51edbdd
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc00002dfe8 sp=0xc00002dfe0 pc=0x5982c521fde1
goroutine 2 gp=0xc000006e00 m=nil [force gc (idle)]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc0000a6fa8 sp=0xc0000a6f88 pc=0x5982c51ee00e
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.forcegchelper()
runtime/proc.go:326 +0xb8 fp=0xc0000a6fe0 sp=0xc0000a6fa8 pc=0x5982c51ede98
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000a6fe8 sp=0xc0000a6fe0 pc=0x5982c521fde1
created by runtime.init.6 in goroutine 1
runtime/proc.go:314 +0x1a
goroutine 3 gp=0xc000007340 m=nil [GC sweep wait]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc0000a7780 sp=0xc0000a7760 pc=0x5982c51ee00e
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.bgsweep(0xc0000220e0)
runtime/mgcsweep.go:278 +0x94 fp=0xc0000a77c8 sp=0xc0000a7780 pc=0x5982c51d8b54
runtime.gcenable.gowrap1()
runtime/mgc.go:203 +0x25 fp=0xc0000a77e0 sp=0xc0000a77c8 pc=0x5982c51cd685
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000a77e8 sp=0xc0000a77e0 pc=0x5982c521fde1
created by runtime.gcenable in goroutine 1
runtime/mgc.go:203 +0x66
goroutine 4 gp=0xc000007500 m=nil [GC scavenge wait]:
runtime.gopark(0xc0000220e0?, 0x5982c5674260?, 0x1?, 0x0?, 0xc000007500?)
runtime/proc.go:402 +0xce fp=0xc0000a7f78 sp=0xc0000a7f58 pc=0x5982c51ee00e
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.(*scavengerState).park(0x5982c5943520)
runtime/mgcscavenge.go:425 +0x49 fp=0xc0000a7fa8 sp=0xc0000a7f78 pc=0x5982c51d6549
runtime.bgscavenge(0xc0000220e0)
runtime/mgcscavenge.go:653 +0x3c fp=0xc0000a7fc8 sp=0xc0000a7fa8 pc=0x5982c51d6adc
runtime.gcenable.gowrap2()
runtime/mgc.go:204 +0x25 fp=0xc0000a7fe0 sp=0xc0000a7fc8 pc=0x5982c51cd625
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000a7fe8 sp=0xc0000a7fe0 pc=0x5982c521fde1
created by runtime.gcenable in goroutine 1
runtime/mgc.go:204 +0xa5
goroutine 5 gp=0xc0000f0000 m=nil [finalizer wait]:
runtime.gopark(0xc0000a6648?, 0x5982c51c0f85?, 0xa8?, 0x1?, 0xc000006380?)
runtime/proc.go:402 +0xce fp=0xc0000a6620 sp=0xc0000a6600 pc=0x5982c51ee00e
runtime.runfinq()
runtime/mfinal.go:194 +0x107 fp=0xc0000a67e0 sp=0xc0000a6620 pc=0x5982c51cc6c7
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000a67e8 sp=0xc0000a67e0 pc=0x5982c521fde1
created by runtime.createfing in goroutine 1
runtime/mfinal.go:164 +0x3d
goroutine 8 gp=0xc0000f0540 m=nil [select]:
runtime.gopark(0xc000253a48?, 0x2?, 0x50?, 0x81?, 0xc0002537ec?)
runtime/proc.go:402 +0xce fp=0xc000253658 sp=0xc000253638 pc=0x5982c51ee00e
runtime.selectgo(0xc000253a48, 0xc0002537e8, 0xf?, 0x0, 0x1?, 0x1)
runtime/select.go:327 +0x725 fp=0xc000253778 sp=0xc000253658 pc=0x5982c51ff3e5
main.(*Server).completion(0xc000188120, {0x5982c5774a10, 0xc000217180}, 0xc000207680)
github.com/ollama/ollama/llama/runner/runner.go:667 +0xa45 fp=0xc000253ab8 sp=0xc000253778 pc=0x5982c5432345
main.(*Server).completion-fm({0x5982c5774a10?, 0xc000217180?}, 0x5982c540cd2d?)
:1 +0x36 fp=0xc000253ae8 sp=0xc000253ab8 pc=0x5982c5435396
net/http.HandlerFunc.ServeHTTP(0xc0000eac30?, {0x5982c5774a10?, 0xc000217180?}, 0x10?)
net/http/server.go:2171 +0x29 fp=0xc000253b10 sp=0xc000253ae8 pc=0x5982c54057c9
net/http.(*ServeMux).ServeHTTP(0x5982c51c0f85?, {0x5982c5774a10, 0xc000217180}, 0xc000207680)
net/http/server.go:2688 +0x1ad fp=0xc000253b60 sp=0xc000253b10 pc=0x5982c540764d
net/http.serverHandler.ServeHTTP({0x5982c5773d60?}, {0x5982c5774a10?, 0xc000217180?}, 0x6?)
net/http/server.go:3142 +0x8e fp=0xc000253b90 sp=0xc000253b60 pc=0x5982c540866e
net/http.(*conn).serve(0xc0001881b0, {0x5982c5774e68, 0xc0000e8e10})
net/http/server.go:2044 +0x5e8 fp=0xc000253fb8 sp=0xc000253b90 pc=0x5982c5404408
net/http.(*Server).Serve.gowrap3()
net/http/server.go:3290 +0x28 fp=0xc000253fe0 sp=0xc000253fb8 pc=0x5982c5408de8
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000253fe8 sp=0xc000253fe0 pc=0x5982c521fde1
created by net/http.(*Server).Serve in goroutine 1
net/http/server.go:3290 +0x4b4
goroutine 70 gp=0xc00021efc0 m=nil [IO wait]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0xb?)
runtime/proc.go:402 +0xce fp=0xc000220da8 sp=0xc000220d88 pc=0x5982c51ee00e
runtime.netpollblock(0x5982c5254558?, 0xc51b6b26?, 0x82?)
runtime/netpoll.go:573 +0xf7 fp=0xc000220de0 sp=0xc000220da8 pc=0x5982c51e6257
internal/poll.runtime_pollWait(0x7ec806b89728, 0x72)
runtime/netpoll.go:345 +0x85 fp=0xc000220e00 sp=0xc000220de0 pc=0x5982c521aaa5
internal/poll.(*pollDesc).wait(0xc0001be180?, 0xc0000e8f41?, 0x0)
internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc000220e28 sp=0xc000220e00 pc=0x5982c526a9c7
internal/poll.(*pollDesc).waitRead(...)
internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Read(0xc0001be180, {0xc0000e8f41, 0x1, 0x1})
internal/poll/fd_unix.go:164 +0x27a fp=0xc000220ec0 sp=0xc000220e28 pc=0x5982c526b51a
net.(*netFD).Read(0xc0001be180, {0xc0000e8f41?, 0x0?, 0x0?})
net/fd_posix.go:55 +0x25 fp=0xc000220f08 sp=0xc000220ec0 pc=0x5982c52d9845
net.(*conn).Read(0xc0000aa0a0, {0xc0000e8f41?, 0x0?, 0x0?})
net/net.go:185 +0x45 fp=0xc000220f50 sp=0xc000220f08 pc=0x5982c52e3b05
net.(*TCPConn).Read(0x0?, {0xc0000e8f41?, 0x0?, 0x0?})
:1 +0x25 fp=0xc000220f80 sp=0xc000220f50 pc=0x5982c52ef4e5
net/http.(*connReader).backgroundRead(0xc0000e8f30)
net/http/server.go:681 +0x37 fp=0xc000220fc8 sp=0xc000220f80 pc=0x5982c53fe377
net/http.(*connReader).startBackgroundRead.gowrap2()
net/http/server.go:677 +0x25 fp=0xc000220fe0 sp=0xc000220fc8 pc=0x5982c53fe2a5
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000220fe8 sp=0xc000220fe0 pc=0x5982c521fde1
created by net/http.(*connReader).startBackgroundRead in goroutine 8
net/http/server.go:677 +0xba
rax 0x0
rbx 0xe031
rcx 0x7ec7edc9eb1c
rdx 0x6
rdi 0xe02d
rsi 0xe031
rbp 0x7ec7a53f6410
rsp 0x7ec7a53f63d0
r8 0x0
r9 0x0
r10 0x8
r11 0x246
r12 0x6
r13 0x84
r14 0x16
r15 0x4c311a4000
rip 0x7ec7edc9eb1c
rflags 0x246
cs 0x33
fs 0x0
gs 0x0
[GIN] 2024/11/21 - 18:09:52 | 200 | 5.978648843s | 127.0.0.1 | POST "/api/chat"
@phalexo commented on GitHub (Nov 22, 2024):
I think a lot of different bugs look the same on the surface, because they
kill the "runner" process, and ollama then reports being unable to
communicate with that runner.
On Thu, Nov 21, 2024 at 9:10 PM huskeyw @.***> wrote:
@jessegross commented on GitHub (Nov 22, 2024):
I agree, there are multiple issues here, I think most of them should be resolved with 0.4.3. @phalexo are you still seeing this with the current version?
@phalexo commented on GitHub (Nov 22, 2024):
I have not yet upgraded to the latest version, still using 0.3.11 at
moment.
On Fri, Nov 22, 2024, 2:37 PM Jesse Gross @.***> wrote:
@huskeyw commented on GitHub (Nov 22, 2024):
I apologies, while its the same output my issues was really related to https://github.com/ollama/ollama/issues/6382
@ibamibrhm commented on GitHub (Nov 24, 2024):
@jessegross I still received the error with version 0.4.4
Error: POST predict: Post "http://127.0.0.1:60337/completion": EOF.@jessegross commented on GitHub (Nov 25, 2024):
@ibamibrhm Can you please collect the server logs and file them in a new bug?
There are too many different things mixed together in this bug and, as I said, I think most have been fixed so I'm going to go ahead and close this one and we can start fresh on any additional issues.
@Glitchfix commented on GitHub (Apr 18, 2025):
I did some investigation about this
In my case it occurs when I was running multiple models in parallel
I changed the env variables to support it and doubled up the queue size to prevent any crashes for multiple parallel incoming requests
It hasn't crashed after that
@Android-PowerUser commented on GitHub (Apr 27, 2025):
I still have it 0.6.6
>>> hello ⠹ time=2025-04-27T22:00:48.808Z level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32 ⠋ ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ops.cpp:4717: void ggml_compute_forward_soft_max_f32(const ggml_compute_params *, ggml_tensor *): assertion "!isnan(wp[i])" failed ⠼ [GIN] 2025/04/27 - 22:01:00 | 200 | 11.424188221s | 127.0.0.1 | POST "/api/chat" Error: POST predict: Post "http://127.0.0.1:43067/completion": EOF@officalsaints commented on GitHub (Apr 29, 2025):
+1 all issue is same to me
@sadhikariSteep commented on GitHub (Apr 29, 2025):
i try both ollama 0.6.5 and 0.6.6 got same error for gemme and deepseek:
ResponseError: POST predict: Post "http://127.0.0.1:44119/completion": EOF (status code: 500)response = await engine.aquery(query) got above error but without async it works
it work for llama3.2:3b
@ghost commented on GitHub (May 1, 2025):
Hello.
192.168.68.59:11434 - ollama server.
╔╣ Request ║ POST
║ http://192.168.68.59:11434/api/generate
╚══════════════════════════════════════════════════════════════════════════════════════════╝
╔╣ Response ║ POST ║ Status: 500 Internal Server Error ║ Time: 367 ms
║ http://192.168.68.59:11434/api/generate
╚══════════════════════════════════════════════════════════════════════════════════════════╝
╔ Body
║
║ {
║ "error": "POST predict: Post "http://127.0.0.1:50262/completion": EOF"
║ }
║
╚══════════════════════════════════════════════
@phalexo commented on GitHub (May 1, 2025):
I don't think this is the same problem for everyone. When a process dies
for whatever reason, could be an OOM, the main process tries to start a new
one. Watch the port number, it changes from one failure to another.
On Thu, May 1, 2025, 1:04 PM HeroeBew @.***> wrote:
@jessegross commented on GitHub (May 1, 2025):
Yes, this is an old issue, so the new comments are likely not related. If you are still seeing this with 0.6.7, please file a new issue and attach logs.
@cattei commented on GitHub (May 1, 2025):
我这边也发生同样的问题了,0.6.7版本,windows系统