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mxyng/docs-cloud
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Reference: github-starred/ollama#31073
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Originally created by @Bandit253 on GitHub (Jan 3, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/8299
What is the issue?
When using from
langchain_community.document_loaders import WebBaseLoader- (maybe not cause)I get
ResponseError: llama runner process has terminated: error loading model: llama_model_loader: failed to load model fromit doesn't even appear to finish the error message!?but after retying, I get this, see image, I have taken the photo before it filled the graph. I had to take the photo because everything stopped with the only way to recover is a hard reset.

Langsmith reports:
LangSmith metedata:
OS
Windows
GPU
Nvidia
CPU
Intel
Ollama version
0.5.4
@rick-github commented on GitHub (Jan 3, 2025):
Server logs will aid in debugging. If your langchain app has logs, they would also be useful. The
llama_model_loader: failed to load model fromerror is from llama.cpp, a missing model name seems to indicate that the constructor is instantiated with a broken argument.Interesting that there is no read/write activity on the drive. https://www.reddit.com/r/techsupport/comments/6ciyje/disk_activity_on_ssd_spikes_to_100_and_freezes_at/
@Bandit253 commented on GitHub (Jan 4, 2025):
That referenced article so far appears to have solved my problem...
Thank you so much!!
@Bandit253 commented on GitHub (Jan 5, 2025):
Oh No! I thought it had been solved, and it was working well then, same thing c drive 100% no real activity.
Hard reset required!
server-1.log
@rick-github commented on GitHub (Jan 5, 2025):
The prompt you are sending is larger than the context window, see here, this can cause extra processing but shouldn't cause what you are seeing here.
This is strange, the cuda_v12 runner was started with a
--modelparameter and crashed withInvalid argument, and then the cuda_v12_avx runner with the same arguments worked 35s later. Not sure what's going on here. Maybe the cuda_v12 runner is broken? You can try settingOLLAMA_LLM_LIBRARY=cuda_v12_avxin the server environment, that will make it skip over the cuda_v12 runner.@Bandit253 commented on GitHub (Jan 5, 2025):
Unfortunately,
OLLAMA_LLM_LIBRARY=cuda_v12_avxdid not help@rick-github commented on GitHub (Jan 5, 2025):
Logs from failure of
OLLAMA_LLM_LIBRARY=cuda_v12_avx?@Bandit253 commented on GitHub (Jan 5, 2025):
The last two logs,
server.log
server-1.log
Thanks of the help
@rick-github commented on GitHub (Jan 5, 2025):
Well, things have improved in that the logs no longer show crashing runners. But there's nothing to indicate any reason for the system getting wedged and requiring a reset.
The thing is that once ollama has loaded the model, there's very little disk activity from the server - pretty much just the occasional log entry. I don't know what would cause 100% disk activity with zero read/write and also make the system unresponsive. From the screenshot, we can see the spike in GPU for the inference and a smaller spike in CPU as is processes the results, and then from that point on GPU and CPU slide away, memory is fairly static, network traffic goes to zero and disk activity goes to 100%. I'm pretty sure this is not directly caused by ollama. Can you give some details about the client that is using ollama? From the logs you are using nomic-embed-text-v1.5 and llama-3.2:3b-instruct-q4_K_M, so I'm guessing this is some sort of RAG system. Do you have a vectorstore backing the app? What sort of post-processing is done? Is the first time using this app, or have you you used it previously without having these issues?
@Bandit253 commented on GitHub (Jan 6, 2025):
A bit of background I was completing an extensive training course which had aspects of all those things: RAG, Tools, vector store, agents and history. The model used most often was llama-3.2:3b and embedding done with nomic-embed-text.
I have now completed that course but intend to develop my own 'complete' application, which will not be quite as intensive, but I will very much be keeping an eye on when and where the problem source, if it occurs again and will likewise pass on my findings.
At this stage it is un-resolved but less critical, I hope.
Thank you for the help and suggestions.
@rick-github commented on GitHub (Jan 6, 2025):
Closing this for now but feel free to re-open when you have new details.
@Bandit253 commented on GitHub (Jan 26, 2025):
Well, I thought I would give you an update and as they say in the classics, "It's not you, it's me". Since last report I have tried all sorts of things with essentially similar results:
So, I think it is my hardware, either an issue with my C drive or with my GPU.
If you have any suggestions, happy to hear them. Next move probably replacing c drive.....
@rick-github commented on GitHub (Jan 27, 2025):
Thanks for the follow up. It's interesting that in worked in docker without the GPU. Could you run ollama natively and in your app set
"options":{"num_gpu":0}? This will force the model to run in CPU, so it will be slow, but if it doesn't cause the drive issues it would confirm that it's the combination of GPU+drive that's causing the problem. Some sort of PCIe clash or lane saturation? What's your motherboard? Maybe moving the devices to different slots to change the pci lane mapping could help.@Bandit253 commented on GitHub (Jan 29, 2025):
Well, your suspicion was completely correct 100% CPU run fine if not really slowly but was unable to make it fail, big models small models, just kept working.
Change to using the GPU C drive maxed out and hard reset.
My motherboard is Gigabyte Z490 UD.
When you say "... moving the devices to different slots.." you are meaning physically?
@rick-github commented on GitHub (Jan 29, 2025):
Yes, unfortunately looking at a pic of the board you don't have many options. An RTX3070 is a double-wide card so I assume it's plugged in to the white PCIEx16 slot. D: is SATA so it's either a 2.5/3.5" drive plugged into a SATA connector or it's an M.2 device in M2A_SB under the GPU, C: can be in either M2M_SB or M2A_SB depending on what D: is. Moving devices is going to require extracting the GPU which will be a pain. The simplest route may be getting another SATA drive and using one of the SATA ports (not number 1, see page 16 of the manual) as drive C:.
The BIOS has some options regarding SATA and PCIe devices but I don't think there's much that can be done there. If you've previously tweaked the settings (overclock, undervoltage, etc) setting default values may have an effect.
@Bandit253 commented on GitHub (Feb 1, 2025):
Well, thank you so much after a small bit of open case surgery, all is well.
I merely swapped the C drive M2 slots and I have been doing a bit of testing that would have killed the PC prior and here I am still able to type.
You're a campion for going above beyond just the support of Ollama!!
@rick-github commented on GitHub (Feb 1, 2025):
Glad you've resolved it, happy inferencing.