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[GH-ISSUE #10956] Garbage output when running llama3.2-vision:11b #53730

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opened 2026-04-29 04:36:21 -05:00 by GiteaMirror · 29 comments
GiteaMirror commented 2026-04-29 04:36:21 -05:00
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Originally created by @TobiasG0 on GitHub (Jun 3, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/10956

What is the issue?

Description

I'm encountering an issue when using the llama3.2-vision:11b model via Ollama Docker to generate image descriptions.

Problem

  • On the first request after starting the Docker container, the model returns a normal, coherent response.
  • On all subsequent requests, the model only produces random tokens until the max number of tokens is reached.
  • This happens even when sending the exact same request twice — the first response is good, but the second one is garbage

Configuration

  • num_ctx: 32768

  • num_predict: 4096

  • The prompt includes:

    • Instructions
    • Contextual information about the image
    • The image itself

Notes

  • This problem only affects the llama3.2-vision:11b model.
  • It started occurring after updating to a newer Ollama Docker version, which required re-downloading the updated model.
  • Re-setting everything from scratch did not resolve the issue.
  • Other models continue to work without issues under the same setup.

Relevant log output

time=2025-06-03T15:13:49.470Z level=INFO source=routes.go:1234 msg="server config" env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11444 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 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:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]"
time=2025-06-03T15:13:49.474Z level=INFO source=images.go:479 msg="total blobs: 28"
time=2025-06-03T15:13:49.474Z level=INFO source=images.go:486 msg="total unused blobs removed: 0"
time=2025-06-03T15:13:49.475Z level=INFO source=routes.go:1287 msg="Listening on [::]:11444 (version 0.9.0)"
time=2025-06-03T15:13:49.475Z level=INFO source=gpu.go:217 msg="looking for compatible GPUs"
time=2025-06-03T15:13:53.018Z level=INFO source=types.go:130 msg="inference compute" id=GPU-dc2dc9cc-4228-3604-544b-80236126c320 library=cuda variant=v12 compute=9.0 driver=12.4 name="NVIDIA H100 NVL" total="93.0 GiB" available="92.5 GiB"
time=2025-06-03T15:13:53.018Z level=INFO source=types.go:130 msg="inference compute" id=GPU-e96fffea-8728-7825-3280-61f981e52209 library=cuda variant=v12 compute=9.0 driver=12.4 name="NVIDIA H100 NVL" total="93.0 GiB" available="92.5 GiB"
time=2025-06-03T15:14:58.686Z level=WARN source=sched.go:140 msg="mllama does not currently support parallel requests"
time=2025-06-03T15:14:59.753Z level=INFO source=sched.go:788 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-9999d473417a8e179d993498195be5f42cab963acc75f4a6b15d981e8b68abed gpu=GPU-dc2dc9cc-4228-3604-544b-80236126c320 parallel=1 available=99310764032 required="17.5 GiB"
time=2025-06-03T15:15:00.405Z level=INFO source=server.go:135 msg="system memory" total="503.5 GiB" free="492.3 GiB" free_swap="8.0 GiB"
time=2025-06-03T15:15:00.408Z level=INFO source=server.go:168 msg=offload library=cuda layers.requested=-1 layers.model=41 layers.offload=41 layers.split="" memory.available="[92.5 GiB]" memory.gpu_overhead="0 B" memory.required.full="17.5 GiB" memory.required.partial="17.5 GiB" memory.required.kv="5.0 GiB" memory.required.allocations="[17.5 GiB]" memory.weights.total="5.3 GiB" memory.weights.repeating="4.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="2.1 GiB" memory.graph.partial="2.2 GiB" projector.weights="1.7 GiB" projector.graph="2.8 GiB"
time=2025-06-03T15:15:00.496Z level=INFO source=server.go:431 msg="starting llama server" cmd="/usr/bin/ollama runner --ollama-engine --model /root/.ollama/models/blobs/sha256-9999d473417a8e179d993498195be5f42cab963acc75f4a6b15d981e8b68abed --ctx-size 32768 --batch-size 512 --n-gpu-layers 41 --threads 112 --parallel 1 --port 36965"
time=2025-06-03T15:15:00.499Z level=INFO source=sched.go:483 msg="loaded runners" count=1
time=2025-06-03T15:15:00.499Z level=INFO source=server.go:591 msg="waiting for llama runner to start responding"
time=2025-06-03T15:15:00.500Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server not responding"
time=2025-06-03T15:15:00.531Z level=INFO source=runner.go:925 msg="starting ollama engine"
time=2025-06-03T15:15:00.532Z level=INFO source=runner.go:983 msg="Server listening on 127.0.0.1:36965"
time=2025-06-03T15:15:00.591Z level=INFO source=ggml.go:92 msg="" architecture=mllama file_type=Q4_K_M name="" description="" num_tensors=908 num_key_values=40
load_backend: loaded CPU backend from /usr/lib/ollama/libggml-cpu-icelake.so
time=2025-06-03T15:15:00.753Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model"
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA H100 NVL, compute capability 9.0, VMM: yes
load_backend: loaded CUDA backend from /usr/lib/ollama/cuda_v12/libggml-cuda.so
time=2025-06-03T15:15:00.765Z level=INFO source=ggml.go:104 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.AVX512=1 CPU.0.AVX512_VBMI=1 CPU.0.AVX512_VNNI=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=500,600,610,700,750,800,860,870,890,900,1200 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(gcc)
time=2025-06-03T15:15:00.987Z level=INFO source=ggml.go:351 msg="model weights" buffer=CUDA0 size="7.0 GiB"
time=2025-06-03T15:15:00.987Z level=INFO source=ggml.go:351 msg="model weights" buffer=CPU size="285.7 MiB"
time=2025-06-03T15:15:08.561Z level=INFO source=ggml.go:638 msg="compute graph" backend=CUDA0 buffer_type=CUDA0 size="2.8 GiB"
time=2025-06-03T15:15:08.561Z level=INFO source=ggml.go:638 msg="compute graph" backend=CPU buffer_type=CPU size="7.8 MiB"
time=2025-06-03T15:15:08.687Z level=INFO source=ggml.go:638 msg="compute graph" backend=CUDA0 buffer_type=CUDA0 size="2.8 GiB"
time=2025-06-03T15:15:08.687Z level=INFO source=ggml.go:638 msg="compute graph" backend=CPU buffer_type=CPU size="8.0 MiB"
time=2025-06-03T15:15:14.340Z level=INFO source=server.go:630 msg="llama runner started in 13.84 seconds"
[GIN] 2025/06/03 - 15:15:41 | 200 | 43.200064598s |   172.30.176.17 | POST     "/api/chat"
time=2025-06-03T15:15:57.655Z level=WARN source=sched.go:140 msg="mllama does not currently support parallel requests"
[GIN] 2025/06/03 - 15:16:51 | 200 | 53.596623511s |   172.30.176.17 | POST     "/api/chat"

OS

Docker

GPU

Nvidia

CPU

Intel

Ollama version

0.9.0

Originally created by @TobiasG0 on GitHub (Jun 3, 2025). Original GitHub issue: https://github.com/ollama/ollama/issues/10956 ### What is the issue? # Description I'm encountering an issue when using the `llama3.2-vision:11b` model via Ollama Docker to generate image descriptions. # Problem * On the **first request** after starting the Docker container, the model returns a **normal, coherent response**. * On **all subsequent requests**, the model only produces random tokens until the max number of tokens is reached. * This happens **even when sending the exact same request twice** — the first response is good, but the second one is garbage # Configuration * `num_ctx`: 32768 * `num_predict`: 4096 * The prompt includes: * Instructions * Contextual information about the image * The image itself # Notes * This problem **only affects** the `llama3.2-vision:11b` model. * It **started occurring after updating** to a newer Ollama Docker version, which required re-downloading the updated model. * Re-setting everything **from scratch did not resolve the issue**. * Other models continue to work without issues under the same setup. ### Relevant log output ```shell time=2025-06-03T15:13:49.470Z level=INFO source=routes.go:1234 msg="server config" env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11444 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 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:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]" time=2025-06-03T15:13:49.474Z level=INFO source=images.go:479 msg="total blobs: 28" time=2025-06-03T15:13:49.474Z level=INFO source=images.go:486 msg="total unused blobs removed: 0" time=2025-06-03T15:13:49.475Z level=INFO source=routes.go:1287 msg="Listening on [::]:11444 (version 0.9.0)" time=2025-06-03T15:13:49.475Z level=INFO source=gpu.go:217 msg="looking for compatible GPUs" time=2025-06-03T15:13:53.018Z level=INFO source=types.go:130 msg="inference compute" id=GPU-dc2dc9cc-4228-3604-544b-80236126c320 library=cuda variant=v12 compute=9.0 driver=12.4 name="NVIDIA H100 NVL" total="93.0 GiB" available="92.5 GiB" time=2025-06-03T15:13:53.018Z level=INFO source=types.go:130 msg="inference compute" id=GPU-e96fffea-8728-7825-3280-61f981e52209 library=cuda variant=v12 compute=9.0 driver=12.4 name="NVIDIA H100 NVL" total="93.0 GiB" available="92.5 GiB" time=2025-06-03T15:14:58.686Z level=WARN source=sched.go:140 msg="mllama does not currently support parallel requests" time=2025-06-03T15:14:59.753Z level=INFO source=sched.go:788 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-9999d473417a8e179d993498195be5f42cab963acc75f4a6b15d981e8b68abed gpu=GPU-dc2dc9cc-4228-3604-544b-80236126c320 parallel=1 available=99310764032 required="17.5 GiB" time=2025-06-03T15:15:00.405Z level=INFO source=server.go:135 msg="system memory" total="503.5 GiB" free="492.3 GiB" free_swap="8.0 GiB" time=2025-06-03T15:15:00.408Z level=INFO source=server.go:168 msg=offload library=cuda layers.requested=-1 layers.model=41 layers.offload=41 layers.split="" memory.available="[92.5 GiB]" memory.gpu_overhead="0 B" memory.required.full="17.5 GiB" memory.required.partial="17.5 GiB" memory.required.kv="5.0 GiB" memory.required.allocations="[17.5 GiB]" memory.weights.total="5.3 GiB" memory.weights.repeating="4.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="2.1 GiB" memory.graph.partial="2.2 GiB" projector.weights="1.7 GiB" projector.graph="2.8 GiB" time=2025-06-03T15:15:00.496Z level=INFO source=server.go:431 msg="starting llama server" cmd="/usr/bin/ollama runner --ollama-engine --model /root/.ollama/models/blobs/sha256-9999d473417a8e179d993498195be5f42cab963acc75f4a6b15d981e8b68abed --ctx-size 32768 --batch-size 512 --n-gpu-layers 41 --threads 112 --parallel 1 --port 36965" time=2025-06-03T15:15:00.499Z level=INFO source=sched.go:483 msg="loaded runners" count=1 time=2025-06-03T15:15:00.499Z level=INFO source=server.go:591 msg="waiting for llama runner to start responding" time=2025-06-03T15:15:00.500Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server not responding" time=2025-06-03T15:15:00.531Z level=INFO source=runner.go:925 msg="starting ollama engine" time=2025-06-03T15:15:00.532Z level=INFO source=runner.go:983 msg="Server listening on 127.0.0.1:36965" time=2025-06-03T15:15:00.591Z level=INFO source=ggml.go:92 msg="" architecture=mllama file_type=Q4_K_M name="" description="" num_tensors=908 num_key_values=40 load_backend: loaded CPU backend from /usr/lib/ollama/libggml-cpu-icelake.so time=2025-06-03T15:15:00.753Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model" ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA H100 NVL, compute capability 9.0, VMM: yes load_backend: loaded CUDA backend from /usr/lib/ollama/cuda_v12/libggml-cuda.so time=2025-06-03T15:15:00.765Z level=INFO source=ggml.go:104 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.AVX512=1 CPU.0.AVX512_VBMI=1 CPU.0.AVX512_VNNI=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=500,600,610,700,750,800,860,870,890,900,1200 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(gcc) time=2025-06-03T15:15:00.987Z level=INFO source=ggml.go:351 msg="model weights" buffer=CUDA0 size="7.0 GiB" time=2025-06-03T15:15:00.987Z level=INFO source=ggml.go:351 msg="model weights" buffer=CPU size="285.7 MiB" time=2025-06-03T15:15:08.561Z level=INFO source=ggml.go:638 msg="compute graph" backend=CUDA0 buffer_type=CUDA0 size="2.8 GiB" time=2025-06-03T15:15:08.561Z level=INFO source=ggml.go:638 msg="compute graph" backend=CPU buffer_type=CPU size="7.8 MiB" time=2025-06-03T15:15:08.687Z level=INFO source=ggml.go:638 msg="compute graph" backend=CUDA0 buffer_type=CUDA0 size="2.8 GiB" time=2025-06-03T15:15:08.687Z level=INFO source=ggml.go:638 msg="compute graph" backend=CPU buffer_type=CPU size="8.0 MiB" time=2025-06-03T15:15:14.340Z level=INFO source=server.go:630 msg="llama runner started in 13.84 seconds" [GIN] 2025/06/03 - 15:15:41 | 200 | 43.200064598s | 172.30.176.17 | POST "/api/chat" time=2025-06-03T15:15:57.655Z level=WARN source=sched.go:140 msg="mllama does not currently support parallel requests" [GIN] 2025/06/03 - 15:16:51 | 200 | 53.596623511s | 172.30.176.17 | POST "/api/chat" ``` ### OS Docker ### GPU Nvidia ### CPU Intel ### Ollama version 0.9.0
GiteaMirror added the bugneeds more info labels 2026-04-29 04:36:21 -05:00
GiteaMirror commented 2026-04-29 04:36:22 -05:00
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@rick-github commented on GitHub (Jun 3, 2025):

If you set OLLAMA_DEBUG=1 there will be some additional information logged that might be useful. How do you get the model back to a properly functioning state?

<!-- gh-comment-id:2936060638 --> @rick-github commented on GitHub (Jun 3, 2025): If you set `OLLAMA_DEBUG=1` there will be some additional information logged that might be useful. How do you get the model back to a properly functioning state?
GiteaMirror commented 2026-04-29 04:36:23 -05:00
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@TobiasG0 commented on GitHub (Jun 3, 2025):

If you set OLLAMA_DEBUG=1 there will be some additional information logged that might be useful. How do you get the model back to a properly functioning state?

I can get the model back into a functioning state by restarting the Ollama Docker container. After that, the first request works, but all subsequent requests produce garbage output until the Docker container is restarted again.

Ollama log using OLLAMA_DEBUG=1:
detailed_ollama_log.txt

<!-- gh-comment-id:2936159187 --> @TobiasG0 commented on GitHub (Jun 3, 2025): > If you set `OLLAMA_DEBUG=1` there will be some additional information logged that might be useful. How do you get the model back to a properly functioning state? I can get the model back into a functioning state by restarting the Ollama Docker container. After that, the first request works, but all subsequent requests produce garbage output until the Docker container is restarted again. **Ollama log using `OLLAMA_DEBUG=1`:** [detailed_ollama_log.txt](https://github.com/user-attachments/files/20577135/detailed_ollama_log.txt)
GiteaMirror commented 2026-04-29 04:36:23 -05:00
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@rick-github commented on GitHub (Jun 3, 2025):

Try stopping the model rather than restarting the container, that may disambiguate between a server problem or a runner problem. docker exec <name-of-container> ollama stop llama3.2-vision:11b or set "keep_alive":0 in the API call. Also try running the model without GPU by setting "options":{"num_gpu":0} in the API call. It will run slower and will probably run multiple inferences without a problem, but worth checking.

<!-- gh-comment-id:2936476691 --> @rick-github commented on GitHub (Jun 3, 2025): Try stopping the model rather than restarting the container, that may disambiguate between a server problem or a runner problem. `docker exec <name-of-container> ollama stop llama3.2-vision:11b` or set `"keep_alive":0` in the API call. Also try running the model without GPU by setting `"options":{"num_gpu":0}` in the API call. It will run slower and will probably run multiple inferences without a problem, but worth checking.
GiteaMirror commented 2026-04-29 04:36:24 -05:00
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@TobiasG0 commented on GitHub (Jun 3, 2025):

Try stopping the model rather than restarting the container, that may disambiguate between a server problem or a runner problem. docker exec <name-of-container> ollama stop llama3.2-vision:11b or set "keep_alive":0 in the API call. Also try running the model without GPU by setting "options":{"num_gpu":0} in the API call. It will run slower and will probably run multiple inferences without a problem, but worth checking.

I'm observing the same behavior when stopping the model between runs. The first request after startup works correctly, but after stopping the model and sending a second request with the same input, the response is garbage.

I've attached the logs for this scenario:
ollama_stop_logs.txt

The same issue occurs when running the model exclusively on the CPU:
ollama_cpu_logs.txt

I've also included two sample responses from the model using the same input:

The first response was generated after restarting the container:
ollama_cpu_response_good.txt

The second response was generated directly after the first one with the same input, without restarting the container:
ollama_cpu_response_bad.txt

<!-- gh-comment-id:2936732207 --> @TobiasG0 commented on GitHub (Jun 3, 2025): > Try stopping the model rather than restarting the container, that may disambiguate between a server problem or a runner problem. `docker exec <name-of-container> ollama stop llama3.2-vision:11b` or set `"keep_alive":0` in the API call. Also try running the model without GPU by setting `"options":{"num_gpu":0}` in the API call. It will run slower and will probably run multiple inferences without a problem, but worth checking. I'm observing the same behavior when stopping the model between runs. The first request after startup works correctly, but after stopping the model and sending a second request with the same input, the response is garbage. I've attached the logs for this scenario: [ollama_stop_logs.txt](https://github.com/user-attachments/files/20579214/ollama_stop_logs.txt) The same issue occurs when running the model exclusively on the CPU: [ollama_cpu_logs.txt](https://github.com/user-attachments/files/20579230/ollama_cpu_logs.txt) I've also included two sample responses from the model using the same input: The first response was generated after restarting the container: [ollama_cpu_response_good.txt](https://github.com/user-attachments/files/20579242/ollama_cpu_response_good.txt) The second response was generated directly after the first one with the same input, without restarting the container: [ollama_cpu_response_bad.txt](https://github.com/user-attachments/files/20579244/ollama_cpu_response_bad.txt)
GiteaMirror commented 2026-04-29 04:36:24 -05:00
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@rick-github commented on GitHub (Jun 3, 2025):

I'm observing the same behavior when stopping the model between runs
The same issue occurs when running the model exclusively on the CPU:

That's interesting - it's independent of the runner and GPU implying a server fault. I raised it as a possible issue, but in my experience it's never the server. How did you install ollama?

<!-- gh-comment-id:2937111548 --> @rick-github commented on GitHub (Jun 3, 2025): > I'm observing the same behavior when stopping the model between runs > The same issue occurs when running the model exclusively on the CPU: That's interesting - it's independent of the runner and GPU implying a server fault. I raised it as a possible issue, but in my experience it's never the server. How did you install ollama?
GiteaMirror commented 2026-04-29 04:36:25 -05:00
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@TobiasG0 commented on GitHub (Jun 3, 2025):

How did you install ollama?

I use the official Ollama Docker image: https://hub.docker.com/r/ollama/ollama

More specifically, I use the following Docker Compose file:
docker-compose.txt

<!-- gh-comment-id:2937220914 --> @TobiasG0 commented on GitHub (Jun 3, 2025): > How did you install ollama? I use the official Ollama Docker image: https://hub.docker.com/r/ollama/ollama More specifically, I use the following Docker Compose file: [docker-compose.txt](https://github.com/user-attachments/files/20580953/docker-compose.txt)
GiteaMirror commented 2026-04-29 04:36:25 -05:00
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@SugarcaneDefender commented on GitHub (Jun 19, 2025):

I will also add that I get this issue, also when using the Ollama API on a Windows install. I have this issue with Llava Phi-3. @TobiasG0 , when you send the image, what does your prompt look like? Mine contains text and words, and some of the words are in additional appended messages in the history.

Perhaps there is an issue with the image pipeline with the API when sending multiple messages?

<!-- gh-comment-id:2986091965 --> @SugarcaneDefender commented on GitHub (Jun 19, 2025): I will also add that I get this issue, also when using the Ollama API on a Windows install. I have this issue with Llava Phi-3. @TobiasG0 , when you send the image, what does your prompt look like? Mine contains text and words, and some of the words are in additional appended messages in the history. Perhaps there is an issue with the image pipeline with the API when sending multiple messages?
GiteaMirror commented 2026-04-29 04:36:26 -05:00
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@SugarcaneDefender commented on GitHub (Jun 19, 2025):

Update - lowering the ammount of context I am sending fixed the issue, I would try raw dogging a message with no context from the API. My issue is solved (although it still not good that it isn't able to auto-shorten context as needed). For future reference of anyone reading this, I also got a lot of "You" and "Your" generated when mine LLM was making garbage. Hope this helps someone!

<!-- gh-comment-id:2986098864 --> @SugarcaneDefender commented on GitHub (Jun 19, 2025): Update - lowering the ammount of context I am sending fixed the issue, I would try raw dogging a message with no context from the API. My issue is solved (although it still not good that it isn't able to auto-shorten context as needed). For future reference of anyone reading this, I also got a lot of "You" and "Your" generated when mine LLM was making garbage. Hope this helps someone!
GiteaMirror commented 2026-04-29 04:36:26 -05:00
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@TobiasG0 commented on GitHub (Jun 19, 2025):

When you send the image, what does your prompt look like?

I'm sending a single user message that includes both the image and the prompt. The prompt usually contains instructions along with additional information about the image. That's also why I’ve set my context window to 32k - some prompts can be quite long due to the additional information.

I could also reduce my context, but then some of the information about the image would be missing. The model should normally be able to handle such large contexts.

<!-- gh-comment-id:2989129281 --> @TobiasG0 commented on GitHub (Jun 19, 2025): > When you send the image, what does your prompt look like? I'm sending a single user message that includes both the image and the prompt. The prompt usually contains instructions along with additional information about the image. That's also why I’ve set my context window to 32k - some prompts can be quite long due to the additional information. I could also reduce my context, but then some of the information about the image would be missing. The model should normally be able to handle such large contexts.
GiteaMirror commented 2026-04-29 04:36:26 -05:00
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@TobiasG0 commented on GitHub (Jun 19, 2025):

@rick-github, are there any updates from your side?

<!-- gh-comment-id:2989132071 --> @TobiasG0 commented on GitHub (Jun 19, 2025): @rick-github, are there any updates from your side?
GiteaMirror commented 2026-04-29 04:36:26 -05:00
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@PDeveloper commented on GitHub (Jun 30, 2025):

I am getting the same behaviour after updating - any llama3.2-vision request will output the json response, followed by extremely random tokens. This wasn't there before updating (and I haven't in a long time).

Now I'm considering reverting otherwise my project is cooked.

Instead of using the Pydantic Model JsonSchemaValue for the format, if I simply use 'json' as a string for the format, it works correctly...

<!-- gh-comment-id:3018684324 --> @PDeveloper commented on GitHub (Jun 30, 2025): I am getting the same behaviour after updating - any llama3.2-vision request will output the json response, followed by extremely random tokens. This wasn't there before updating (and I haven't in a long time). Now I'm considering reverting otherwise my project is cooked. Instead of using the Pydantic Model JsonSchemaValue for the format, if I simply use 'json' as a string for the format, it works correctly...
GiteaMirror commented 2026-04-29 04:36:27 -05:00
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@tgreenhaw5 commented on GitHub (Jun 30, 2025):

I also have this issue with exactly the same symptoms. Loaded on windows 11 and it does this both by CLI and API. Ran a clean install and even tried running an old installer that used to work fine.

<!-- gh-comment-id:3019348653 --> @tgreenhaw5 commented on GitHub (Jun 30, 2025): I also have this issue with exactly the same symptoms. Loaded on windows 11 and it does this both by CLI and API. Ran a clean install and even tried running an old installer that used to work fine.
GiteaMirror commented 2026-04-29 04:36:27 -05:00
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@PDeveloper commented on GitHub (Jul 1, 2025):

Working the problem over the last day, I have managed to get some improvement by modifying the raw json schema Pydantic gives me:

        json_schema = Model.model_json_schema()
        json_schema['required'] = ['all', 'model', 'properties']
        json_schema['type'] = 'object'
        json_schema['additionalProperties'] = False

As well as removing any Optional fields from the model. This gets me the old behaviour, only trouble now is it sometimes returns an incomplete response, so the json string randomly ends before finishing. For this I added a retry loop increasing the temperature as a workaround, but still highly unreliable... (I know these are LLMs, but before there was some semblance of consistency).

Hope this helps others.

Edit:
Even with this method, I'm still getting the garbage output once in a while, or incomplete JSONs:

{ "key1": "value1", "key2": "value2", "key3": "value3", "key4": "incomplete valu /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  /  / 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<!-- gh-comment-id:3023078907 --> @PDeveloper commented on GitHub (Jul 1, 2025): Working the problem over the last day, I have managed to get some improvement by modifying the raw json schema Pydantic gives me: ``` json_schema = Model.model_json_schema() json_schema['required'] = ['all', 'model', 'properties'] json_schema['type'] = 'object' json_schema['additionalProperties'] = False ``` As well as removing any Optional fields from the model. This gets me the old behaviour, only trouble now is it sometimes returns an incomplete response, so the json string randomly ends before finishing. For this I added a retry loop increasing the temperature as a workaround, but still highly unreliable... (I know these are LLMs, but before there was some semblance of consistency). Hope this helps others. Edit: Even with this method, I'm still getting the garbage output once in a while, or incomplete JSONs: ``` { "key1": "value1", "key2": "value2", "key3": "value3", "key4": "incomplete valu / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / 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GiteaMirror commented 2026-04-29 04:36:28 -05:00
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@TobiasG0 commented on GitHub (Jul 6, 2025):

Update: The issue persists with Ollama version 0.9.5.

<!-- gh-comment-id:3041897372 --> @TobiasG0 commented on GitHub (Jul 6, 2025): Update: The issue persists with Ollama version 0.9.5.
GiteaMirror commented 2026-04-29 04:36:28 -05:00
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@tgreenhaw5 commented on GitHub (Jul 7, 2025):

I got better results when I set option no repeat to 1 to force no repetition, but it’s not working really well anymore. It’s likely that an undefined default variable or default settings changed without regression testing.

I switched to qwen2.5vl:7b and it’s working without issues and frankly better results.

<!-- gh-comment-id:3046427607 --> @tgreenhaw5 commented on GitHub (Jul 7, 2025): I got better results when I set option no repeat to 1 to force no repetition, but it’s not working really well anymore. It’s likely that an undefined default variable or default settings changed without regression testing. I switched to qwen2.5vl:7b and it’s working without issues and frankly better results.
GiteaMirror commented 2026-04-29 04:36:29 -05:00
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@viulian commented on GitHub (Aug 15, 2025):

I (think I) encounter this identical problem while doing batch processing of images. ollama version is 0.11.4.

Without num_predict ollama seemed to "hang" somewhere between the 2nd and 5th image. With num_predict set, it doesn't hang, but responses start having gibberish. Restarting ollama produces correct output, but the same situation repeats.

Adding a 5s sleep after each sequential call seems to help - it handles more calls before responses have gibberish - but, eventually, it ends up in the same scenario.

<!-- gh-comment-id:3190785804 --> @viulian commented on GitHub (Aug 15, 2025): I (think I) encounter this identical problem while doing batch processing of images. ollama version is 0.11.4. Without `num_predict` ollama seemed to "hang" somewhere between the 2nd and 5th image. With `num_predict` set, it doesn't hang, but responses start having gibberish. Restarting ollama produces correct output, but the same situation repeats. Adding a 5s sleep after each sequential call seems to help - it handles more calls before responses have gibberish - but, eventually, it ends up in the same scenario.
GiteaMirror commented 2026-04-29 04:36:30 -05:00
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@raffaeler commented on GitHub (Sep 13, 2025):

I am also stuck with batch processing hanging after a few images. It works using other models like llava (which is definitely inferior for quality of the descriptions).

<!-- gh-comment-id:3288120598 --> @raffaeler commented on GitHub (Sep 13, 2025): I am also stuck with batch processing hanging after a few images. It works using other models like llava (which is definitely inferior for quality of the descriptions).
GiteaMirror commented 2026-04-29 04:36:30 -05:00
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@tgreenhaw5 commented on GitHub (Sep 13, 2025):

I am also stuck with batch processing hanging after a few images. It works using other models like llava (which is definitely inferior for quality of the descriptions).

Have you tried qwen2.5vl:7b? How does it compare for you?

<!-- gh-comment-id:3288507253 --> @tgreenhaw5 commented on GitHub (Sep 13, 2025): > I am also stuck with batch processing hanging after a few images. It works using other models like llava (which is definitely inferior for quality of the descriptions). Have you tried qwen2.5vl:7b? How does it compare for you?
GiteaMirror commented 2026-04-29 04:36:31 -05:00
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@raffaeler commented on GitHub (Sep 14, 2025):

@tgreenhaw5 The model card on the Ollama library reports that qwen2.5vl requires Ollama 0.7.0.
I am not sure if this means an older version or the current, newer version.
Did you try it with the current release? I certainly can't go back for other models.

<!-- gh-comment-id:3289318740 --> @raffaeler commented on GitHub (Sep 14, 2025): @tgreenhaw5 The model card on the Ollama library reports that `qwen2.5vl` requires `Ollama 0.7.0`. I am not sure if this means an older version or the current, newer version. Did you try it with the current release? I certainly can't go back for other models.
GiteaMirror commented 2026-04-29 04:36:31 -05:00
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@tgreenhaw5 commented on GitHub (Sep 14, 2025):

I'm was using 0.11.4 and it works fine. I noticed I was a behind few minor versions so I loaded that and OLLAMA -v reports 0.11.10 and it too works fine.

Maybe "requires Ollama 0.7.0" means minimum version?

<!-- gh-comment-id:3289541282 --> @tgreenhaw5 commented on GitHub (Sep 14, 2025): I'm was using 0.11.4 and it works fine. I noticed I was a behind few minor versions so I loaded that and OLLAMA -v reports 0.11.10 and it too works fine. Maybe "requires Ollama 0.7.0" means minimum version?
GiteaMirror commented 2026-04-29 04:36:31 -05:00
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@raffaeler commented on GitHub (Sep 14, 2025):

@tgreenhaw5 Yes qwen2.5vl works correctly. Anyway, the model generates much shorter descriptions compared to llama3.2-vision. For my use-case, a detailed description worked better.

I hope the incompatibility between the current Ollama "newer" versions and the llama3.2-vision model will be fixed soon.

<!-- gh-comment-id:3289612217 --> @raffaeler commented on GitHub (Sep 14, 2025): @tgreenhaw5 Yes `qwen2.5vl` works correctly. Anyway, the model generates much shorter descriptions compared to `llama3.2-vision`. For my use-case, a detailed description worked better. I hope the incompatibility between the current Ollama "newer" versions and the `llama3.2-vision` model will be fixed soon.
GiteaMirror commented 2026-04-29 04:36:32 -05:00
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@rick-github commented on GitHub (Sep 14, 2025):

In general, qwen2.5vl works fine for any release from 0.7.0 onwards. Similarly, llama3.2-vision should work across all ollama versions since 0.4.0. The problem is, frustratingly, that there's a small set of users for which the model doesn't perform fine. I have been unable to replicate the problem on a variety of Nvidia and AMD hardware. If you are able to supply a script or procedure that reliably demonstrates the problem that would be very helpful. Failing that, a full log with OLLAMA_DEBUG=1 might provide some insight.

<!-- gh-comment-id:3289631406 --> @rick-github commented on GitHub (Sep 14, 2025): In general, qwen2.5vl works fine for any release from 0.7.0 onwards. Similarly, llama3.2-vision should work across all ollama versions since 0.4.0. The problem is, frustratingly, that there's a small set of users for which the model doesn't perform fine. I have been unable to replicate the problem on a variety of Nvidia and AMD hardware. If you are able to supply a script or procedure that reliably demonstrates the problem that would be very helpful. Failing that, a full log with `OLLAMA_DEBUG=1` might provide some insight.
GiteaMirror commented 2026-04-29 04:36:32 -05:00
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@raffaeler commented on GitHub (Sep 14, 2025):

@rick-github I understand that, in fact in my Python code I can make a couple of requests and the description generation is successful. After that it simply hangs and I could not find no useful log to provide an explanation.
FYI I am on Ubuntu 24.04 with an RTX4060 with the headless drivers which makes the GPU only available to apps (in this case the script using Ollama).

I'll do more research, but it can take some time as I am preparing a talk and I can currently rely on the descriptions (several hundreds) I generated (with llama3.2-vision) before upgrading Ollama.

<!-- gh-comment-id:3289637238 --> @raffaeler commented on GitHub (Sep 14, 2025): @rick-github I understand that, in fact in my Python code I can make a couple of requests and the description generation is successful. After that it simply hangs and I could not find no useful log to provide an explanation. FYI I am on Ubuntu 24.04 with an RTX4060 with the headless drivers which makes the GPU only available to apps (in this case the script using Ollama). I'll do more research, but it can take some time as I am preparing a talk and I can currently rely on the descriptions (several hundreds) I generated (with `llama3.2-vision`) before upgrading Ollama.
GiteaMirror commented 2026-04-29 04:36:33 -05:00
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@tgreenhaw5 commented on GitHub (Sep 14, 2025):

In general, qwen2.5vl works fine for any release from 0.7.0 onwards. Similarly, llama3.2-vision should work across all ollama versions since 0.4.0. The problem is, frustratingly, that there's a small set of users for which the model doesn't perform fine. I have been unable to replicate the problem on a variety of Nvidia and AMD hardware. If you are able to supply a script or procedure that reliably demonstrates the problem that would be very helpful. Failing that, a full log with OLLAMA_DEBUG=1 might provide some insight.

<!-- gh-comment-id:3289806906 --> @tgreenhaw5 commented on GitHub (Sep 14, 2025): > In general, qwen2.5vl works fine for any release from 0.7.0 onwards. Similarly, llama3.2-vision should work across all ollama versions since 0.4.0. The problem is, frustratingly, that there's a small set of users for which the model doesn't perform fine. I have been unable to replicate the problem on a variety of Nvidia and AMD hardware. If you are able to supply a script or procedure that reliably demonstrates the problem that would be very helpful. Failing that, a full log with `OLLAMA_DEBUG=1` might provide some insight.
GiteaMirror commented 2026-04-29 04:36:33 -05:00
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@tgreenhaw5 commented on GitHub (Sep 14, 2025):

Interesting... I was going to provide as much detail as possible to replicate the issue, but I can no longer get it to fail. llamma3.2-vision failed almost immediately every time for me but it has been a while since I tried. I updated from 0.11.4 to 0.11.10 today and perhaps something has changed that corrects the problem.

I use Windows 11 on a 3090.

If I can get it to fail again, I will provide as much detail as possible.

<!-- gh-comment-id:3289809674 --> @tgreenhaw5 commented on GitHub (Sep 14, 2025): Interesting... I was going to provide as much detail as possible to replicate the issue, but I can no longer get it to fail. llamma3.2-vision failed almost immediately every time for me but it has been a while since I tried. I updated from 0.11.4 to 0.11.10 today and perhaps something has changed that corrects the problem. I use Windows 11 on a 3090. If I can get it to fail again, I will provide as much detail as possible.
GiteaMirror commented 2026-04-29 04:36:34 -05:00
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@raffaeler commented on GitHub (Sep 14, 2025):

I'm also on 0.11.10 but on Ubuntu, and it always fails after very few images.

<!-- gh-comment-id:3289827879 --> @raffaeler commented on GitHub (Sep 14, 2025): I'm also on `0.11.10` but on Ubuntu, and it always fails after very few images.
GiteaMirror commented 2026-04-29 04:36:34 -05:00
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@0x6r1an0y commented on GitHub (Sep 21, 2025):

Environment:

  • Ollama version: 0.12.0
  • OS: Windows 11
  • GPU: NVIDIA RTX 3090 (driver 576.80, CUDA 12.9)

Still get gibberish on llama3.2-vision. Switched to qwen2.5-vl and got better results.

<!-- gh-comment-id:3315462556 --> @0x6r1an0y commented on GitHub (Sep 21, 2025): Environment: - Ollama version: `0.12.0` - OS: `Windows 11` - GPU: `NVIDIA RTX 3090 (driver 576.80, CUDA 12.9)` Still get gibberish on `llama3.2-vision`. Switched to `qwen2.5-vl` and got better results.
GiteaMirror commented 2026-04-29 04:36:35 -05:00
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@x86Gr commented on GitHub (Oct 4, 2025):

ollama 12.3
Debian 13.1
bare metal installation, no docker.

I can confirm that llama3.2 vision worked wonders up until about version 0.8.0 or earlier, with 12.3 it's simply unusable, tried on the same dataset and the results went from 11b rivalling gemma3-27b Q4_K_M to being garbage.

<!-- gh-comment-id:3368413423 --> @x86Gr commented on GitHub (Oct 4, 2025): ollama 12.3 Debian 13.1 bare metal installation, no docker. I can confirm that llama3.2 vision worked wonders up until about version 0.8.0 or earlier, with 12.3 it's simply unusable, tried on the same dataset and the results went from 11b rivalling gemma3-27b Q4_K_M to being garbage.
GiteaMirror commented 2026-04-29 04:36:36 -05:00
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@rick-github commented on GitHub (Oct 4, 2025):

If you are able to supply a script or procedure that reliably demonstrates the problem that would be very helpful. Failing that, a full log with OLLAMA_DEBUG=1 might provide some insight.

<!-- gh-comment-id:3368551457 --> @rick-github commented on GitHub (Oct 4, 2025): If you are able to supply a script or procedure that reliably demonstrates the problem that would be very helpful. Failing that, a full log with OLLAMA_DEBUG=1 might provide some insight.
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