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https://api.github.com/repos/ollama/ollama/issues/2979

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https://github.com/ollama/ollama/issues/2979

I_kwDOJ0Z1Ps6Bj8gJ

Starcoder2 crashing ollama docker version 0.1.28

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NONE

I noticed that the ollama version shipped as docker container has been updated to 0.1.28 and thus should run starcoder2 and gemma models - i am still not having luck running those, ollama just crashes... am i missing something? https://pastebin.com/ALJRfZZ5

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https://github.com/ollama/ollama/issues/8660

I_kwDOJ0Z1Ps6n-y90

GPU Memory Not Released After Exiting deepseek-r1:32b Model

closed

NONE

### What is the issue? - Ollama Version: 0.5.7 - Model: deepseek-r1:32b - GPU: NVIDIA RTX 3090 (24GB VRAM) - OS: Windows 11 (include build version if known) After running the `deepseek-r1:32b` model via `ollama run deepseek-r1:32b` and exiting with `/bye` in my terminal, the GPU's dedicated memory remains fully allocated at 24GB despite 0% GPU usage. This persists until I close the ollama application fully, and occurs again when using the model. Is this a bug? It seems strange that the dedicated memory remains at the maximum even after closing the terrminal. ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.5.7

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https://github.com/ollama/ollama/issues/3362

I_kwDOJ0Z1Ps6Do7u4

Report better error on windows on port conflict with winnat

open

NONE

Hi DevOps My Ollama was working fine for me until I tried to use it today not sure what has happened. The LOGS show this repeated Crash and attempt to reload in the app.log Time=2024-03-25T12:09:31.329-05:00 level=INFO source=logging.go:45 msg="ollama app started" time=2024-03-25T12:09:31.389-05:00 level=INFO source=server.go:135 msg="unable to connect to server" time=2024-03-25T12:09:34.633-05:00 level=INFO source=server.go:91 msg="started ollama server with pid 33376" time=2024-03-25T12:09:34.633-05:00 level=INFO source=server.go:93 msg="ollama server logs C:\\Users\\David\\AppData\\Local\\Ollama\\server.log" time=2024-03-25T12:09:35.525-05:00 level=WARN source=server.go:113 msg="server crash 1 - exit code 1 - respawning" time=2024-03-25T12:09:36.037-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:09:37.038-05:00 level=WARN source=server.go:113 msg="server crash 2 - exit code 1 - respawning" time=2024-03-25T12:09:37.550-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:09:39.557-05:00 level=WARN source=server.go:113 msg="server crash 3 - exit code 1 - respawning" time=2024-03-25T12:09:40.071-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:09:43.072-05:00 level=WARN source=server.go:113 msg="server crash 4 - exit code 1 - respawning" time=2024-03-25T12:09:43.572-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:09:47.587-05:00 level=WARN source=server.go:113 msg="server crash 5 - exit code 1 - respawning" time=2024-03-25T12:09:48.087-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:09:53.087-05:00 level=WARN source=server.go:113 msg="server crash 6 - exit code 1 - respawning" time=2024-03-25T12:09:53.599-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:09:59.613-05:00 level=WARN source=server.go:113 msg="server crash 7 - exit code 1 - respawning" time=2024-03-25T12:10:00.116-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:10:07.123-05:00 level=WARN source=server.go:113 msg="server crash 8 - exit code 1 - respawning" time=2024-03-25T12:10:07.636-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:10:15.651-05:00 level=WARN source=server.go:113 msg="server crash 9 - exit code 1 - respawning" time=2024-03-25T12:10:16.166-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:10:25.171-05:00 level=WARN source=server.go:113 msg="server crash 10 - exit code 1 - respawning" time=2024-03-25T12:10:25.683-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:10:35.698-05:00 level=WARN source=server.go:113 msg="server crash 11 - exit code 1 - respawning" time=2024-03-25T12:10:36.207-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:10:47.222-05:00 level=WARN source=server.go:113 msg="server crash 12 - exit code 1 - respawning" time=2024-03-25T12:10:47.734-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:10:59.745-05:00 level=WARN source=server.go:113 msg="server crash 13 - exit code 1 - respawning" time=2024-03-25T12:11:00.252-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:11:13.267-05:00 level=WARN source=server.go:113 msg="server crash 14 - exit code 1 - respawning" time=2024-03-25T12:11:13.782-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:11:27.784-05:00 level=WARN source=server.go:113 msg="server crash 15 - exit code 1 - respawning" time=2024-03-25T12:11:28.293-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:11:43.295-05:00 level=WARN source=server.go:113 msg="server crash 16 - exit code 1 - respawning" time=2024-03-25T12:11:43.810-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:11:59.814-05:00 level=WARN source=server.go:113 msg="server crash 17 - exit code 1 - respawning" time=2024-03-25T12:12:00.328-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:12:17.333-05:00 level=WARN source=server.go:113 msg="server crash 18 - exit code 1 - respawning" time=2024-03-25T12:12:17.845-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:12:35.856-05:00 level=WARN source=server.go:113 msg="server crash 19 - exit code 1 - respawning" time=2024-03-25T12:12:36.370-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:12:55.374-05:00 level=WARN source=server.go:113 msg="server crash 20 - exit code 1 - respawning" time=2024-03-25T12:12:55.888-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:13:15.889-05:00 level=WARN source=server.go:113 msg="server crash 21 - exit code 1 - respawning" time=2024-03-25T12:13:16.403-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:13:37.410-05:00 level=WARN source=server.go:113 msg="server crash 22 - exit code 1 - respawning" time=2024-03-25T12:13:37.924-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:13:59.930-05:00 level=WARN source=server.go:113 msg="server crash 23 - exit code 1 - respawning" time=2024-03-25T12:14:00.436-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:14:23.441-05:00 level=WARN source=server.go:113 msg="server crash 24 - exit code 1 - respawning" time=2024-03-25T12:14:23.954-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:14:47.968-05:00 level=WARN source=server.go:113 msg="server crash 25 - exit code 1 - respawning" time=2024-03-25T12:14:48.482-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:15:13.490-05:00 level=WARN source=server.go:113 msg="server crash 26 - exit code 1 - respawning" time=2024-03-25T12:15:14.003-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:15:40.015-05:00 level=WARN source=server.go:113 msg="server crash 27 - exit code 1 - respawning" time=2024-03-25T12:15:40.529-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:16:07.542-05:00 level=WARN source=server.go:113 msg="server crash 28 - exit code 1 - respawning" time=2024-03-25T12:16:08.052-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:16:36.055-05:00 level=WARN source=server.go:113 msg="server crash 29 - exit code 1 - respawning" time=2024-03-25T12:16:36.569-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:17:05.578-05:00 level=WARN source=server.go:113 msg="server crash 30 - exit code 1 - respawning" time=2024-03-25T12:17:06.089-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:17:36.095-05:00 level=WARN source=server.go:113 msg="server crash 31 - exit code 1 - respawning" time=2024-03-25T12:17:36.595-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:18:07.599-05:00 level=WARN source=server.go:113 msg="server crash 32 - exit code 1 - respawning" time=2024-03-25T12:18:08.108-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:18:40.115-05:00 level=WARN source=server.go:113 msg="server crash 33 - exit code 1 - respawning" time=2024-03-25T12:18:40.628-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:19:13.638-05:00 level=WARN source=server.go:113 msg="server crash 34 - exit code 1 - respawning" time=2024-03-25T12:19:14.152-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:19:48.153-05:00 level=WARN source=server.go:113 msg="server crash 35 - exit code 1 - respawning" time=2024-03-25T12:19:48.664-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:20:23.680-05:00 level=WARN source=server.go:113 msg="server crash 36 - exit code 1 - respawning" time=2024-03-25T12:20:24.181-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:21:00.187-05:00 level=WARN source=server.go:113 msg="server crash 37 - exit code 1 - respawning" time=2024-03-25T12:21:00.696-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:21:37.709-05:00 level=WARN source=server.go:113 msg="server crash 38 - exit code 1 - respawning" time=2024-03-25T12:21:38.210-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:22:16.225-05:00 level=WARN source=server.go:113 msg="server crash 39 - exit code 1 - respawning" time=2024-03-25T12:22:16.728-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:22:55.736-05:00 level=WARN source=server.go:113 msg="server crash 40 - exit code 1 - respawning" time=2024-03-25T12:22:56.251-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:23:36.259-05:00 level=WARN source=server.go:113 msg="server crash 41 - exit code 1 - respawning" time=2024-03-25T12:23:36.772-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:24:17.786-05:00 level=WARN source=server.go:113 msg="server crash 42 - exit code 1 - respawning" time=2024-03-25T12:24:18.297-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:25:00.300-05:00 level=WARN source=server.go:113 msg="server crash 43 - exit code 1 - respawning" time=2024-03-25T12:25:00.801-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:25:43.807-05:00 level=WARN source=server.go:113 msg="server crash 44 - exit code 1 - respawning" time=2024-03-25T12:25:44.319-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:26:28.319-05:00 level=WARN source=server.go:113 msg="server crash 45 - exit code 1 - respawning" time=2024-03-25T12:26:28.834-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:27:13.836-05:00 level=WARN source=server.go:113 msg="server crash 46 - exit code 1 - respawning" time=2024-03-25T12:27:14.349-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:28:00.358-05:00 level=WARN source=server.go:113 msg="server crash 47 - exit code 1 - respawning" time=2024-03-25T12:28:00.858-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:28:47.859-05:00 level=WARN source=server.go:113 msg="server crash 48 - exit code 1 - respawning" time=2024-03-25T12:28:48.370-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:29:36.379-05:00 level=WARN source=server.go:113 msg="server crash 49 - exit code 1 - respawning" time=2024-03-25T12:29:36.890-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:30:25.903-05:00 level=WARN source=server.go:113 msg="server crash 50 - exit code 1 - respawning" time=2024-03-25T12:30:26.414-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:31:16.423-05:00 level=WARN source=server.go:113 msg="server crash 51 - exit code 1 - respawning" time=2024-03-25T12:31:16.928-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:32:07.931-05:00 level=WARN source=server.go:113 msg="server crash 52 - exit code 1 - respawning" time=2024-03-25T12:32:08.440-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:33:00.453-05:00 level=WARN source=server.go:113 msg="server crash 53 - exit code 1 - respawning" time=2024-03-25T12:33:00.967-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:33:53.982-05:00 level=WARN source=server.go:113 msg="server crash 54 - exit code 1 - respawning" time=2024-03-25T12:33:54.492-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:34:48.500-05:00 level=WARN source=server.go:113 msg="server crash 55 - exit code 1 - respawning" time=2024-03-25T12:34:49.001-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:35:44.010-05:00 level=WARN source=server.go:113 msg="server crash 56 - exit code 1 - respawning" time=2024-03-25T12:35:44.519-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:36:40.524-05:00 level=WARN source=server.go:113 msg="server crash 57 - exit code 1 - respawning" time=2024-03-25T12:36:41.036-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:37:38.048-05:00 level=WARN source=server.go:113 msg="server crash 58 - exit code 1 - respawning" time=2024-03-25T12:37:38.556-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:38:36.560-05:00 level=WARN source=server.go:113 msg="server crash 59 - exit code 1 - respawning" time=2024-03-25T12:38:37.076-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:39:36.085-05:00 level=WARN source=server.go:113 msg="server crash 60 - exit code 1 - respawning" time=2024-03-25T12:39:36.597-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:40:36.611-05:00 level=WARN source=server.go:113 msg="server crash 61 - exit code 1 - respawning" time=2024-03-25T12:40:37.120-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:41:38.134-05:00 level=WARN source=server.go:113 msg="server crash 62 - exit code 1 - respawning" time=2024-03-25T12:41:38.642-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:42:40.653-05:00 level=WARN source=server.go:113 msg="server crash 63 - exit code 1 - respawning" time=2024-03-25T12:42:41.167-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:43:44.169-05:00 level=WARN source=server.go:113 msg="server crash 64 - exit code 1 - respawning" time=2024-03-25T12:43:44.681-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:44:48.683-05:00 level=WARN source=server.go:113 msg="server crash 65 - exit code 1 - respawning" time=2024-03-25T12:44:49.190-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:45:54.201-05:00 level=WARN source=server.go:113 msg="server crash 66 - exit code 1 - respawning" time=2024-03-25T12:45:54.714-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:47:00.715-05:00 level=WARN source=server.go:113 msg="server crash 67 - exit code 1 - respawning" time=2024-03-25T12:47:01.226-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:48:08.233-05:00 level=WARN source=server.go:113 msg="server crash 68 - exit code 1 - respawning" time=2024-03-25T12:48:08.747-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:49:16.758-05:00 level=WARN source=server.go:113 msg="server crash 69 - exit code 1 - respawning" time=2024-03-25T12:49:17.269-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:50:26.284-05:00 level=WARN source=server.go:113 msg="server crash 70 - exit code 1 - respawning" time=2024-03-25T12:50:26.796-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:51:36.803-05:00 level=WARN source=server.go:113 msg="server crash 71 - exit code 1 - respawning" time=2024-03-25T12:51:37.313-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:52:48.320-05:00 level=WARN source=server.go:113 msg="server crash 72 - exit code 1 - respawning" time=2024-03-25T12:52:48.830-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:54:00.841-05:00 level=WARN source=server.go:113 msg="server crash 73 - exit code 1 - respawning" time=2024-03-25T12:54:01.353-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:55:14.366-05:00 level=WARN source=server.go:113 msg="server crash 74 - exit code 1 - respawning" time=2024-03-25T12:55:14.880-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:56:28.893-05:00 level=WARN source=server.go:113 msg="server crash 75 - exit code 1 - respawning" time=2024-03-25T12:56:29.394-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:57:44.407-05:00 level=WARN source=server.go:113 msg="server crash 76 - exit code 1 - respawning" time=2024-03-25T12:57:44.917-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T12:59:00.925-05:00 level=WARN source=server.go:113 msg="server crash 77 - exit code 1 - respawning" time=2024-03-25T12:59:01.436-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T13:00:18.439-05:00 level=WARN source=server.go:113 msg="server crash 78 - exit code 1 - respawning" time=2024-03-25T13:00:18.951-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T13:01:36.954-05:00 level=WARN source=server.go:113 msg="server crash 79 - exit code 1 - respawning" time=2024-03-25T13:01:37.466-05:00 level=ERROR source=server.go:116 msg="failed to restart server exec: already started" time=2024-03-25T13:02:24.837-05:00 level=INFO source=logging.go:45 msg="ollama app started" time=2024-03-25T13:02:24.898-05:00 level=INFO source=server.go:135 msg="unable to connect to server" In the server log it shows Error: listen tcp 127.0.0.1:11434: bind: An attempt was made to access a socket in a way forbidden by its access permissions.

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https://github.com/ollama/ollama/issues/5953

I_kwDOJ0Z1Ps6Q21xt

Who are you?

closed

NONE

### What is the issue?  ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.2.8

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I_kwDOJ0Z1Ps6PGv66

Running latest version 0.2.1 running slowly and not returning output for long text input

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### What is the issue? I am running ollama on an AWS ml.p3.2xlarge SageMaker notebook instance. When I install the latest version, 0.2.1, the response time on a langchain chain running an extract names prompt on a page of text using llama3:latest is about 8 seconds and doesn't return any names. However, when I install version 0.1.37, the response time goes down to under a second and I get an accurate response with people's names found in the text. ### OS Linux ### GPU Nvidia ### CPU _No response_ ### Ollama version 0.2.1

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I_kwDOJ0Z1Ps57kXM9

ollama list flags help

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NONE

There is no obvious way of seeing what flags are available for ollama list ``` ollama list --help List models Usage: ollama list [flags] Aliases: list, ls Flags: -h, --help help for list ```

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I_kwDOJ0Z1Ps6Amdys

bug? - session save does not save latest messages of the chat

closed

NONE

I was having a very long conversation with nollama/mythomax-l2-13b:Q5_K_S, saved the session and restored it and found that the latest 100-200 lines of the discussion were missing. I haven't tried to reproduce it (I don't have lengthy chats often), but I thought I'd report it. When I get another chance, I'll test it again

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PR_kwDOJ0Z1Ps5zq6Yg

Ollama Show: Check for Projector Type

closed

CONTRIBUTOR

Fixes #5289 <img width="410" alt="Screenshot 2024-06-26 at 11 21 57 AM" src="https://github.com/ollama/ollama/assets/65097070/4ae18164-e5c2-453b-91d4-de54569b8e11">

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PR_kwDOJ0Z1Ps6A3i20

build: Make target improvements

closed

COLLABORATOR

Add a few new targets and help for building locally. This also adjusts the runner lookup to favor local builds, then runners relative to the executable. Fixes #7491 Fixes #7483 Fixes #7452 Fixes #2187 Fixes #2205 Fixes #2281 Fixes #7457 Fixes #7622 Fixes #7577 Fixes #1756 Fixes #7817 Fixes #6857 Carries #7199

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PR_kwDOJ0Z1Ps5r8bOC

Update generate scripts with new `LLAMA_CUDA` variable, set `HIP_PLATFORM` on Windows to avoid compiler errors

closed

MEMBER

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I_kwDOJ0Z1Ps6W-2ix

Setting temperature on any llava model makes the Ollama server hangs on REST calls

closed

NONE

### What is the issue? When calling llava models from a REST client, setting temperature cause the ollama server hangs until process is killed. ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.3.10

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I_kwDOJ0Z1Ps6M5LnH

Add HelpingAI-9B in it

open

NONE

HelpingAI-9B is an advanced language model designed for emotionally intelligent conversational interactions. This model excels in empathetic engagement, understanding user emotions, and providing supportive dialogue.

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I_kwDOJ0Z1Ps6EN2Js

Bug in MODEL download directory and launching ollama service in Linux

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NONE

I write this post to add more information: 1 - As you mentioned : I edited `sudo systemctl edit ollama.service`  And the /media/Samsung/ollama_models is empty....  So seems here a bug ( as said before the doc says you have to change the ollama.service file) 2 - ollama serve vs systemd I run systemd start ollama ( today i booted my computer), and fails  But if i run ollama serve it seems to work ( i again just to be sure i started ollama, then see the status...and executed ollama serve):  And in other tab seems ollama works:  3 - where are the model downloaded: As posted before /media/Samsung/ollama_models -> as you can see is empty /home/ollama -> doesnt exist  and /usr/share/ollama ->  im going mad ;) Thans for your help Editing post for update: Finally i found the ollama model at /home/eduardo/.ollama, but it shouldnt be there as default directory is /usr/share/ollama/.ollama, and i set the environment variable OLLAMA_MODEL to point to /media/Samsung/ollama_models _Originally posted by @ejgutierrez74 in https://github.com/ollama/ollama/issues/3045#issuecomment-1991349181_

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PR_kwDOJ0Z1Ps58nEbk

Fix Ollama silently failing on extra, unsupported openai parameters.

open

NONE

Currently Ollama will just let you send unsupported api params to the openai compatible endpoint and just silently fail. This wreaks havoc on downstream uses causing unexpected behaviour. ```python # Retrieve the API key and ensure it's set ollama_api_key = os.getenv("OLLAMA_API_KEY") if not ollama_api_key: raise ValueError("API key not found. Please ensure that the OLLAMA_API_KEY is set in your .env.local file.") # Initialize the OpenAI client ollama_client = OpenAI( base_url="http://localhost:11434/v1", api_key=ollama_api_key, ) model = "llama3.1:latest" # Construct the completion request with n=2 response = openai.Completion.create( model=model, prompt="Once upon a time", max_tokens=50, n=2, # Requesting two completions temperature=0.7 ) print(f"Number of completions requested: 2") print(f"Number of completions received: {len(response.choices)}\n") assert len(response.choices) == 2, "Shouldn't ever get here" # Iterate and print each completion for idx, choice in enumerate(response.choices, 1): print(f"Completion {idx}: {choice.text.strip()}") ``` This leads to ``` umber of completions requested: 2 Number of completions received: 1 Completion 1: Once upon a time, in a land where the sun always shone brightly, there lived a young adventurer eager to explore uncharted territories and uncover hidden treasures. ``` My change would cause the API to error because that parameter is just plainly not supported by ollama

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I_kwDOJ0Z1Ps6EJYdm

prompt_eval_count in api is broken

closed

CONTRIBUTOR

### What is the issue? prompt_eval_count parameter is absent on some calls, on other calls it returns wrong information. 1. i tried /api/chat with "stablelm2", no system prompt, prompt="hi". in result there is no field "prompt_eval_count" most of the time. sometimes it's there, randomly, but rarely. 2. when you have small num_ctx and the supplied prompt (content of all messages in /api/chat) exceeds the num_ctx size, the prompt_eval_count may either be absent or provide wrong information. i believe it returns the amount of tokens that could fit the context window, instead of the whole context prompt that was sent. thanks :). ### What did you expect to see? expected behavior: 1. there should always be prompt_eval_count. 2. it should report the count of submitted tokens, not the processed ones. 3. optionally there maybe one more parameter returned, showing the amount of processed tokens. ### Steps to reproduce use /api/chat to send array of messages, limit num_ctx, send longer content than fits into the context window defined with num_ctx, check the values returned in final json reply with done==true. ### Are there any recent changes that introduced the issue? _No response_ ### OS Linux ### Architecture amd64 ### Platform _No response_ ### Ollama version 0.1.30 ### GPU Other ### GPU info cpu only ### CPU AMD ### Other software _No response_

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I_kwDOJ0Z1Ps6lnBr2

deepseek v3

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I_kwDOJ0Z1Ps6IOvl5

Error: could not connect to ollama app, is it running?

closed

NONE

### What is the issue? When try run command "Ollama list", the following error occurs: server.log 2024/05/08 20:50:26 routes.go:989: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[* http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:*] OLLAMA_RUNNERS_DIR:C:\\Users\\Qiang.Liu\\AppData\\Local\\Programs\\Ollama\\ollama_runners OLLAMA_TMPDIR:]" time=2024-05-08T20:50:26.065+08:00 level=INFO source=images.go:897 msg="total blobs: 16" time=2024-05-08T20:50:26.068+08:00 level=INFO source=images.go:904 msg="total unused blobs removed: 0" time=2024-05-08T20:50:26.071+08:00 level=INFO source=routes.go:1034 msg="Listening on 127.0.0.1:11434 (version 0.1.34)" time=2024-05-08T20:50:26.071+08:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11.3 rocm_v5.7]" time=2024-05-08T20:50:26.071+08:00 level=INFO source=gpu.go:122 msg="Detecting GPUs" time=2024-05-08T20:50:26.091+08:00 level=INFO source=cpu_common.go:15 msg="CPU has AVX" Exception 0xc0000005 0x8 0x228ed7065e0 0x228ed7065e0 PC=0x228ed7065e0 signal arrived during external code execution runtime.cgocall(0xf73c20, 0xc000680408) runtime/cgocall.go:157 +0x3e fp=0xc0003850d0 sp=0xc000385098 pc=0xf092fe syscall.SyscallN(0x7ffdc82ddcd0?, {0xc0000ff758?, 0x1?, 0x7ffdc7fd0000?}) runtime/syscall_windows.go:544 +0x107 fp=0xc000385148 sp=0xc0003850d0 pc=0xf6f147 github.com/ollama/ollama/gpu.(*HipLib).AMDDriverVersion(0xc0000be750) github.com/ollama/ollama/gpu/amd_hip_windows.go:82 +0x69 fp=0xc0003851b8 sp=0xc000385148 pc=0x139fc09 github.com/ollama/ollama/gpu.AMDGetGPUInfo() github.com/ollama/ollama/gpu/amd_windows.go:37 +0x91 fp=0xc000385840 sp=0xc0003851b8 pc=0x13a0331 github.com/ollama/ollama/gpu.GetGPUInfo() github.com/ollama/ollama/gpu/gpu.go:214 +0x625 fp=0xc000385ae8 sp=0xc000385840 pc=0x13a4845 github.com/ollama/ollama/server.Serve({0x2009900, 0xc000276bc0}) github.com/ollama/ollama/server/routes.go:1059 +0x771 fp=0xc000385c70 sp=0xc000385ae8 pc=0x19ef231 github.com/ollama/ollama/cmd.RunServer(0xc0000a8b00?, {0x27b2ae0?, 0x4?, 0x1e6cc3e?}) github.com/ollama/ollama/cmd/cmd.go:901 +0x17c fp=0xc000385d58 sp=0xc000385c70 pc=0x1a092dc github.com/spf13/cobra.(*Command).execute(0xc0004b4908, {0x27b2ae0, 0x0, 0x0}) github.com/spf13/[email protected]/command.go:940 +0x882 fp=0xc000385e78 sp=0xc000385d58 pc=0x12aaa22 github.com/spf13/cobra.(*Command).ExecuteC(0xc00002d808) github.com/spf13/[email protected]/command.go:1068 +0x3a5 fp=0xc000385f30 sp=0xc000385e78 pc=0x12ab265 github.com/spf13/cobra.(*Command).Execute(...) github.com/spf13/[email protected]/command.go:992 github.com/spf13/cobra.(*Command).ExecuteContext(...) github.com/spf13/[email protected]/command.go:985 main.main() github.com/ollama/ollama/main.go:11 +0x4d fp=0xc000385f50 sp=0xc000385f30 pc=0x1a1208d runtime.main() runtime/proc.go:271 +0x28b fp=0xc000385fe0 sp=0xc000385f50 pc=0xf4134b runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000385fe8 sp=0xc000385fe0 pc=0xf72461 goroutine 2 gp=0xc000084700 m=nil [force gc (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000087fa8 sp=0xc000087f88 pc=0xf4174e runtime.goparkunlock(...) runtime/proc.go:408 runtime.forcegchelper() runtime/proc.go:326 +0xb8 fp=0xc000087fe0 sp=0xc000087fa8 pc=0xf415d8 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000087fe8 sp=0xc000087fe0 pc=0xf72461 created by runtime.init.6 in goroutine 1 runtime/proc.go:314 +0x1a goroutine 3 gp=0xc000084a80 m=nil [GC sweep wait]: runtime.gopark(0x1?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000089f80 sp=0xc000089f60 pc=0xf4174e runtime.goparkunlock(...) runtime/proc.go:408 runtime.bgsweep(0xc00003a070) runtime/mgcsweep.go:318 +0xdf fp=0xc000089fc8 sp=0xc000089f80 pc=0xf2b7ff runtime.gcenable.gowrap1() runtime/mgc.go:203 +0x25 fp=0xc000089fe0 sp=0xc000089fc8 pc=0xf200a5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000089fe8 sp=0xc000089fe0 pc=0xf72461 created by runtime.gcenable in goroutine 1 runtime/mgc.go:203 +0x66 goroutine 4 gp=0xc000084c40 m=nil [GC scavenge wait]: runtime.gopark(0x10000?, 0x1ffb9f0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000099f78 sp=0xc000099f58 pc=0xf4174e runtime.goparkunlock(...) runtime/proc.go:408 runtime.(*scavengerState).park(0x2726fc0) runtime/mgcscavenge.go:425 +0x49 fp=0xc000099fa8 sp=0xc000099f78 pc=0xf29189 runtime.bgscavenge(0xc00003a070) runtime/mgcscavenge.go:658 +0x59 fp=0xc000099fc8 sp=0xc000099fa8 pc=0xf29739 runtime.gcenable.gowrap2() runtime/mgc.go:204 +0x25 fp=0xc000099fe0 sp=0xc000099fc8 pc=0xf20045 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000099fe8 sp=0xc000099fe0 pc=0xf72461 created by runtime.gcenable in goroutine 1 runtime/mgc.go:204 +0xa5 goroutine 18 gp=0xc000104380 m=nil [finalizer wait]: runtime.gopark(0xc00008be48?, 0xf13445?, 0xa8?, 0x1?, 0xc000084000?) runtime/proc.go:402 +0xce fp=0xc00008be20 sp=0xc00008be00 pc=0xf4174e runtime.runfinq() runtime/mfinal.go:194 +0x107 fp=0xc00008bfe0 sp=0xc00008be20 pc=0xf1f127 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00008bfe8 sp=0xc00008bfe0 pc=0xf72461 created by runtime.createfing in goroutine 1 runtime/mfinal.go:164 +0x3d goroutine 5 gp=0xc000085340 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc00009bf50 sp=0xc00009bf30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc00009bfe0 sp=0xc00009bf50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00009bfe8 sp=0xc00009bfe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 19 gp=0xc000104fc0 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000095f50 sp=0xc000095f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000095fe0 sp=0xc000095f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000095fe8 sp=0xc000095fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 6 gp=0xc000085500 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc00041df50 sp=0xc00041df30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc00041dfe0 sp=0xc00041df50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00041dfe8 sp=0xc00041dfe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 20 gp=0xc000105180 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000097f50 sp=0xc000097f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000097fe0 sp=0xc000097f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000097fe8 sp=0xc000097fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 7 gp=0xc0000856c0 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc00041ff50 sp=0xc00041ff30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc00041ffe0 sp=0xc00041ff50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00041ffe8 sp=0xc00041ffe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 21 gp=0xc000105340 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000419f50 sp=0xc000419f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000419fe0 sp=0xc000419f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000419fe8 sp=0xc000419fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 8 gp=0xc000085880 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000425f50 sp=0xc000425f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000425fe0 sp=0xc000425f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000425fe8 sp=0xc000425fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 22 gp=0xc000105500 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc00041bf50 sp=0xc00041bf30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc00041bfe0 sp=0xc00041bf50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00041bfe8 sp=0xc00041bfe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 9 gp=0xc000085a40 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000427f50 sp=0xc000427f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000427fe0 sp=0xc000427f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000427fe8 sp=0xc000427fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 23 gp=0xc0001056c0 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000421f50 sp=0xc000421f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000421fe0 sp=0xc000421f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000421fe8 sp=0xc000421fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 10 gp=0xc000085c00 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc00042df50 sp=0xc00042df30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc00042dfe0 sp=0xc00042df50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00042dfe8 sp=0xc00042dfe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 24 gp=0xc000105880 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000423f50 sp=0xc000423f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000423fe0 sp=0xc000423f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000423fe8 sp=0xc000423fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 11 gp=0xc000085dc0 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc00042ff50 sp=0xc00042ff30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc00042ffe0 sp=0xc00042ff50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00042ffe8 sp=0xc00042ffe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 34 gp=0xc000482000 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000429f50 sp=0xc000429f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000429fe0 sp=0xc000429f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000429fe8 sp=0xc000429fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 25 gp=0xc000105a40 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002f5f50 sp=0xc0002f5f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002f5fe0 sp=0xc0002f5f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002f5fe8 sp=0xc0002f5fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 35 gp=0xc0004821c0 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc00042bf50 sp=0xc00042bf30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc00042bfe0 sp=0xc00042bf50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00042bfe8 sp=0xc00042bfe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 26 gp=0xc000105c00 m=nil [GC worker (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002f7f50 sp=0xc0002f7f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002f7fe0 sp=0xc0002f7f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002f7fe8 sp=0xc0002f7fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 36 gp=0xc000482380 m=nil [GC worker (idle)]: runtime.gopark(0xa5c45b8f8c?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002f1f50 sp=0xc0002f1f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002f1fe0 sp=0xc0002f1f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002f1fe8 sp=0xc0002f1fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 27 gp=0xc000105dc0 m=nil [GC worker (idle)]: runtime.gopark(0xa5c45b8f8c?, 0x3?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002fdf50 sp=0xc0002fdf30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002fdfe0 sp=0xc0002fdf50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002fdfe8 sp=0xc0002fdfe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 37 gp=0xc000482540 m=nil [GC worker (idle)]: runtime.gopark(0x27b4a60?, 0x1?, 0x4c?, 0xcc?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002f3f50 sp=0xc0002f3f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002f3fe0 sp=0xc0002f3f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002f3fe8 sp=0xc0002f3fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 28 gp=0xc000500000 m=nil [GC worker (idle)]: runtime.gopark(0x27b4a60?, 0x1?, 0x9c?, 0x61?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002fff50 sp=0xc0002fff30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002fffe0 sp=0xc0002fff50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002fffe8 sp=0xc0002fffe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 38 gp=0xc000482700 m=nil [GC worker (idle)]: runtime.gopark(0x27b4a60?, 0x1?, 0x7c?, 0xd9?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002f9f50 sp=0xc0002f9f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002f9fe0 sp=0xc0002f9f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002f9fe8 sp=0xc0002f9fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 29 gp=0xc0005001c0 m=nil [GC worker (idle)]: runtime.gopark(0x27b4a60?, 0x1?, 0xd4?, 0xaf?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000507f50 sp=0xc000507f30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000507fe0 sp=0xc000507f50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000507fe8 sp=0xc000507fe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 39 gp=0xc0004828c0 m=nil [GC worker (idle)]: runtime.gopark(0xa5c45b8f8c?, 0x1?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0002fbf50 sp=0xc0002fbf30 pc=0xf4174e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0002fbfe0 sp=0xc0002fbf50 pc=0xf221e5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0002fbfe8 sp=0xc0002fbfe0 pc=0xf72461 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 12 gp=0xc0005841c0 m=5 mp=0xc000100008 [syscall]: runtime.notetsleepg(0x27b36a0, 0xffffffffffffffff) runtime/lock_sema.go:296 +0x31 fp=0xc000505fa0 sp=0xc000505f68 pc=0xf11a11 os/signal.signal_recv() runtime/sigqueue.go:152 +0x29 fp=0xc000505fc0 sp=0xc000505fa0 pc=0xf6e169 os/signal.loop() os/signal/signal_unix.go:23 +0x13 fp=0xc000505fe0 sp=0xc000505fc0 pc=0x12335d3 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000505fe8 sp=0xc000505fe0 pc=0xf72461 created by os/signal.Notify.func1.1 in goroutine 1 os/signal/signal.go:151 +0x1f goroutine 13 gp=0xc000584380 m=nil [chan receive]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000509f08 sp=0xc000509ee8 pc=0xf4174e runtime.chanrecv(0xc0004ce4e0, 0x0, 0x1) runtime/chan.go:583 +0x3cd fp=0xc000509f80 sp=0xc000509f08 pc=0xf0b98d runtime.chanrecv1(0x0?, 0x0?) runtime/chan.go:442 +0x12 fp=0xc000509fa8 sp=0xc000509f80 pc=0xf0b592 github.com/ollama/ollama/server.Serve.func2() github.com/ollama/ollama/server/routes.go:1043 +0x34 fp=0xc000509fe0 sp=0xc000509fa8 pc=0x19ef2f4 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000509fe8 sp=0xc000509fe0 pc=0xf72461 created by github.com/ollama/ollama/server.Serve in goroutine 1 github.com/ollama/ollama/server/routes.go:1042 +0x6f7 goroutine 14 gp=0xc000584540 m=nil [select]: runtime.gopark(0xc00047df58?, 0x3?, 0x60?, 0x0?, 0xc00047de32?) runtime/proc.go:402 +0xce fp=0xc00047dcb8 sp=0xc00047dc98 pc=0xf4174e runtime.selectgo(0xc00047df58, 0xc00047de2c, 0x0?, 0x0, 0x0?, 0x1) runtime/select.go:327 +0x725 fp=0xc00047ddd8 sp=0xc00047dcb8 pc=0xf51ba5 github.com/ollama/ollama/server.(*Scheduler).processPending(0xc000174af0, {0x200c0c0, 0xc000174aa0}) github.com/ollama/ollama/server/sched.go:97 +0xcf fp=0xc00047dfb8 sp=0xc00047ddd8 pc=0x19f27af github.com/ollama/ollama/server.(*Scheduler).Run.func1() github.com/ollama/ollama/server/sched.go:87 +0x1f fp=0xc00047dfe0 sp=0xc00047dfb8 pc=0x19f26bf runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00047dfe8 sp=0xc00047dfe0 pc=0xf72461 created by github.com/ollama/ollama/server.(*Scheduler).Run in goroutine 1 github.com/ollama/ollama/server/sched.go:86 +0xb4 goroutine 15 gp=0xc000584700 m=nil [select]: runtime.gopark(0xc000503f50?, 0x3?, 0x0?, 0x0?, 0xc000503d5a?) runtime/proc.go:402 +0xce fp=0xc000503be8 sp=0xc000503bc8 pc=0xf4174e runtime.selectgo(0xc000503f50, 0xc000503d54, 0x0?, 0x0, 0x0?, 0x1) runtime/select.go:327 +0x725 fp=0xc000503d08 sp=0xc000503be8 pc=0xf51ba5 github.com/ollama/ollama/server.(*Scheduler).processCompleted(0xc000174af0, {0x200c0c0, 0xc000174aa0}) github.com/ollama/ollama/server/sched.go:209 +0xec fp=0xc000503fb8 sp=0xc000503d08 pc=0x19f332c github.com/ollama/ollama/server.(*Scheduler).Run.func2() github.com/ollama/ollama/server/sched.go:91 +0x1f fp=0xc000503fe0 sp=0xc000503fb8 pc=0x19f267f runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000503fe8 sp=0xc000503fe0 pc=0xf72461 created by github.com/ollama/ollama/server.(*Scheduler).Run in goroutine 1 github.com/ollama/ollama/server/sched.go:90 +0x110 rax 0x228f4cf83f0 rbx 0x228ed7065e0 rcx 0xd3d rdx 0x6 rdi 0x228ed7117f0 rsi 0x228f4cf83f0 rbp 0xd3c rsp 0x45e97fe108 r8 0x2 r9 0x40 r10 0x80 r11 0x22000000007f4100 r12 0x1 r13 0x0 r14 0x228f4b98340 r15 0x228ed7065e0 rip 0x228ed7065e0 rflags 0x10202 cs 0x33 fs 0x53 gs 0x2b ### OS Windows ### GPU AMD ### CPU Intel ### Ollama version 0.1.34

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https://github.com/ollama/ollama/issues/551

I_kwDOJ0Z1Ps5xWNUv

Dockerfile.cuda fails to build server

closed

CONTRIBUTOR

On an AWS EC2 `g4dn.2xlarge` instance with Ollama https://github.com/jmorganca/ollama/tree/c345053a8bf47d5ef8f1fe15d385108059209fba: ```none > sudo docker buildx build . --file Dockerfile.cuda [+] Building 57.2s (7/16) docker:default => => transferring context: 97B 0.0s [+] Building 57.3s (7/16) docker:default => => transferring dockerfile: 939B 0.0s [+] Building 113.7s (15/16) docker:default => [internal] load .dockerignore 0.0s => => transferring context: 97B 0.0s => [internal] load build definition from Dockerfile.cuda 0.0s => => transferring dockerfile: 939B 0.0s => [internal] load metadata for docker.io/nvidia/cuda:12.2.0-runtime-ubuntu22.04 1.0s => [internal] load metadata for docker.io/nvidia/cuda:12.2.0-devel-ubuntu22.04 0.9s => [stage-0 1/7] FROM docker.io/nvidia/cuda:12.2.0-devel-ubuntu22.04@sha256:0e2d7e252847c334b056937e533683556926f5343a472b6b92f858a7af8ab880 71.6s ... => [stage-1 2/3] RUN groupadd ollama && useradd -m -g ollama ollama 20.6s => [stage-0 2/7] WORKDIR /go/src/github.com/jmorganca/ollama 21.0s => [stage-0 3/7] RUN apt-get update && apt-get install -y git build-essential cmake 13.5s => [stage-0 4/7] ADD https://dl.google.com/go/go1.21.1.linux-amd64.tar.gz /tmp/go1.21.1.tar.gz 0.4s => [stage-0 5/7] RUN mkdir -p /usr/local && tar xz -C /usr/local </tmp/go1.21.1.tar.gz 3.2s => [stage-0 6/7] COPY . . 0.0s => ERROR [stage-0 7/7] RUN /usr/local/go/bin/go generate ./... && /usr/local/go/bin/go build -ldflags "-linkmode=external -extldflags='-static' -X=github.com/jmorganca/ollama/version.Version=0.0.0 -X= 2.9s ------ > [stage-0 7/7] RUN /usr/local/go/bin/go generate ./... && /usr/local/go/bin/go build -ldflags "-linkmode=external -extldflags='-static' -X=github.com/jmorganca/ollama/version.Version=0.0.0 -X=github.com/jmorganca/ollama/server.mode=release" .: 0.377 go: downloading github.com/mattn/go-runewidth v0.0.14 0.377 go: downloading github.com/mitchellh/colorstring v0.0.0-20190213212951-d06e56a500db 0.378 go: downloading golang.org/x/term v0.10.0 0.385 go: downloading gonum.org/v1/gonum v0.13.0 0.388 go: downloading github.com/chzyer/readline v1.5.1 0.388 go: downloading golang.org/x/crypto v0.10.0 0.391 go: downloading github.com/pbnjay/memory v0.0.0-20210728143218-7b4eea64cf58 0.391 go: downloading github.com/dustin/go-humanize v1.0.1 0.478 go: downloading github.com/gin-contrib/cors v1.4.0 0.480 go: downloading github.com/olekukonko/tablewriter v0.0.5 0.483 go: downloading github.com/gin-gonic/gin v1.9.1 0.486 go: downloading github.com/spf13/cobra v1.7.0 0.487 go: downloading golang.org/x/exp v0.0.0-20230817173708-d852ddb80c63 0.565 go: downloading golang.org/x/sys v0.11.0 0.566 go: downloading github.com/rivo/uniseg v0.2.0 0.573 go: downloading github.com/spf13/pflag v1.0.5 0.580 go: downloading github.com/gin-contrib/sse v0.1.0 0.580 go: downloading github.com/mattn/go-isatty v0.0.19 0.597 go: downloading golang.org/x/net v0.10.0 0.624 go: downloading github.com/go-playground/validator/v10 v10.14.0 0.624 go: downloading github.com/pelletier/go-toml/v2 v2.0.8 0.651 go: downloading github.com/ugorji/go/codec v1.2.11 0.700 go: downloading google.golang.org/protobuf v1.30.0 0.707 go: downloading gopkg.in/yaml.v3 v3.0.1 0.741 go: downloading github.com/gabriel-vasile/mimetype v1.4.2 0.742 go: downloading github.com/go-playground/universal-translator v0.18.1 0.745 go: downloading github.com/leodido/go-urn v1.2.4 0.778 go: downloading golang.org/x/text v0.10.0 0.841 go: downloading github.com/go-playground/locales v0.14.1 1.894 fatal: not a git repository: /go/src/github.com/jmorganca/ollama/../.git/modules/ollama 1.895 llm/llama.cpp/generate.go:6: running "git": exit status 128 ------ Dockerfile.cuda:13 -------------------- 12 | ENV GOARCH=$TARGETARCH 13 | >>> RUN /usr/local/go/bin/go generate ./... \ 14 | >>> && /usr/local/go/bin/go build -ldflags "-linkmode=external -extldflags='-static' -X=github.com/jmorganca/ollama/version.Version=$VERSION -X=github.com/jmorganca/ollama/server.mode=release" . 15 | -------------------- ERROR: failed to solve: process "/bin/sh -c /usr/local/go/bin/go generate ./... && /usr/local/go/bin/go build -ldflags \"-linkmode=external -extldflags='-static' -X=github.com/jmorganca/ollama/version.Version=$VERSION -X=github.com/jmorganca/ollama/server.mode=release\" ." did not complete successfully: exit code: 1 ``` [Line 13](https://github.com/jmorganca/ollama/blob/c345053a8bf47d5ef8f1fe15d385108059209fba/Dockerfile.cuda#L13) fails with: ``` ERROR: failed to solve: process "/bin/sh -c /usr/local/go/bin/go generate ./... && /usr/local/go/bin/go build -ldflags \"-linkmode=external -extldflags='-static' -X=github.com/jmorganca/ollama/version.Version=$VERSION -X=github.com/jmorganca/ollama/server.mode=release\" ." did not complete successfully: exit code: 1 ``` Any idea what's going on here?

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I_kwDOJ0Z1Ps6Q_Zov

Tools and properties.type Not Supporting Arrays

open

NONE

### What is the issue? **Title:** Issue with `DynamicStructuredTool` and `properties.type` Not Supporting Arrays in LangchainJS **Description:** I am encountering an issue when using `DynamicStructuredTool` in LangchainJS. Specifically, the `type` property within `properties` does not currently support arrays. This results in an error when I try to use a nullable argument specified as `["string", "null"]`. The error message is as follows: ``` LLM run errored with error: "HTTP error! status: 400 Response: {\"error\":{\"message\":\"json: cannot unmarshal array into Go struct field .tools.function.parameters.properties.type of type string\",\"type\":\"invalid_request_error\",\"param\":null,\"code\":null}}\n \nMistralAPIError: HTTP error! status: 400 Response: {\"error\":{\"message\":\"json: cannot unmarshal array into Go struct field .tools.function.parameters.properties.type of type string\",\"type\":\"invalid_request_error\",\"param\":null,\"code\":null}}\n at MistralClient._request (file:///Users/xonlly/projects/devana.ai/serveur/node_modules/@mistralai/mistralai/src/client.js:162:17)\n at processTicksAndRejections (node:internal/process/task_queues:95:5)\n at MistralClient.chat (file:///Users/xonlly/projects/devana.ai/serveur/node_modules/@mistralai/mistralai/src/client.js:338:22)\n at /Users/xonlly/projects/devana.ai/serveur/node_modules/@langchain/mistralai/dist/chat_models.cjs:366:27\n at RetryOperation._fn (/Users/xonlly/projects/devana.ai/serveur/node_modules/p-retry/index.js:50:12)" ``` **JSON Configuration:** ```json { "model": "mistral-nemo", "messages": [ { "role": "user", "content": "Hello" } ], "tools": [ { "type": "function", "function": { "name": "search-files", "description": "Search all files in the collection", "parameters": { "type": "object", "properties": { "query": { "type": ["string", "null"] } }, "required": ["query"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#" } } } ], "temperature": 0.7, "top_p": 1, "stream": false, "tool_choice": "required" } ``` **Steps to Reproduce:** 1. Use LangchainJS with `DynamicStructuredTool`. 2. Define a nullable argument with `["string", "null"]` in the `type` property. 3. Observe the error upon execution. **Expected Behavior:** The `type` property should accept arrays to support nullable arguments without causing an error. **Actual Behavior:** An error occurs because the `type` property does not support arrays, resulting in a failure to unmarshal the array into a Go struct field. **Proposed Solution:** Update the `type` property within `properties` to support arrays, allowing for nullable arguments to be correctly processed. **Environment:** - Ollama version: 0.3.0 - Operating System: Ubuntu 22.04 **Additional Context:** Any guidance or updates to address this limitation would be greatly appreciated. Thank you! ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.3.0

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I_kwDOJ0Z1Ps6X2yQj

AMD ROCm Card can not use flash attention

open

NONE

### What is the issue? My cards is w7900, and rocm driver is 6.3 , I found the llama-cpp server started by Ollama always without -fa flag. I check the code , found : // only cuda (compute capability 7+) and metal support flash attention if g.Library != "metal" && (g.Library != "cuda" || g.DriverMajor < 7) { flashAttnEnabled = false } This code sames wrong. Ref: https://github.com/Dao-AILab/flash-attention/pull/1010 Support for RoCM has been added tk flash attention 2 ### OS Linux ### GPU AMD ### CPU Intel ### Ollama version _No response_

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I_kwDOJ0Z1Ps6VmC3k

expose slots data through API

closed

NONE

hI Can the information that can be seen in the logs be exposed through /slots api per server/port ? We need this to manage queuing in our load balancer. This has been exposed by llama cpp already. https://github.com/ggerganov/llama.cpp/tree/master/examples/server#get-slots-returns-the-current-slots-processing-state DEBUG [update_slots] all slots are idle and system prompt is empty, clear the KV cache | tid="140031137009664" timestamp=1725608401 DEBUG [process_single_task] slot data | n_idle_slots=3 n_processing_slots=0 task_id=0 tid="140031137009664" timestamp=1725608401 Many thanks!

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I_kwDOJ0Z1Ps6ILIqK

error loading model architecture: unknown model architecture: 'qwen2moe'

open

NONE

### What is the issue? Qwen1.5-MoE-A2.7B-Chat is installed by convert-hf-to-gguf.py according to the process. After 4-bit quantization, ollamamodelfile is created, but it is not supported when loading. What is the cause of this? ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.1.32

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PR_kwDOJ0Z1Ps5zF3gJ

fix: skip os.removeAll() if PID does not exist

closed

CONTRIBUTOR

previously deleted all directories in $TMPDIR starting with ollama. Added a "continue" to skip the directory removal if a PID doesn't exist. We do this to prevent accidentally deleting directories in tmpdir that share the ollama name but aren't created by us for processes resolves: https://github.com/ollama/ollama/issues/5129

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I_kwDOJ0Z1Ps6H-FPv

Support pull from habor registry in proxy mode an push to harbor

open

NONE

Not sure why its not working, maybe i do something bad. From other ticket i understand it supposed to work with OCI registry. What i tried : ollama pull habor-server//ollama.com/library/llama3:text Error: pull model manifest: 400 ollama pull habor-server/ollama.com/llama3:text Error: pull model manifest: 400 ollama cp llama2:7b habor-server/aistuff/llama2:7b ollama push habor-server/aistuff/llama2:7b retrieving manifest Error: Get "harbor?nonce=zzzz&scope=&service=&ts=xxxx": unsupported protocol scheme ""

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PR_kwDOJ0Z1Ps5gDJXg

Make alt+backspace delete word

closed

CONTRIBUTOR

In GNU Readline you can press alt+backspace to delete word. I'm used to this behavior and so it's jarring not to be able to do it. This commit adds the feature.

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I_kwDOJ0Z1Ps6BurtU

Search on ollama.com/library is missing lots of models

open

NONE

Current behavior: Using @ehartford as an example since he's a prolific ollama model contributor: https://ollama.com/search?q=ehartford&p=1 Shows his models: <img width="1276" alt="Screenshot 2024-03-08 at 9 45 33 AM" src="https://github.com/ollama/ollama/assets/2172753/08b9dc80-5d94-4b86-82dd-37c0dddac326"> https://ollama.com/library?q=ehartford Does not: <img width="1257" alt="Screenshot 2024-03-08 at 9 45 52 AM" src="https://github.com/ollama/ollama/assets/2172753/6f1c248b-a93c-468a-a78c-24811a857731"> Expected behavior is that they would be the same.

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I_kwDOJ0Z1Ps6CsVZh

ollama/ollama Docker image: committed modifications aren't saved

closed

NONE

### What is the issue? I'm using the Docker image `ollama/ollama:latest`. I'm running the image and, in the new created container, I'm pulling `llama2`. Once the pull operation finished, I'm checking its success using the `ollama list` command. Now, I commit the modification, I tag the new modified image and I push it on DockerHub. Pulling it again, running it and checking the presence of `llama2` fails as the result of the `ollama list` command is empty this time. ### What did you expect to see? I expect that the new augmented image contains `llama2` ### Steps to reproduce $ docker pull ollama/ollama:latest $ docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama $ docker exec -ti ollama ollama pull llama2 ... waiting 10 minutes ... $ docker exec -ti ollama ollama list NAME ID SIZE MODIFIED llama2:latest 78e26419b446 3.8 GB About a minute ago $ docker commit ollama $ docker tag <image_id> nicolasduminil/ollama:llama2 $ docker push nicolasduminil/ollama:llama2 $ docker pull nicolasduminil/ollama:llama2 $ docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama nicolasduminil/ollama:llama2 $ docker exec -ti ollama ollama list NAME ID SIZE MODIFIED ### Are there any recent changes that introduced the issue? Negative ### OS Linux ### Architecture x86 ### Platform _No response_ ### Ollama version _No response_ ### GPU _No response_ ### GPU info _No response_ ### CPU Intel ### Other software None

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I_kwDOJ0Z1Ps6DSqQc

ollama run in national user name

closed

NONE

### What is the issue?  My Username has international characters like á and the ollama not handle it. ### What did you expect to see? RUN ### Steps to reproduce 1 Create a windows user with international charaters like: Gábor 2 start the ollama with: ollama run llama2 ### Are there any recent changes that introduced the issue? _No response_ ### OS Windows ### Architecture amd64 ### Platform _No response_ ### Ollama version _No response_ ### GPU Nvidia ### GPU info _No response_ ### CPU AMD ### Other software _No response_

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I_kwDOJ0Z1Ps6XzHhF

llama runner process has terminated: exit status 0xc0000005

open

NONE

### What is the issue? It's again the https://github.com/ollama/ollama/issues/6011 issue. **The issue is with embedding call with the model converted using convert_hf_to_gguf.py.** litellm.llms.ollama.OllamaError: {"error":"llama runner process has terminated: exit status 0xc0000005"} ``` INFO [wmain] system info | n_threads=6 n_threads_batch=6 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="18380" timestamp=1727231008 total_threads=12 INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="13505" tid="18380" timestamp=1727231008 llama_model_loader: loaded meta data with 26 key-value pairs and 389 tensors from C:\Users\Administrator\.ollama\models\blobs\sha256-aad91e93e9ec705a527cfa8701698055cf473223437acd029762bb77be6fc92d (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 = bert llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Conan_Embedding_V1 llama_model_loader: - kv 3: general.size_label str = 324M llama_model_loader: - kv 4: general.license str = cc-by-nc-4.0 llama_model_loader: - kv 5: general.tags arr[str,1] = ["mteb"] llama_model_loader: - kv 6: bert.block_count u32 = 24 llama_model_loader: - kv 7: bert.context_length u32 = 512 llama_model_loader: - kv 8: bert.embedding_length u32 = 1024 llama_model_loader: - kv 9: bert.feed_forward_length u32 = 4096 llama_model_loader: - kv 10: bert.attention.head_count u32 = 16 llama_model_loader: - kv 11: bert.attention.layer_norm_epsilon f32 = 0.000000 llama_model_loader: - kv 12: general.file_type u32 = 1 llama_model_loader: - kv 13: bert.attention.causal bool = false llama_model_loader: - kv 14: bert.pooling_type u32 = 1 llama_model_loader: - kv 15: tokenizer.ggml.token_type_count u32 = 2 llama_model_loader: - kv 16: tokenizer.ggml.model str = bert llama_model_loader: - kv 17: tokenizer.ggml.pre str = Conan-embedding-v1 llama_model_loader: - kv 18: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "... llama_model_loader: - kv 19: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 100 llama_model_loader: - kv 21: tokenizer.ggml.seperator_token_id u32 = 102 llama_model_loader: - kv 22: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 23: tokenizer.ggml.cls_token_id u32 = 101 llama_model_loader: - kv 24: tokenizer.ggml.mask_token_id u32 = 103 llama_model_loader: - kv 25: general.quantization_version u32 = 2 llama_model_loader: - type f32: 244 tensors llama_model_loader: - type f16: 145 tensors llm_load_vocab: special tokens cache size = 5 llm_load_vocab: token to piece cache size = 0.0769 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = bert llm_load_print_meta: vocab type = WPM llm_load_print_meta: n_vocab = 21128 llm_load_print_meta: n_merges = 0 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 512 llm_load_print_meta: n_embd = 1024 llm_load_print_meta: n_layer = 24 llm_load_print_meta: n_head = 16 llm_load_print_meta: n_head_kv = 16 llm_load_print_meta: n_rot = 64 llm_load_print_meta: n_swa = 0 llm_load_print_meta: n_embd_head_k = 64 llm_load_print_meta: n_embd_head_v = 64 llm_load_print_meta: n_gqa = 1 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 = 1.0e-12 llm_load_print_meta: f_norm_rms_eps = 0.0e+00 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 = 4096 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 0 llm_load_print_meta: pooling type = 1 llm_load_print_meta: rope type = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 512 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 = 335M llm_load_print_meta: model ftype = F16 llm_load_print_meta: model params = 324.47 M llm_load_print_meta: model size = 620.50 MiB (16.04 BPW) llm_load_print_meta: general.name = Conan_Embedding_V1 llm_load_print_meta: UNK token = 100 '[UNK]' llm_load_print_meta: SEP token = 102 '[SEP]' llm_load_print_meta: PAD token = 0 '[PAD]' llm_load_print_meta: CLS token = 101 '[CLS]' llm_load_print_meta: MASK token = 103 '[MASK]' llm_load_print_meta: LF token = 0 '[PAD]' llm_load_print_meta: max token length = 48 llm_load_tensors: ggml ctx size = 0.16 MiB llm_load_tensors: CPU buffer size = 620.50 MiB time=2024-09-25T10:23:28.796+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model" llama_new_context_with_model: n_ctx = 2048 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: flash_attn = 0 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 192.00 MiB llama_new_context_with_model: KV self size = 192.00 MiB, K (f16): 96.00 MiB, V (f16): 96.00 MiB llama_new_context_with_model: CPU output buffer size = 0.00 MiB llama_new_context_with_model: CPU compute buffer size = 26.00 MiB llama_new_context_with_model: graph nodes = 851 llama_new_context_with_model: graph splits = 1 time=2024-09-25T10:23:30.338+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server not responding" time=2024-09-25T10:23:31.963+08:00 level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server error" time=2024-09-25T10:23:32.226+08:00 level=ERROR source=sched.go:455 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000005" [GIN] 2024/09/25 - 10:23:32 | 500 | 3.7323168s | 127.0.0.1 | POST "/api/embed" ``` ### OS Windows ### GPU _No response_ ### CPU Intel ### Ollama version 0.3.11 0.3.12

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PR_kwDOJ0Z1Ps5t4K3P

return code `499` when user cancels request while a model is loading

closed

MEMBER

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PR_kwDOJ0Z1Ps52hIpP

Revert "llm(llama): pass rope factors"

closed

MEMBER

Reverts ollama/ollama#5924

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I_kwDOJ0Z1Ps6R5k_u

.git file is missing

closed

NONE

### What is the issue? As per the docker file present in repo there needs to be a file named .git in repo but it is missing.  ### OS Linux ### GPU _No response_ ### CPU _No response_ ### Ollama version _No response_

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I_kwDOJ0Z1Ps6Fw0Ok

error: inlining failed in call to ‘always_inline’ ‘_mm256_fmadd_ps’: target specific option mismatch

closed

NONE

### What is the issue? ``` gmake[3]: Leaving directory '/mnt/storage/tmp/ollama/ollama/llm/build/linux/x86_64/cpu' [ 8%] Built target build_info In file included from /usr/lib/gcc/x86_64-pc-linux-gnu/13/include/immintrin.h:109, from /mnt/storage/tmp/ollama/ollama/llm/llama.cpp/ggml-impl.h:93, from /mnt/storage/tmp/ollama/ollama/llm/llama.cpp/ggml-quants.c:5: /usr/lib/gcc/x86_64-pc-linux-gnu/13/include/fmaintrin.h: In function ‘ggml_vec_dot_q4_0_q8_0’: /usr/lib/gcc/x86_64-pc-linux-gnu/13/include/fmaintrin.h:63:1: error: inlining failed in call to ‘always_inline’ ‘_mm256_fmadd_ps’: target specific option mismatch 63 | _mm256_fmadd_ps (__m256 __A, __m256 __B, __m256 __C) | ^~~~~~~~~~~~~~~ /mnt/storage/tmp/ollama/ollama/llm/llama.cpp/ggml-quants.c:3835:15: note: called from here 3835 | acc = _mm256_fmadd_ps( d, q, acc ); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~ /usr/lib/gcc/x86_64-pc-linux-gnu/13/include/fmaintrin.h:63:1: error: inlining failed in call to ‘always_inline’ ‘_mm256_fmadd_ps’: target specific option mismatch 63 | _mm256_fmadd_ps (__m256 __A, __m256 __B, __m256 __C) | ^~~~~~~~~~~~~~~ /mnt/storage/tmp/ollama/ollama/llm/llama.cpp/ggml-quants.c:3835:15: note: called from here 3835 | acc = _mm256_fmadd_ps( d, q, acc ); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~ /usr/lib/gcc/x86_64-pc-linux-gnu/13/include/fmaintrin.h:63:1: error: inlining failed in call to ‘always_inline’ ‘_mm256_fmadd_ps’: target specific option mismatch 63 | _mm256_fmadd_ps (__m256 __A, __m256 __B, __m256 __C) | ^~~~~~~~~~~~~~~ /mnt/storage/tmp/ollama/ollama/llm/llama.cpp/ggml-quants.c:3835:15: note: called from here 3835 | acc = _mm256_fmadd_ps( d, q, acc ); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~ /usr/lib/gcc/x86_64-pc-linux-gnu/13/include/fmaintrin.h:63:1: error: inlining failed in call to ‘always_inline’ ‘_mm256_fmadd_ps’: target specific option mismatch 63 | _mm256_fmadd_ps (__m256 __A, __m256 __B, __m256 __C) | ^~~~~~~~~~~~~~~ /mnt/storage/tmp/ollama/ollama/llm/llama.cpp/ggml-quants.c:3835:15: note: called from here 3835 | acc = _mm256_fmadd_ps( d, q, acc ); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~ gmake[3]: *** [CMakeFiles/ggml.dir/build.make:121: CMakeFiles/ggml.dir/ggml-quants.c.o] Error 1 ``` ### What did you expect to see? build ### Steps to reproduce go clean git pull export LAMA_NATIVE=on export LLAMA_AVX=on export LLAMA_AVX2=on export LLAMA_F16C=on export LLAMA_FMA=on export LLAMA_VULKAN=1 export CMAKE_C_COMPILER=/usr/lib/llvm/17/bin/clang export CMAKE_CXX_COMPILER=/usr/lib/llvm/17/bin/clang++ export CC=clang export CXX=clang++ export CGO_CFLAGS="-g" export CMAKE_C_COMPILER=clang export CMAKE_CXX_COMPILER=clang++ go clean ./... go generate ./... go build . ### Are there any recent changes that introduced the issue? _No response_ ### OS Linux ### Architecture _No response_ ### Platform _No response_ ### Ollama version a0b8a32eb4a933f7ad3bf687892f05f85dd75fee ### GPU AMD ### GPU info _No response_ ### CPU AMD ### Other software _No response_

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PR_kwDOJ0Z1Ps504gzi

Remove nested runner payloads from linux

closed

COLLABORATOR

This adjusts linux to follow the same model we use for windows with a discrete archive (zip/tgz) to cary the primary executable, subprocess runners, and dependent libraries. Darwin retain the payload model where the go binary is fully self contained. Marking draft as it still needs some more testing and CI will need adjusting, but initial happy path looks good. For comparison, the current (v0.2.1) artifacts are: - `ollama-linux-amd64` - 467 MB - `ollama-linux-amd64-rocm.tgz` - 1.14 GB With this PR: ``` % ls -lh ./dist/ollama-linux-amd64.tgz -rw-r--r-- 1 daniel staff 1.1G Jul 9 13:19 ./dist/ollama-linux-amd64.tgz ``` After extracting on a system: ``` % ls -F cuda/ ollama* ollama_runners/ rocm/ % du -sh . 4.6G . ``` Note: I opted to include rocm into the single artifact, since we do the same on Windows, and that simplifies the overall logic. That said, it's the brunt of the extracted size so this may be an area worth optimizing. One option might be to simply exclude the rocm directory during the extract in the install script if no AMD GPUs are detected. ``` % du -sh rocm 3.6G rocm ```

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I_kwDOJ0Z1Ps6Xx8Qa

Ollama bricks chromium based apps on mac

closed

NONE

### What is the issue? I got a weird issue where chromium based apps stop working due to an ERR_ADDRESS_INVALID error. At first, I thought it would be some kind of problem with chromium and tried out a few different things, however nothing worked till I stopped ollama. Since I got ollama that weird error started to popup randomly. Without ollama running in the background, I wasn't able to reproduce the error. If I try to use ollama I either get the error: `Error: pull model manifest: file does not exist` or `Error: Head "http://127.0.0.1:11434/": dial tcp 127.0.0.1:11434: connect: can't assign requested address` tho I usually get the first one before the second one. Honestly I have no idea what could cause this issue and reinstalling might fix the error but since I seem to be the only one to have this problem, it might be good to look further into it. I attached the log file, feel free to ask if I have to provide anything else. What I tried to fix this issue (before knowing its probably ollama fault): disabled proxys, disabled ipv6, flushed dns records, turned off any apple related safety features for network. Tried different mac os versions (now on 15.0), different networks. [server.log](https://github.com/user-attachments/files/17121474/server.log) Edit: It might be because of the ports that ollama uses, but I don't really think that 11434 is used by chromium for it to not work or does ollama use other ports as well ? ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.3.11

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I_kwDOJ0Z1Ps6HKFJV

Ollama.com - Pull Statistics can be easily fooled

closed

NONE

### What is the issue? Ollama.com model statistics include a pull count. The pull count statistic provides users with the "popularity" of models, where it seems that it is easy to increase the pull count. **Code Reapply** By using the following Python code snippet, you can increase the amount of pulls a hundred times. ```python import subprocess import time import signal import os for i in range(1000): process = subprocess.Popen(["ollama", "pull", "USERNAME/MODELNAME"]) time.sleep(1) try: os.kill(process.pid, signal.SIGTERM) except ProcessLookupError: pass ``` Note that 2-3 minutes is needed to see the changes on the UI. **Suggestion** Pull statistics must be increased after the whole model is pulled into a machine, not after starting a pull request. I'm not a Go dev, don't think that I can provide a fix in a short time. ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.1.32

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PR_kwDOJ0Z1Ps5jQyk1

update Dockerfile.build

closed

CONTRIBUTOR

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I_kwDOJ0Z1Ps6IeJKP

Monetary Support / Donations

closed

NONE

How do I buy you a cup of coffee ?

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I_kwDOJ0Z1Ps6NRSKS

Cannot run model imported from safetensors: byte not found in vocab

closed

NONE

### What is the issue? I have been trying to import HugginFace safetensors models but getting the following error when trying to use the model with `run`. This happens both with and without quantization. For example, trying to reproduce the`tinyllama` model from the library: ```sh git clone https://huggingface.co./TinyLlama/TinyLlama-1.1B-Chat-v1.0 cd TinyLlama-1.1B-Chat-v1.0 ollama show tinyllama --modelfile > Modelfile # Copy reference model file sed -ibak "s@FROM .*@FROM ${PWD}@" Modelfile # Update FROM ollama create tinyllama-hf # OK ollama run tinyllama-hf # Error: llama runner process has terminated: signal: abort trap error: error loading model vocabulary: ERROR: byte not found in vocab ``` Directly importing a similar GGUF seems to work fine: ```sh git clone https://huggingface.co./TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF cd TinyLlama-1.1B-Chat-v1.0-GGUF ollama show tinyllama --modelfile > Modelfile sed -ibak "s@FROM .*@FROM ${PWD}/tinyllama-1.1b-chat-v1.0.Q4_0.gguf@" Modelfile ollama create tinyllama-hf-gguf # OK ollama run tinyllama-hf-gguf # OK ``` Manually converting the model to GGUF with `convert-hf-to-gguf.py` also works. ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.1.45

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I_kwDOJ0Z1Ps6SRIj-

Ollama server running out of memory when it didn't in previous version

closed

NONE

### What is the issue? Hello, I'm trying to run Llama 3.1 8b in Ollama 0.3.4 on a laptop (it has 8gb ram and 4 cpu cores, running it on CPU as my GPU is integrated and not compatible). Maybe this sounds crazy, but it worked fine on Ollama 0.3.3, and just after updating to 0.3.4 and running it (I also tried updating the model but the same) I get: Error: Post "http://127.0.0.1:11434/api/chat": EOF and when I try again: Error: llama runner process has terminated: signal: killed So I think it's running out of memory. I like to try things on my laptop before deploying them to my server, so I would like if you could "fix" that back, or show me a workaround for this. (Mistral 7b works fine with 0.3.4). Thanks in advanced. ### OS Linux ### GPU Other ### CPU Intel ### Ollama version 0.3.4

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I_kwDOJ0Z1Ps6dEi7q

Manual ollama update doesn't work

closed

NONE

### What is the issue? According to docs, after downloading release, this is a last step to update ollama manually: ```bash sudo tar -C /usr -xzf ollama-linux-amd64-rocm.tgz ``` However, ollama version remains unchanged even after `ollama` service restart ### OS Linux ### GPU AMD ### CPU AMD ### Ollama version 0.3.14

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I_kwDOJ0Z1Ps50sxvs

Add Parameter Environment="OLLAMA_HOST=127.0.0.1:11434" to the ollama.service file

closed

NONE

For those who prefer to use ollama primarily via it's API it would be nice if the ollama.service file would already contain the line... `Environment="OLLAMA_HOST=127.0.0.1:11434"` Additionally it would be nice if the info that for the service the interface IP and Port may be changed in this file, would be added to the Readme. For access over all interfaces on port 4711 change to: `Environment="OLLAMA_HOST=0.0.0.0:4711"` But if you like to use the console command then, you then need to use it like this: `OLLAMA_HOST="[REAL-INTERFACE-IP-ADDRESS]:4711" ollama show llama2-uncensored --modelfile` As shortcuts, you then may add for example two aliases to your ~/.bash_aliases: ``` alias ollama-run='OLLAMA_HOST="[REAL-INTERFACE-IP-ADDRESS or localhost]:4711" ollama run' alias ollama-list='OLLAMA_HOST="[REAL-INTERFACE-IP-ADDRESS or localhost]:4711" ollama list' alias ollama-show='OLLAMA_HOST="[REAL-INTERFACE-IP-ADDRESS or localhost]:4711" ollama show' ``` After you have added the aliases, you need to logoff and logon again or do... `source ~/.bash_aliases` Additionally, if you like to run for example chatbot-ollama on the same machine like ollama, but like to make chatbot-ollama available for the whole network, you may then start that one with this command: `OLLAMA_HOST="http://127.0.0.1:4711" npm run dev -- -H [REAL-INTERFACE-IP-ADDRESS-OF-CHATBOT-OLLAMA]` If you do that on a machine accessible by the public, be sure to block the chatbot-ollama service/website for the public or use eg. apache or nginx as a proxy to protect it with username/password protection.

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PR_kwDOJ0Z1Ps59c822

doc: Adding docs on how to compile ollama to run on Intel discrete GPU platform

open

NONE

- Adding the steps to compile Ollama to run on Intel(R) discrete GPU platform - Adding the discrete GPU that have been verified in the GPU docs

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I_kwDOJ0Z1Ps6n6phB

olama installer should ask in drive user wants to install it

open

NONE

Currently, Ollama Desktop is being installed on the C drive. However, if users prefer not to have it stored there, the system should offer an option to choose a different installation location during the setup process. **Feature Request:** The Ollama Desktop installation wizard should prompt users to choose their preferred installation directory. **Problem:** The Ollama Desktop installation wizard is installed on the root drive (C drive) without allowing users to control where it is stored.

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I_kwDOJ0Z1Ps6kRcgV

I built an ollama app for Android

closed

NONE

The source code of the app is here https://github.com/echoo-app/echoo <img width="472" alt="settings" src="https://github.com/user-attachments/assets/2d420674-baa7-4644-b420-a5d44b3c3019" /> <img width="472" alt="home" src="https://github.com/user-attachments/assets/1e26dacf-6baa-4089-af82-c2cb86d4a8be" /> How to add to this list <img width="215" alt="Screenshot 2024-12-23 at 8 59 03 PM" src="https://github.com/user-attachments/assets/9f416dd0-fde2-41d6-989a-d2f4bd4e6fbe" />

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I_kwDOJ0Z1Ps53lxKi

Multi-line prompting from CLI issue - not waiting for closing """

closed

NONE

THe engine started to spew out code before ending the multi-line with closing """. as an example - >>> """ ... can you give python code to use this API using the python requests package/library ... curl -X POST \ ... --url "localhost:5000/v1/..." \ ... --header "Content-Type: application/json" \ ... --data ' ... { ... "chat": "string" ... } ... ' Here is an example of how you can use the `requests` package in Python to make a POST request to the `/v1/completions` endpoint ... It started response right after this curl command before giving closing """

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PR_kwDOJ0Z1Ps5jKNmU

Update README.md - Terminal Integration - ShellOracle

closed

CONTRIBUTOR

ShellOracle is a new ZSH Line Editor widget that uses Ollama for intelligent shell command generation! Ollama rocks! 

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I_kwDOJ0Z1Ps578uEQ

Feature Request : new flag of --benchmark

open

NONE

We are making a website for performance results using different hardware running ollama models at http://LLMPerformance.ai It would be really helpful if ollama had a flag "--benchmark" which made it output everything that "--verbose" does but also added the following info: CPU: Memory: GPU: VRAM: LLM Model Name: LLM Model Layers on GPU: Total Model Layers: Ollama Version: Operating System: Date: The names etc don't matter as long as it is clear. This will help make sure that reported results have enough info to be reproducible.

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I_kwDOJ0Z1Ps6HNvqu

cannot run moondream in Ollama (ERROR source=routes.go:120 msg="error loading llama server" error="llama runner process no longer running: 3221225477 ")

closed

NONE

### What is the issue? I have downloaded the moondream model from official ollama site (https://ollama.com/library/moondream) but while running the model in ollama i get this error ERROR source=routes.go:120 msg="error loading llama server" error="llama runner process no longer running: 3221225477 "  Below is the entire ollama server log file: time=2024-04-29T11:07:39.775+05:30 level=INFO source=images.go:817 msg="total blobs: 6" time=2024-04-29T11:07:39.778+05:30 level=INFO source=images.go:824 msg="total unused blobs removed: 0" time=2024-04-29T11:07:39.779+05:30 level=INFO source=routes.go:1143 msg="Listening on 127.0.0.1:11434 (version 0.1.32)" time=2024-04-29T11:07:39.791+05:30 level=INFO source=payload.go:28 msg="extracting embedded files" dir=C:\Users\LENOVO\AppData\Local\Temp\ollama3526940464\runners time=2024-04-29T11:07:40.021+05:30 level=INFO source=payload.go:41 msg="Dynamic LLM libraries [cpu_avx2 cuda_v11.3 rocm_v5.7 cpu cpu_avx]" [GIN] 2024/04/29 - 11:07:40 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2024/04/29 - 11:07:40 | 200 | 2.1732ms | 127.0.0.1 | POST "/api/show" [GIN] 2024/04/29 - 11:07:40 | 200 | 1.6291ms | 127.0.0.1 | POST "/api/show" time=2024-04-29T11:07:40.857+05:30 level=INFO source=gpu.go:121 msg="Detecting GPU type" time=2024-04-29T11:07:40.857+05:30 level=INFO source=gpu.go:268 msg="Searching for GPU management library cudart64_.dll" time=2024-04-29T11:07:40.869+05:30 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [C:\Users\LENOVO\AppData\Local\Programs\Ollama\cudart64_110.dll]" time=2024-04-29T11:07:41.992+05:30 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" time=2024-04-29T11:07:41.993+05:30 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-29T11:07:42.065+05:30 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 8.6" time=2024-04-29T11:07:42.082+05:30 level=INFO source=gpu.go:121 msg="Detecting GPU type" time=2024-04-29T11:07:42.082+05:30 level=INFO source=gpu.go:268 msg="Searching for GPU management library cudart64_.dll" time=2024-04-29T11:07:42.092+05:30 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [C:\Users\LENOVO\AppData\Local\Programs\Ollama\cudart64_110.dll]" time=2024-04-29T11:07:42.093+05:30 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" time=2024-04-29T11:07:42.093+05:30 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-29T11:07:42.142+05:30 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 8.6" time=2024-04-29T11:07:42.168+05:30 level=INFO source=server.go:127 msg="offload to gpu" reallayers=25 layers=25 required="2588.9 MiB" used="2588.9 MiB" available="3304.2 MiB" kv="384.0 MiB" fulloffload="148.0 MiB" partialoffload="190.0 MiB" time=2024-04-29T11:07:42.168+05:30 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-29T11:07:42.177+05:30 level=INFO source=server.go:264 msg="starting llama server" cmd="C:\Users\LENOVO\AppData\Local\Temp\ollama3526940464\runners\cuda_v11.3\ollama_llama_server.exe --model C:\Users\LENOVO\.ollama\models\blobs\sha256-e554c6b9de016673fd2c732e0342967727e9659ca5f853a4947cc96263fa602b --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 25 --mmproj C:\Users\LENOVO\.ollama\models\blobs\sha256-4cc1cb3660d87ff56432ebeb7884ad35d67c48c7b9f6b2856f305e39c38eed8f --port 53596" time=2024-04-29T11:07:42.229+05:30 level=INFO source=server.go:389 msg="waiting for llama runner to start responding" {"function":"server_params_parse","level":"INFO","line":2603,"msg":"logging to file is disabled.","tid":"21912","timestamp":1714369062} {"build":2679,"commit":"7593639","function":"wmain","level":"INFO","line":2820,"msg":"build info","tid":"21912","timestamp":1714369062} {"function":"wmain","level":"INFO","line":2827,"msg":"system info","n_threads":8,"n_threads_batch":-1,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | ","tid":"21912","timestamp":1714369062,"total_threads":16} {"function":"load_model","level":"INFO","line":395,"msg":"Multi Modal Mode Enabled","tid":"21912","timestamp":1714369062} ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 3050 Laptop GPU, compute capability 8.6, VMM: yes key clip.vision.image_grid_pinpoints not found in file key clip.vision.mm_patch_merge_type not found in file key clip.vision.image_crop_resolution not found in file clip_model_load: failed to load vision model tensors time=2024-04-29T11:07:43.382+05:30 level=ERROR source=routes.go:120 msg="error loading llama server" error="llama runner process no longer running: 3221225477 " I am able to run other models in ollama like Mixtral, mxbai, tinyllama etc without any issue but Moondream is failing to start. OS details: Windows GPU: Nvidia RTX 3050 Any help would be highly appreciated. ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.1.32

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PR_kwDOJ0Z1Ps57rPMs

llama: doc: explain golang objc linker warning

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I_kwDOJ0Z1Ps6K6qLS

Can't answer chinese Starting from 0.1.39

closed

NONE

### What is the issue? Starting from 0.1.39, if you input a Chinese question, ollama will not be able to output a Chinese answer ### OS _No response_ ### GPU _No response_ ### CPU _No response_ ### Ollama version 0.1.39+

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I_kwDOJ0Z1Ps6GF8Vt

Use NVIDIA + AMD GPUs simultaneously (CUDA OOM?)

closed

NONE

### What is the issue? I'm trying to run my ollama:rocm docker image (pulled 4/16/24) and it does the Nvidia M40 and Ryzen 7900x CPU offloads. I see there is full nvidia VRAM usage and the remaining layers offload to my CPU RAM. However I also have my 7900xtx AMD card in there, and when I'm not passing the "--gpus all" in the docker CLI for the run, I can use exclusively the AMD GPU. When I add that parameter, the NV GPU works, but the AMD GPU sits idle. I'd like to use models that utilize upto 48gb of VRAM by splitting the usage across the two cards though they are not of the same vendor ( I know the challenges, trust me, took forever to get this far). So my setup of course; i've got the rocm stuff working, tested with just ollama:rocm and passing --device /dev/dri/renderD129 (my 7900xtx) i've got the cuda stuff working, tested with just ollama:rocm and passing --device /dev/dri/renderD129 (my 7900xtx) --gpus all Total I have 48GB VRAM and 64GB System RAM The steps to reproduce; `docker run -d --gpus all --device /dev/kfd --device /dev/dri/renderD129 --device /dev/dri/renderD128 -v ollama:/root/.ollama -e OLLAMA_ORIGINS='*.github.io' -p 11434:11434 --name ollama ollama/ollama:rocm` `docker exec -it /bin/bash` `ollama run --verbose llama2-uncensored:70b` (this is 38G) and the output I get is; ``` [root@d8466466df3c /]# ollama run --verbose llama2-uncensored:70b Error: llama runner process no longer running: -1 CUDA error: out of memory current device: 0, in function alloc at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:302 cuMemCreate(&handle, reserve_size, &prop, 0) GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:60: !"CUDA error" [root@d8466466df3c /]# ``` And screenshot for reference (mid-run of final command)  happy to follow up and test. ### OS Linux, Docker ### GPU Nvidia, AMD ### CPU AMD ### Ollama version ollama version is 0.1.32:rocm

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I_kwDOJ0Z1Ps6PYH5w

2nd prompt never completes

closed

NONE

### What is the issue? Whenever i try to give the second prompt on any GGUF models ollama fails here is the logs time=2024-07-12T15:47:23.505Z level=INFO source=sched.go:738 msg="new model will fit in available VRAM in single GPU, loading" model=/home/udemirezen/.ollama/models/blobs/sha256-c0dd304d761e8e05d082cc2902d7624a7f87858fdfaa4ef098330ffe767ff0d3 gpu=GPU-eb3a6957-5b68-4958-c313-3b20f0815e4d parallel=4 available=33768079360 required="12.4 GiB" time=2024-07-12T15:47:23.506Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=33 layers.offload=33 layers.split="" memory.available="[31.4 GiB]" memory.required.full="12.4 GiB" memory.required.partial="12.4 GiB" memory.required.kv="8.0 GiB" memory.required.allocations="[12.4 GiB]" memory.weights.total="10.5 GiB" memory.weights.repeating="10.4 GiB" memory.weights.nonrepeating="102.6 MiB" memory.graph.full="1.1 GiB" memory.graph.partial="1.3 GiB" time=2024-07-12T15:47:23.507Z level=INFO source=server.go:375 msg="starting llama server" cmd="/tmp/ollama3602127725/runners/cuda_v11/ollama_llama_server --model /home/udemirezen/.ollama/models/blobs/sha256-c0dd304d761e8e05d082cc2902d7624a7f87858fdfaa4ef098330ffe767ff0d3 --ctx-size 16384 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 4 --port 40663" time=2024-07-12T15:47:23.508Z level=INFO source=sched.go:474 msg="loaded runners" count=1 time=2024-07-12T15:47:23.508Z level=INFO source=server.go:563 msg="waiting for llama runner to start responding" time=2024-07-12T15:47:23.513Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server error" INFO [main] build info | build=1 commit="a8db2a9" tid="139871727800320" timestamp=1720799243 INFO [main] system info | n_threads=24 n_threads_batch=-1 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 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | " tid="139871727800320" timestamp=1720799243 total_threads=48 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="47" port="40663" tid="139871727800320" timestamp=1720799243 llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from /home/udemirezen/.ollama/models/blobs/sha256-c0dd304d761e8e05d082cc2902d7624a7f87858fdfaa4ef098330ffe767ff0d3 (version GGUF V2) 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.name str = LLaMA v2 llama_model_loader: - kv 2: llama.context_length u32 = 4096 llama_model_loader: - kv 3: llama.embedding_length u32 = 4096 llama_model_loader: - kv 4: llama.block_count u32 = 32 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 10: general.file_type u32 = 10 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<... llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 18: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q2_K: 65 tensors llama_model_loader: - type q3_K: 160 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: special tokens cache size = 259 llm_load_vocab: token to piece cache size = 0.1684 MB llm_load_print_meta: format = GGUF V2 llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 4096 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_layer = 32 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 32 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 = 1 llm_load_print_meta: n_embd_k_gqa = 4096 llm_load_print_meta: n_embd_v_gqa = 4096 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 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 = 11008 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 = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 4096 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: model type = 7B llm_load_print_meta: model ftype = Q2_K - Medium llm_load_print_meta: model params = 6.74 B llm_load_print_meta: model size = 2.63 GiB (3.35 BPW) llm_load_print_meta: general.name = LLaMA v2 llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_print_meta: max token length = 48 time=2024-07-12T15:47:23.765Z level=INFO source=server.go:604 msg="waiting for server to become available" status="llm server loading model" ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: Tesla V100-SXM2-32GB, compute capability 7.0, VMM: yes llm_load_tensors: ggml ctx size = 0.27 MiB llm_load_tensors: offloading 32 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 33/33 layers to GPU llm_load_tensors: CPU buffer size = 41.02 MiB llm_load_tensors: CUDA0 buffer size = 2653.31 MiB llama_new_context_with_model: n_ctx = 16384 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: flash_attn = 0 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 8192.00 MiB llama_new_context_with_model: KV self size = 8192.00 MiB, K (f16): 4096.00 MiB, V (f16): 4096.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 0.55 MiB llama_new_context_with_model: CUDA0 compute buffer size = 1088.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 40.01 MiB llama_new_context_with_model: graph nodes = 1030 llama_new_context_with_model: graph splits = 2 INFO [main] model loaded | tid="139871727800320" timestamp=1720799246 time=2024-07-12T15:47:26.280Z level=INFO source=server.go:609 msg="llama runner started in 2.77 seconds" ggml_cuda_compute_forward: MUL failed CUDA error: unspecified launch failure current device: 0, in function ggml_cuda_compute_forward at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:2283 err GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:100: !"CUDA error" to visualize things i use the Open WebUI here are some screenshots   ### OS Linux ### GPU Nvidia ### CPU Other ### Ollama version 0.2.1

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I_kwDOJ0Z1Ps6GXkDx

An existing connection was forcibly closed by the remote host.Could you help me?

closed

NONE

### What is the issue? PS C:\Users\Administrator\AppData\Local\Ollama> ollama run llama3 pulling manifest Error: pull model manifest: Get "https://ollama.com/token?nonce=1AKxIvoajv-NPGYukzWJcA&scope=repository%!A(MISSING)library%!F(MISSING)llama3%!A(MISSING)pull&service=ollama.com&ts=1713578711": read tcp 192.168.124.11:53463->34.120.132.20:443: wsarecv: An existing connection was forcibly closed by the remote host.  (https://github.com/ollama/ollama/files/15046570/server.log) ### OS Windows ### GPU Nvidia ### CPU Intel ### Ollama version _No response_

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I_kwDOJ0Z1Ps57QQcz

How to run Ollama only on a dedicated GPU? (Instead of all GPUs)

closed

NONE

Hi, I have 3x3090 and I want to run Ollama Instance only on a dedicated GPU. The reason for this: To have 3xOllama Instances (with different ports) for using with Autogen. I also tried the "Docker Ollama" without luck. Or is there an other solution? Let me know... Thanks in advance Steve

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PR_kwDOJ0Z1Ps5r7F9r

fix: close files in the CreateHandler func

closed

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I_kwDOJ0Z1Ps6a5VhH

Windows ARM64 fails when loading model, error code 0xc000001d

open

NONE

### What is the issue? I installed the latest Ollama for Windows (ARM64 build) on my 2023 Windows Dev Kit, which has an 8-core ARM processor, a Snapdragon 8cx Gen 3. It's running Windows 11 Pro. I can pull models, but when I go to run them, I get an error. It doesn't matter what model I run, I've tried several. Here's an example. ``` C:\Users\Mike Chambers>ollama pull gemma2:2b pulling manifest pulling 7462734796d6... 100% ▕████████████████████████████████████████████████████████▏ 1.6 GB pulling e0a42594d802... 100% ▕████████████████████████████████████████████████████████▏ 358 B pulling 097a36493f71... 100% ▕████████████████████████████████████████████████████████▏ 8.4 KB pulling 2490e7468436... 100% ▕████████████████████████████████████████████████████████▏ 65 B pulling e18ad7af7efb... 100% ▕████████████████████████████████████████████████████████▏ 487 B verifying sha256 digest writing manifest success C:\Users\Mike Chambers>ollama run gemma2:2b Error: llama runner process has terminated: exit status 0xc000001d C:\Users\Mike Chambers> ``` ### OS Windows ### GPU _No response_ ### CPU Other ### Ollama version 0.3.13 [server.log](https://github.com/user-attachments/files/17448637/server.log)

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PR_kwDOJ0Z1Ps6FGxNy

Docs: Add /api/version endpoint to API documentation

closed

CONTRIBUTOR

This PR adds documentation for the `/api/version` endpoint to the Ollama API documentation. This endpoint allows clients to retrieve the Ollama server version, which is useful for ensuring compatibility with features that are dependent on specific server versions. The `/api/version` endpoint was added in a previous commit but was not documented. This PR addresses that omission. Resolves #8040

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PR_kwDOJ0Z1Ps5elQcL

update langchainjs doc

closed

CONTRIBUTOR

Updates docs/tutorials/langchainjs.md from issue #539 adds missing await in line 36 and adds instructions to install cheerio

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PR_kwDOJ0Z1Ps5e0dAn

Update configuration instructions in README

closed

NONE

Added in instructions on how to specify model file location, as it isn't mentioned in the docs. This should probably be handled by a `ollama config modelpath` function instead.

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I_kwDOJ0Z1Ps6ASZYn

Langchain + Chainlit integration issue

closed

NONE

Hi! I'm using Ollama on a local server RTX A6000 ADA running Mixtral 8x7B. I run Ollama locally and expose an API endpoint for multiple user to connect and use the LLM in a chat powered by Chainlit + Langchain. There is 2 issue I want to solve: 1 - Ollama is serving, but the model seems to deload from the GPU after a period of inactivity. This makes the first requet to be a little longer than usual. Is it possible to make sure the model is always running and loaded in the GPU? 2 - When I do 2 request at the same time the output streaming for both user freeze for a few second before continuing slower. It makes sens it goes slower as Ollama is serving 2 user at the same time, but I wonder why there is this constant freeze, when I do 10 requests at the same time some of them don't even go through. Could anyone help me on this? Thanks! Michel

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I_kwDOJ0Z1Ps5-kbtg

Ollama outputs endless stream of random words

closed

NONE

When running a model with any prompt, the output is a constant stream of random characters and words from various languages. The nonsensical output will continue until ollama is terminated. An example prompt and output is included below: ``` $ ollama run llama2 >>> hi alseularoured阳negneg вер VALUESalling阳 statementençneg LageTX subsequent VALUES門 посownerençneg African calculate amerik calculate VALUES interrupted competed succeed subsequentcdot Lage VALUES VALUES segmentsetra Архиownerးularдні right Pubenç пос門 subsequent АрхиWR African calculate ante Storm ante calculateenç阳 Архи Mortремен concentrationularottedowneretraship succeed subsequent Архиeffect seis VALUE верalse Lage stre VALUESular Lage calculateenç пос riv VALUES calculate nad Hannover облаouredoured VALUES出 ante statement вер Betrieb calculatecdot VALUES阳TX Lage Lage subsequentishingcalled Stormalling österreich阳 segments nad阳ovooured amerik ante верး succeed Pub Pub Архиownerishing calculate VALUES competed interruptedishing Stormular門shiputer nad concentration seis門 Mort Pubishing rightремен African MortInterceptor subsequent statement succeed Lage statementWRularençдні Lageenç African阳 Mortotted VALUESeffect ante門 succeedTX stre Australneg阳enç обла nad ante Hannoverbo antecdot посcalledenç посalse amerikowner segments阳 Lage Pub Mortularovoneg Storm Lage Архи Mortishing statement concentration門 ante Storm Mort Betrieb riv вер Pub Africanးneg interrupted calculatenegenç wol阳 ante calculateular nadдні statementallingenç stre ante Архиalseençnegetraownerремен stre VALUES вер African Storm African nad calculate門 Africanownereffectouredneg Storm calculate верTX Africanotted ante VALUES antecdot Hannover Mort seis subsequent amerik subsequentboowner門阳 Mort concentrationenç обла African African Mortownerး пос Mort阳 Stormship ante competed interruptedençetra subsequent Betrieb Lagecalled calculateular succeed anteдні riv阳 пос ante subsequentovo streuter segments succeed ante Pub succeed AustraleffectWR subsequent VALUES ... ``` The full output text is cut off to save space. This occurs with any prompt I tried for both llama2 and mistral. The same phenomenon occurs when using curl rather than the CLI. Activity Monitor shows no GPU usage, so I suspect no model inference is actually occurring. I am using a 16GB M2 Mac.

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PR_kwDOJ0Z1Ps5taLzO

Report errors on server lookup instead of path lookup failure

closed

COLLABORATOR

This should help identify more details on the failure mode for #3738

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I_kwDOJ0Z1Ps526e3B

Error while loading Nous-Capybara-34B

closed

NONE

I tried to run https://huggingface.co./TheBloke/Nous-Capybara-34B-GGUF using this modelfile ``` FROM ./nous-capybara-34b.Q3_K_S.gguf TEMPLATE """USER: {{ .Prompt }} ASSISTANT:""" PARAMETER num_ctx 200000 PARAMETER stop "USER" PARAMETER stop "ASSISTANT" ``` I got this error ``` Error: llama runner process has terminated ``` I have 32GB RAM and 1050 TI GPU with 4GB.

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I_kwDOJ0Z1Ps56Em5S

Is there any option to unload a model from memory?

closed

NONE

As in the title. I want to unload model, is there any option for it?

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I_kwDOJ0Z1Ps6Sa6O4

Offload a model command

closed