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https://api.github.com/repos/ollama/ollama/issues/5092
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| 2,356,264,443
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I_kwDOJ0Z1Ps6Mcb37
| 5,092
|
could support llama3 chinese model
|
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| 2024-06-17T03:22:52
| 2024-06-17T09:16:36
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could support llama3 chinese model?
FlagAlpha/Llama3-Chinese-8B-Instruct
same as the other os llama2-chinese
model link: https://github.com/LlamaFamily/Llama-Chinese?tab=readme-ov-file#llama3%E4%B8%AD%E6%96%87%E5%BE%AE%E8%B0%83%E6%A8%A1%E5%9E%8B
https://huggingface.co/FlagAlpha/Llama3-Chinese-8B-Instruct
| null |
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I_kwDOJ0Z1Ps6ON6a_
| 5,431
|
out of memory error when running mixtral:8x22b
|
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### What is the issue?
**GPU:** Nvidia GeForce RTX 2070 (7.5 GB)
**RAM:** 16 GB
**Problem:**
I've pulled mixtral:8x22b (`ollama pull`) and would like to run it. After typing `ollama run mixtral:8x22b` the process terminates with Error: llama runner process has terminated: exit status 0xc0000409
When looking in the server.log I can see that it fails with the memory error and tells me that it couldn't allocate enough memory.
I've read a lot of issues here and the workaround to limit OLLAMA_MAX_VRAM (limited to 6 GB) didn't help either.
Could you please explain whats the error and how I could handle it? Many thanks in advance!
**Log:**
2024/07/02 10:51:58 routes.go:1064: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:6000000000 OLLAMA_MODELS:C:\\Users\\-----\\.ollama\\models OLLAMA_NOHISTORY:false 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:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR:C:\\Users\\-----\\AppData\\Local\\Programs\\Ollama\\ollama_runners OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-07-02T10:51:58.666+02:00 level=INFO source=images.go:730 msg="total blobs: 5"
time=2024-07-02T10:51:58.668+02:00 level=INFO source=images.go:737 msg="total unused blobs removed: 0"
time=2024-07-02T10:51:58.671+02:00 level=INFO source=routes.go:1111 msg="Listening on 127.0.0.1:11434 (version 0.1.48)"
time=2024-07-02T10:51:58.674+02:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11.3 rocm_v5.7]"
time=2024-07-02T10:51:58.856+02:00 level=INFO source=types.go:98 msg="inference compute" id=GPU-9b341200-a290-ff84-3a34-412d81b35c4f library=cuda compute=7.5 driver=12.5 name="NVIDIA GeForce RTX 2070 SUPER" total="8.0 GiB" available="7.0 GiB"
[GIN] 2024/07/02 - 10:51:58 | 200 | 0s | 127.0.0.1 | HEAD "/"
[GIN] 2024/07/02 - 10:51:58 | 200 | 21.6534ms | 127.0.0.1 | POST "/api/show"
time=2024-07-02T10:51:58.970+02:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=57 layers.offload=3 layers.split="" memory.available="[7.6 GiB]" memory.required.full="77.5 GiB" memory.required.partial="7.1 GiB" memory.required.kv="448.0 MiB" memory.required.allocations="[7.1 GiB]" memory.weights.total="74.2 GiB" memory.weights.repeating="74.1 GiB" memory.weights.nonrepeating="157.5 MiB" memory.graph.full="244.0 MiB" memory.graph.partial="1.3 GiB"
time=2024-07-02T10:51:58.981+02:00 level=INFO source=server.go:368 msg="starting llama server" cmd="C:\\Users\\-----\\AppData\\Local\\Programs\\Ollama\\ollama_runners\\cuda_v11.3\\ollama_llama_server.exe --model C:\\Users\\-----\\.ollama\\models\\blobs\\sha256-d0eeef8264ce10a7e578789ee69986c66425639e72c9855e36a0345c230918c9 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 3 --no-mmap --parallel 1 --port 51455"
time=2024-07-02T10:51:59.007+02:00 level=INFO source=sched.go:382 msg="loaded runners" count=1
time=2024-07-02T10:51:59.007+02:00 level=INFO source=server.go:556 msg="waiting for llama runner to start responding"
time=2024-07-02T10:51:59.008+02:00 level=INFO source=server.go:594 msg="waiting for server to become available" status="llm server error"
INFO [wmain] build info | build=3171 commit="7c26775a" tid="6888" timestamp=1719910319
INFO [wmain] system info | n_threads=4 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 = 1 | " tid="6888" timestamp=1719910319 total_threads=8
INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="51455" tid="6888" timestamp=1719910319
llama_model_loader: loaded meta data with 28 key-value pairs and 563 tensors from C:\Users\-----\.ollama\models\blobs\sha256-d0eeef8264ce10a7e578789ee69986c66425639e72c9855e36a0345c230918c9 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = Mixtral-8x22B-Instruct-v0.1
llama_model_loader: - kv 2: llama.block_count u32 = 56
llama_model_loader: - kv 3: llama.context_length u32 = 65536
llama_model_loader: - kv 4: llama.embedding_length u32 = 6144
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 16384
llama_model_loader: - kv 6: llama.attention.head_count u32 = 48
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.expert_count u32 = 8
llama_model_loader: - kv 11: llama.expert_used_count u32 = 2
llama_model_loader: - kv 12: general.file_type u32 = 2
llama_model_loader: - kv 13: llama.vocab_size u32 = 32768
llama_model_loader: - kv 14: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 15: tokenizer.ggml.model str = llama
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,32768] = ["<unk>", "<s>", "</s>", "[INST]", "[...
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,32768] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,32768] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template.tool_use str = {{bos_token}}{% set user_messages = m...
llama_model_loader: - kv 25: tokenizer.chat_templates arr[str,1] = ["tool_use"]
llama_model_loader: - kv 26: tokenizer.chat_template str = {{bos_token}}{% for message in messag...
llama_model_loader: - kv 27: general.quantization_version u32 = 2
llama_model_loader: - type f32: 113 tensors
llama_model_loader: - type f16: 56 tensors
llama_model_loader: - type q4_0: 281 tensors
llama_model_loader: - type q8_0: 112 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 = 0.1732 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32768
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 65536
llm_load_print_meta: n_embd = 6144
llm_load_print_meta: n_head = 48
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 56
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 6
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 16384
llm_load_print_meta: n_expert = 8
llm_load_print_meta: n_expert_used = 2
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 = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 65536
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 = 8x22B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 140.63 B
llm_load_print_meta: model size = 74.05 GiB (4.52 BPW)
llm_load_print_meta: general.name = Mixtral-8x22B-Instruct-v0.1
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 = 781 '<0x0A>'
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 2070 SUPER, compute capability 7.5, VMM: yes
llm_load_tensors: ggml ctx size = 0.56 MiB
ggml_cuda_host_malloc: failed to allocate 71783.23 MiB of pinned memory: out of memory
ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 75270168608
llama_model_load: error loading model: unable to allocate backend buffer
llama_load_model_from_file: exception loading model
time=2024-07-02T10:51:59.625+02:00 level=ERROR source=sched.go:388 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000409 "
[GIN] 2024/07/02 - 10:51:59 | 500 | 707.5768ms | 127.0.0.1 | POST "/api/chat"
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.48
|
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| 2,110,690,190
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I_kwDOJ0Z1Ps59zpOO
| 2,290
|
LLaVA 1.6 now available
|
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| 2024-01-31T18:13:03
| 2024-02-10T17:20:33
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https://llava-vl.github.io/blog/2024-01-30-llava-1-6/
Supposedly a big improvement
|
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PR_kwDOJ0Z1Ps5gKs12
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fix: gguf int type
|
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I_kwDOJ0Z1Ps6NTPNW
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Will you please add this agent to your community integration list in your readme?
|
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This agent runs ollama and manages memories in files. https://github.com/MikeyBeez/RAGAgent.
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PR_kwDOJ0Z1Ps5f0ZTD
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Adding `ogpt.nvim` into the list of plugins!
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ChatGPT.nvim is a well built plugin. `ogpt.nvim` is a fork that supports Ollama
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PR_kwDOJ0Z1Ps5l-fbz
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Get paths right for first run, and deps
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| 2024-02-05T04:51:02
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Tested inside a Win 10 home hyperV VM with nothing extra added. This gets all the deps right and loads the CPU runner.
|
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PR_kwDOJ0Z1Ps5W19mq
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log prediction failures
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| 2023-07-31T20:47:07
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this will help track down #241
|
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PR_kwDOJ0Z1Ps5yjED-
| 5,058
|
gpu: Fix build warning
|
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Fix build warning
```
# github.com/ollama/ollama/gpu
gpu_info_oneapi.c: In function ‘oneapi_check_vram’:
gpu_info_oneapi.c:163:51: warning: format not a string literal and no format arguments [-Wformat-security]
163 | snprintf(&resp->gpu_name[0], GPU_NAME_LEN, props.modelName);
| ~~~~~^~~~~~~~~~
```
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| 2,187,147,964
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PR_kwDOJ0Z1Ps5prGG4
| 3,148
|
fix: support wide characters in lib path
|
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| 2024-03-14T19:46:53
| 2024-05-09T16:50:16
| 2024-05-09T16:50:16
|
CONTRIBUTOR
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Previous behavior:
```
Error: Unable to load dynamic library: Unable to load dynamic server library: ������ ������ ã�� �� �����ϴ�.
```
When the user's home path contains unicode characters on Windows the packaged runtime libraries failed to open, add support for wide characters to fix this.
resolves #2615
resolces #3367
|
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| 2,527,433,452
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I_kwDOJ0Z1Ps6WpZLs
| 6,820
|
Typo in Gemma 2 model card
|
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| 2024-09-16T03:38:58
| 2024-09-16T03:45:57
| 2024-09-16T03:45:57
|
NONE
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For some reason at https://ollama.com/library/gemma2 it says
```
Google Gemma 2 is a high-performing and efficient model by now available in three sizes: 2B, 9B, and 27B.
```
Which makes zero sense if you read the `efficient model by now available in three sizes: 2B, 9B, and 27B. ` part which I assume should be `efficient model by Google now available in three sizes: 2B, 9B, and 27B.`
|
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I_kwDOJ0Z1Ps6RZsnZ
| 6,093
|
Only one of the dual CPUs is in use
|
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| 2024-07-31T08:20:02
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NONE
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### What is the issue?
My machine has two CPUs without GPUs, and when I run the model, I find that the CPUs are used at most 50%
### OS
Linux
### GPU
Other
### CPU
Intel
### Ollama version
0.3.0
|
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| 2,076,244,566
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PR_kwDOJ0Z1Ps5jyPbc
| 1,924
|
Add group delete to uninstall instructions
|
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| 2024-01-11T10:24:57
| 2024-01-12T05:07:01
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CONTRIBUTOR
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|
After executing the `userdel ollama` command, I saw this message:
```sh
$ sudo userdel ollama
userdel: group ollama not removed because it has other members.
```
Which reminded me that I had to remove the dangling group too. For completeness, the uninstall instructions should do this too.
Thanks!
|
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PR_kwDOJ0Z1Ps5s-d7I
| 3,713
|
update copy handler to use model.Name
|
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| 2024-04-17T21:12:53
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I_kwDOJ0Z1Ps5sJd2b
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|
Start/stop tokens seem to bug out sometimes in long winded sessions
|
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| 2023-07-20T16:48:25
| 2023-07-28T00:20:57
| 2023-07-28T00:20:57
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stuff like:
```
>>> ... user prompt ...
...some response here...
<<SYS>>
You are an expert at summarizing text documents step by step and preserving
information. Between each of our interactions, summarize my message in a bullet
point summary, including all previously summarized information.
<</SYS>>
>>> ...
```
I've also seen it have conversations back and forth with itself lol but that might have been my fault due to mucking with instruction formats
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PR_kwDOJ0Z1Ps5nkNSS
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|
Adding '--tag' to install.sh to simplify pre-release or specific version install locally
|
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| 2024-02-21T19:38:39
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| 2024-05-07T23:39:58
|
NONE
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Adding a 'feature' to the install script for Linux users that allows specifying the `--tag` on installation.
### Why
There will be times where we'll want to allow early-adopters to quickly access and test new features; however, they're not stable enough for a release.
### Solution
Allow the user to specify a dynamic tag on the `install.sh` script. The script is 100% backwards compatible with current functionality and acts exactly the same without the 'tag' argument.
### Error Handling
In the case the user doesn't specify a tag or specifies 'latest' they'll get the exact same functionality today.
If they specify a tag which is semantically invalid based on historical tag scheme, e.g. v[number].[number].[number] then the system will exit in an error. This can easily be removed if the scheme changes.
If the user specifies a valid tag name that doesn't exist, the download can't be found, or github is down then the script fails and provides an error to specify that the download failed.
In both cases, trying again with the 'latest' release would provide the user a potentially confusing understanding of their local state where they may miss the error output and think they're running a newer or different version of the tool.
### Follow on Work
If this PR is accepted, we may want to consider documenting in the core readme where we discuss this process
```curl -fsSL https://ollama.com/install.sh | sh```
To share how to download a specific tag. I wouldn't think removing the existing simple script is a good idea bc it complicates the experience for new users.
```curl -fsSL https://ollama.com/install.sh | sh -s -- --tag "$TAG"```
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PR_kwDOJ0Z1Ps5cx7Fd
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|
server: print version on start
|
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| 2023-10-13T23:11:39
| 2023-10-16T16:59:31
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|
CONTRIBUTOR
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I_kwDOJ0Z1Ps6gvYFt
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Error: pull model manifest, SSL_ERROR_SYSCALL in connection to ollama.com:443
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### What is the issue?
When I run 'ollama run ollama run llama3:8b', the following message appears:
`pulling manifest
Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/qwen2.5-coder/manifests/32b": EOF
`
Execute `curl fsSL' https://ollama.com/install.sh |It also appears when sh `, the following message appears:
` curl: (35) OpenSSL SSL_connect: SSL_ERROR_SYSCALL in connection to ollama. com: 443 `cerror.
How can it be resolved?
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
_No response_
|
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How to load multiple but same species models on different GPUs?
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| 2024-08-20T09:07:52
| 2024-10-22T23:58:14
| 2024-10-22T23:58:13
|
NONE
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### What is the issue?
Linux, I use the following command to start Ollama server:
CUDA_VISIBLE_DEVICES=1,2,3,4,5 OLLAMA_MAX_LOADED_MODELS=5 ./ollama-linux-amd64 serve&
Then I want to run several py files used llama3.1:70b, but when I run the several py files, then they all use the same model.
That's to say, only one Gpu is activated. I want to my five gpus to load diffrent llama3.1:70b to run the different py files.
Is there a way to achieve it? Thank you!
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
|
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| 2,184,284,619
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PR_kwDOJ0Z1Ps5phWR5
| 3,111
|
Update ollama.iss
|
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add arm64 support
|
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Failed to verify certificate: x509: certificate signed by unknown authority
|
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NONE
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I started using Ollama in the last 12hrs and I'm loving it... Why? Because I come from the CloudNative space, I've been working with Docker/Kubernetes Engineering for a while... I love the concept from Ollama and I can't wait until the `Modelfile` works well :)
# 🚨 Problem
* While pulling models, we get failures on the pull of models
* form the CLI
* to the API endpoints
# 🐳 Docker client
So, some clues on this:
* According to https://github.com/kubernetes/kubernetes/issues/43924#issuecomment-290905127, this error occurs when a docker client tries to pull docker images from an insecure Docker Registry...
* Considering Ollama uses a docker registry to implement the model repository, I would say it's possible ollama's backend is actually a Docker Registry whose TLS certs were self-signed...
* Meanwhile, ollama's CLI client runs a client that connects to the docker daemon to pull the Models...
* I don't get the same error running from my local machine, but I get it when running in a Kubernetes cluster...
* My local machine has all the bypass and lower security configuration while the Kubernetes cluster doesn't
At least I know registry.ollama.ai is a docker registry ;), which indicates the suspicions above...
```console
$ docker pull registry.ollama.ai/library/llama2
Using default tag: latest
latest: Pulling from library/llama2
unsupported media type application/vnd.ollama.image.model
```
# 👽 Using the API
* This is similar to the bug reported at https://github.com/jmorganca/ollama/issues/823, which I think it was prematurely closed...
```console
curl -i http://localhost:11434/api/pull -d '{"name": "llama2"}'
HTTP/1.1 200 OK
Content-Type: application/x-ndjson
Date: Thu, 0[9](jobs/1414268#step:5:10) Nov 2023 20:22:16 GMT
Transfer-Encoding: chunked
```
```json
{"status":"pulling manifest"}
{"error":"pull model manifest: Get "https://registry.ollama.ai/v2/library/llama2/manifests/latest": tls: failed to verify certificate: x509: certificate signed by unknown authority"}
```
> * Also the Json objects returned by the server is returning those extra/unescaped `"`
```console
$ echo '{"error":"pull model manifest: Get "https://registry.ollama.ai/v2/library/llama2/manifests/latest": tls: failed to verify certificate: x509: certificate signed by unknown authority"}' | jq
parse error: Invalid numeric literal at line 1, column 42
```
# 🔊 Server Logs
* According to the server logs, the error is printed at https://github.com/jmorganca/ollama/blob/main/server/images.go#L1170
* At least we should consider having the settings on `makeRequest` to take into account the certificate instructions
* https://github.com/jmorganca/ollama/blob/main/server/images.go#L1208
* If we want to do something like the docker or kubernetes approach, consider adding the the insecure tls option in the client
* https://github.com/jmorganca/ollama/blob/main/server/images.go#L1245-L1251
```console
Print service container logs: c470f383b37b44b6b05555572e49de37_dockerhubdockerartifactorycomollamaollama_c9a05e
/usr/local/bin/docker logs --details bcc88cf81ef4ff93[2](https://git.companycom/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:2)[3](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:3)50a2522c4596f0c7f01ee52432558f98b350be6319695d
Couldn't find '/root/.ollama/id_ed25519'. Generating new private key.
2023/11/09 22:16:[4](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:4)6 images.go:824: total blobs: 0
2023/11/09 22:16:46 images.go:831: total unused blobs removed: 0
2023/11/09 22:16:46 routes.go:680: Listening on [::]:11434 (version 0.1.8)
Your new public key is:
ssh-ed2[5](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:5)519 AAAAC3NzaC1lZDI1NTE5AAAAID4[6](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:6)z8kD0XvZfSsZnSogyAdTu/06A0e0YvpxrRlSfIXA
2023/11/09 22:16:46 routes.go:[7](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:7)00: Warning: GPU support may not be enabled, check you have installed GPU drivers: nvidia-smi command failed
2023/11/09 22:16:47 images.go:1172: couldn't start upload: Get "https://registry.ollama.ai/v2/library/llama2/manifests/latest": tls: failed to verify certificate: x509: certificate signed by unknown authority
2023/11/09 22:16:4[8](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:8) images.go:1172: couldn't start upload: Get "https://registry.ollama.ai/v2/library/llama2/manifests/latest": tls: failed to verify certificate: x50[9](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:9): certificate signed by unknown authority
[GIN] 2023/[11](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:11)/09 - 22:16:47 | 200 | 93.842[15](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:15)9ms | 172.18.0.1 | POST "/api/pull"
[GIN] 2023/11/09 - 22:16:48 | 200 | 51.96959ms | 172.18.0.1 | POST "/api/pull"
[GIN] 2023/11/09 - 22:[16](https://git.company.com/seceng-devsecops-platform/devsecops-ai-llm-prreviewer/actions/runs/504830/jobs/1415211#step:13:16):48 | 404 | 160.559µs | 172.18.0.1 | POST "/api/generate"
```
# ❓ Approach to the problem
* I think the Ollama CLI and server must have settings to by-pass this security setting on the docker client against an "insecure" docker registry
* I know the intent is good to host `registry.ollama.ai`, but what if users deploy their own ollama registries on their enterprises?
* If we are to trust this registry, or any other deployed by anyone, I would think it's safe to say that the same toggles implemented by the Docker and Kuberentes communities should be added to ollama...
* Docker daemon can be configured to support accept insecure registries https://docs.docker.com/engine/reference/commandline/dockerd/#insecure-registries
* Users provide `--insecure-registry registry.company.myollama.genai` param (if I were to deploy my own)
# 🤔 Possible solution
* According to https://pkg.go.dev/github.com/docker/docker/client#section-readme,
> // InsecureSkipVerify controls whether a client verifies the server's
// certificate chain and host name. If InsecureSkipVerify is true, crypto/tls
// accepts any certificate presented by the server and any host name in that
// certificate. In this mode, TLS is susceptible to machine-in-the-middle
// attacks unless custom verification is used. This should be used only for
// testing or in combination with VerifyConnection or VerifyPeerCertificate.
* Then, a possible patch for this problem could be as follows:
* Adding the patch below to https://github.com/jmorganca/ollama/blob/main/server/images.go#L1245-L1251
* If we want to trust the configured MODELS_REGISTRY, then look for an env var `MODELS_REGISTRY_SKIP_TLS_CHECK` that specifies that...
```diff
client := http.Client{
Transport: &http.Transport{
Proxy: http.ProxyURL(proxyURL),
},
}
+ // Check if the registry TLS should be skipped
+ if skip, ok := os.LookupEnv("MODELS_REGISTRY_SKIP_TLS_CHECK"); ok && (skip == "true" || skip == "0") {
+ // If so, add an unverified TLS configuration to the HTTP client
+ client.Transport = &http.Transport{
+ TLSClientConfig: &tls.Config{
+ InsecureSkipVerify: true,
+ },
+ Proxy: http.ProxyURL(proxyURL),
+ }
+ }
```
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PR_kwDOJ0Z1Ps6CJ05x
| 7,705
|
fix: typo in wintray messages const
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Found small typo in const name
|
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OOM errors for large context models can be solved by reducing 'num_batch' down from the default of 512
|
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I thought I'd post this here in case it helps others suffering from OOM errors as I searched and can see no mention of either "num_batch" or "n_batch" anywhere here.
I've been having endless problems with OOM errors when I try to run models with a context length of 16k like "deepseek-coder:33b-instruct" and originally thought it was due to this:
```
// 75% of the absolute max number of layers we can fit in available VRAM, off-loading too many layers to the GPU can cause OOM errors
layers := int(info.FreeMemory/bytesPerLayer) * 3 / 4
```
But whatever I set that to (even tiny fractions like 1 / 100), I would still eventually get an OOM error after inputting a lot of data to the 16k models... I could actually see the VRAM use go up using nvidia-smi in Linux until it hit the 24GB of my 4090 and then crash.
So next I tried "num_gpu=0" and this did work (I still got the benefit of the cuBLAS for the prompt evaluation, but otherwise very slow generation...). As soon as I set this to even "num_gpu =1" then I would get an OOM error after inputting a lot of data (but still way less than 16k tokens) to the 16k models.
So I then went into the Ollama source and found there are some hidden "PARAMETER" settings not mentioned in "/docs/modelfile.md " that can be found in "api/types.go" and one of these is "num_batch" (which corresponds to "n_batch" in llama.cpp) and it turns out this is was the solution. The default value is 512 (which is inherited from llama.cpp) and I found that reducing it finally solved the OOT crash problem.
It looks like there may even be a relationship that it needs to be decreased by num_ctx/4096 (= 4 for the 16k context models), and this in turn could possibly have something to do with the 3 / 4 magic number in the code above and/or the fact tbat 4096 is a very common default context size?? Anyway, setting to 128 *almost* worked unless I deliberately fed in a file I have created that I know deepseek-coder:33b-instruct will tokenize into 16216 tokens... So I then reduced to 64 and have since fed this same file in 4-5 times using the chat completion API so the complete conversation is > 64k tokens and it still hasn't crashed yet (the poor thing had a meltdown after 64k tokens and just replied "I'm sorry, but I can't assist with that" though lol).
I suspect I could get even closer to 128 as it did almost work but atm I'm just leaving it at 64 to see how I get on...
It should be noted that num_batch has to be >=32 (as per the llama.cpp docs) or otherwise it won't use the cuBLAS kernels for prompt evaluations at all.
I suggest anybody suffering from similar OOM errors add this to their modelfiles, starting at 32:
```PARAMETER num_batch 32```
and keep doubling it until you get the OOM errors again.
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I_kwDOJ0Z1Ps6A153-
| 2,835
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CUDA out of memory error on Windows for ollama run starts up
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Hi there,
I just installed ollama 0.1.27 and tried to run gemma:2b but it suggest CUDA out of memory error. Could you please investigate and figure out root cause?
I'm using CPU `i7-4700HQ` with RAM 16G.
attached log and nvidia-smi report
>+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 531.41 Driver Version: 531.41 CUDA Version: 12.1 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce GTX 960M WDDM | 00000000:02:00.0 Off | N/A |
| N/A 0C P0 N/A / N/A| 181MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 272 C+G ...s (x86)\Mozilla Firefox\firefox.exe N/A |
| 0 N/A N/A 4520 C+G ....0_x64__8wekyb3d8bbwe\YourPhone.exe N/A |
| 0 N/A N/A 7580 C+G ....Experiences.TextInput.InputApp.exe N/A |
| 0 N/A N/A 9940 C+G ...2txyewy\StartMenuExperienceHost.exe N/A |
| 0 N/A N/A 11012 C+G ...t.LockApp_cw5n1h2txyewy\LockApp.exe N/A |
| 0 N/A N/A 12428 C+G ...cal\Microsoft\OneDrive\OneDrive.exe N/A |
| 0 N/A N/A 13100 C+G ...s (x86)\Mozilla Firefox\firefox.exe N/A |
| 0 N/A N/A 13332 C+G ...guoyun\bin-7.1.3\NutstoreClient.exe N/A |
+---------------------------------------------------------------------------------------+
log:
> [GIN] 2024/02/29 - 23:47:32 | 200 | 32.7µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/02/29 - 23:47:32 | 200 | 1.2447ms | 127.0.0.1 | POST "/api/show"
[GIN] 2024/02/29 - 23:47:32 | 200 | 2.4218ms | 127.0.0.1 | POST "/api/show"
time=2024-02-29T23:47:37.171+08:00 level=INFO source=gpu.go:94 msg="Detecting GPU type"
time=2024-02-29T23:47:37.171+08:00 level=INFO source=gpu.go:265 msg="Searching for GPU management library nvml.dll"
time=2024-02-29T23:47:37.216+08:00 level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [c:\\Windows\\System32\\nvml.dll C:\\Windows\\system32\\nvml.dll C:\\WINDOWS\\system32\\nvml.dll]"
time=2024-02-29T23:47:37.236+08:00 level=INFO source=gpu.go:99 msg="Nvidia GPU detected"
time=2024-02-29T23:47:37.236+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-02-29T23:47:37.248+08:00 level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 5.0"
time=2024-02-29T23:47:37.248+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-02-29T23:47:37.252+08:00 level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 5.0"
time=2024-02-29T23:47:37.253+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-02-29T23:47:37.253+08:00 level=INFO source=dyn_ext_server.go:385 msg="Updating PATH to
time=2024-02-29T23:47:37.328+08:00 level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: C:\\Users\\bolun\\AppData\\Local\\Temp\\ollama625311207\\cuda_v11.3\\ext_server.dll"
time=2024-02-29T23:47:37.329+08:00 level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server"
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 1 CUDA devices:
Device 0: NVIDIA GeForce GTX 960M, compute capability 5.0, VMM: yes
llama_model_loader: loaded meta data with 21 key-value pairs and 164 tensors from C:\Users\bolun\.ollama\models\blobs\sha256-c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 (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 = gemma
llama_model_loader: - kv 1: general.name str = gemma-2b-it
llama_model_loader: - kv 2: gemma.context_length u32 = 8192
llama_model_loader: - kv 3: gemma.block_count u32 = 18
llama_model_loader: - kv 4: gemma.embedding_length u32 = 2048
llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 16384
llama_model_loader: - kv 6: gemma.attention.head_count u32 = 8
llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 1
llama_model_loader: - kv 8: gemma.attention.key_length u32 = 256
llama_model_loader: - kv 9: gemma.attention.value_length u32 = 256
llama_model_loader: - kv 10: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 14: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256128] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256128] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256128] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: general.quantization_version u32 = 2
llama_model_loader: - kv 20: general.file_type u32 = 2
llama_model_loader: - type f32: 37 tensors
llama_model_loader: - type q4_0: 126 tensors
llama_model_loader: - type q8_0: 1 tensors
llm_load_vocab: mismatch in special tokens definition ( 544/256128 vs 388/256128 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = gemma
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 256128
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_head = 8
llm_load_print_meta: n_head_kv = 1
llm_load_print_meta: n_layer = 18
llm_load_print_meta: n_rot = 256
llm_load_print_meta: n_embd_head_k = 256
llm_load_print_meta: n_embd_head_v = 256
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 256
llm_load_print_meta: n_embd_v_gqa = 256
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: n_ff = 16384
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 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_yarn_orig_ctx = 8192
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 2B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 2.51 B
llm_load_print_meta: model size = 1.56 GiB (5.34 BPW)
llm_load_print_meta: general.name = gemma-2b-it
llm_load_print_meta: BOS token = 2 '<bos>'
llm_load_print_meta: EOS token = 1 '<eos>'
llm_load_print_meta: UNK token = 3 '<unk>'
llm_load_print_meta: PAD token = 0 '<pad>'
llm_load_print_meta: LF token = 227 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.13 MiB
llm_load_tensors: offloading 18 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 19/19 layers to GPU
llm_load_tensors: CPU buffer size = 531.52 MiB
llm_load_tensors: CUDA0 buffer size = 1594.93 MiB
.....................................................
llama_new_context_with_model: n_ctx = 2048
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 = 36.00 MiB
llama_new_context_with_model: KV self size = 36.00 MiB, K (f16): 18.00 MiB, V (f16): 18.00 MiB
llama_new_context_with_model: CUDA_Host input buffer size = 9.02 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 504.25 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 4.00 MiB
llama_new_context_with_model: graph splits (measure): 3
CUDA error: out of memory
current device: 0, in function ggml_cuda_pool_malloc_vmm at C:\Users\jeff\git\ollama\llm\llama.cpp\ggml-cuda.cu:7990
cuMemSetAccess(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, &access, 1)
GGML_ASSERT: C:\Users\jeff\git\ollama\llm\llama.cpp\ggml-cuda.cu:243: !"CUDA error"
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I_kwDOJ0Z1Ps6QBkFp
| 5,769
|
Update llama.cpp to support Ascend
|
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[] | null | 1
| 2024-07-18T13:11:29
| 2024-10-24T03:00:09
| 2024-10-24T03:00:09
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### What is the issue?
llama.cpp is able to support ascend via https://github.com/ggerganov/llama.cpp/pull/6035/files. I hope to update the llama.cpp submodule to the latest version. According to it, I can adapt ollama in the ascend
### OS
Linux
### GPU
Other
### CPU
_No response_
### Ollama version
v0.2.0
|
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I_kwDOJ0Z1Ps6HgU62
| 4,078
|
Use the already downloaded models
|
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| 2024-05-01T12:03:47
| 2024-05-03T22:57:24
| 2024-05-01T13:15:55
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NONE
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When we use the steps mentioned below and download the models it will create few files:
https://github.com/meta-llama/llama3?tab=readme-ov-file#quick-start
checklist.chk consolidated.00.pth params.json tokenizer.model
Here these file size is in GB.
Now, I want to switch to ollama and use these files how can I do without ollama run or ollama pull command .
As this will be duplicate of the same model.
|
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| 2,386,779,661
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PR_kwDOJ0Z1Ps50Oknp
| 5,439
|
Switch ARM64 container image base to rocky 8
|
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| 2024-07-02T17:24:21
| 2024-07-02T18:01:18
| 2024-07-02T18:01:15
|
COLLABORATOR
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The centos 7 arm mirrors have disappeared due to the EOL 2 days ago, and the vault sed workaround which works for x86 doesn't work for arm.
|
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I_kwDOJ0Z1Ps6ja1pD
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GPU not working on Windows.
|
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| 2024-12-16T08:44:42
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### What is the issue?
Hello, I am using the latest lama of today and am on a 10-year-old laptop. The logs say that Ollama is not detecting my GPU. I have a 4th Gen Intel i7 with 4 cores, 32 Gb of DDR3 Ram, a 4Gb 780M and am running Llama 3.3 Quantized 28 Gb. I'm pretty sure the framework is meant to buffer the AI on the GPU and slowly load it from memory. I don't think my GPU memory is the problem.
### Here are my Logs:
### App.Log:
time=2024-12-16T19:19:40.276+11:00 level=INFO source=logging.go:50 msg="ollama app started"
time=2024-12-16T19:19:40.276+11:00 level=INFO source=lifecycle.go:19 msg="app config" env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 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:C:\\Users\\odinl\\.ollama\\models OLLAMA_MULTIUSER_CACHE: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://*] OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-12-16T19:19:40.316+11:00 level=INFO source=server.go:182 msg="unable to connect to server"
time=2024-12-16T19:19:40.316+11:00 level=INFO source=server.go:141 msg="starting server..."
time=2024-12-16T19:19:40.898+11:00 level=INFO source=server.go:127 msg="started ollama server with pid 10904"
time=2024-12-16T19:19:40.898+11:00 level=INFO source=server.go:129 msg="ollama server logs C:\\Users\\odinl\\AppData\\Local\\Ollama\\server.log"
### Server Log:
2024/12/16 19:19:41 routes.go:1195: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 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:C:\\Users\\odinl\\.ollama\\models OLLAMA_MULTIUSER_CACHE: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://*] OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-12-16T19:19:41.040+11:00 level=INFO source=images.go:753 msg="total blobs: 6"
time=2024-12-16T19:19:41.041+11:00 level=INFO source=images.go:760 msg="total unused blobs removed: 0"
time=2024-12-16T19:19:41.043+11:00 level=INFO source=routes.go:1246 msg="Listening on 127.0.0.1:11434 (version 0.5.1)"
time=2024-12-16T19:19:41.047+11:00 level=INFO source=common.go:49 msg="Dynamic LLM libraries" runners="[cpu_avx2 cuda_v11 cuda_v12 rocm cpu cpu_avx]"
time=2024-12-16T19:19:41.049+11:00 level=INFO source=gpu.go:221 msg="looking for compatible GPUs"
time=2024-12-16T19:19:41.049+11:00 level=INFO source=gpu_windows.go:167 msg=packages count=1
time=2024-12-16T19:19:41.049+11:00 level=INFO source=gpu_windows.go:214 msg="" package=0 cores=4 efficiency=0 threads=8
time=2024-12-16T19:19:41.073+11:00 level=INFO source=gpu.go:620 msg="Unable to load cudart library C:\\Windows\\system32\\nvcuda.dll: symbol lookup for cuDeviceGetUuid failed: The specified procedure could not be found.\r\n"
time=2024-12-16T19:19:42.201+11:00 level=INFO source=gpu.go:386 msg="no compatible GPUs were discovered"
time=2024-12-16T19:19:42.201+11:00 level=INFO source=types.go:123 msg="inference compute" id=0 library=cpu variant=avx2 compute="" driver=0.0 name="" total="31.9 GiB" available="29.2 GiB"
[GIN] 2024/12/16 - 19:19:51 | 200 | 50.6µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/12/16 - 19:19:51 | 200 | 99.5398ms | 127.0.0.1 | POST "/api/show"
time=2024-12-16T19:19:51.613+11:00 level=INFO source=server.go:105 msg="system memory" total="31.9 GiB" free="29.0 GiB" free_swap="33.8 GiB"
time=2024-12-16T19:19:51.615+11:00 level=INFO source=memory.go:356 msg="offload to cpu" layers.requested=-1 layers.model=81 layers.offload=0 layers.split="" memory.available="[29.0 GiB]" memory.gpu_overhead="0 B" memory.required.full="28.1 GiB" memory.required.partial="0 B" memory.required.kv="2.5 GiB" memory.required.allocations="[28.1 GiB]" memory.weights.total="25.9 GiB" memory.weights.repeating="25.1 GiB" memory.weights.nonrepeating="822.0 MiB" memory.graph.full="1.1 GiB" memory.graph.partial="1.1 GiB"
time=2024-12-16T19:19:51.623+11:00 level=INFO source=server.go:397 msg="starting llama server" cmd="C:\\Users\\odinl\\AppData\\Local\\Programs\\Ollama\\lib\\ollama\\runners\\cpu_avx2\\ollama_llama_server.exe --model C:\\Users\\odinl\\.ollama\\models\\blobs\\sha256-35a6401f84b6c06d3d87140f6a437240cd02f65cc27216043911cda2bdde9137 --ctx-size 8192 --batch-size 512 --threads 4 --no-mmap --parallel 4 --port 49717"
time=2024-12-16T19:19:51.887+11:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
time=2024-12-16T19:19:51.888+11:00 level=INFO source=server.go:576 msg="waiting for llama runner to start responding"
time=2024-12-16T19:19:51.889+11:00 level=INFO source=server.go:610 msg="waiting for server to become available" status="llm server error"
time=2024-12-16T19:19:51.917+11:00 level=INFO source=runner.go:941 msg="starting go runner"
time=2024-12-16T19:19:51.918+11:00 level=INFO source=runner.go:942 msg=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 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | cgo(clang)" threads=4
time=2024-12-16T19:19:51.918+11:00 level=INFO source=.:0 msg="Server listening on 127.0.0.1:49717"
llama_model_loader: loaded meta data with 36 key-value pairs and 724 tensors from C:\Users\odinl\.ollama\models\blobs\sha256-35a6401f84b6c06d3d87140f6a437240cd02f65cc27216043911cda2bdde9137 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Llama 3.1 70B Instruct 2024 12
llama_model_loader: - kv 3: general.version str = 2024-12
llama_model_loader: - kv 4: general.finetune str = Instruct
llama_model_loader: - kv 5: general.basename str = Llama-3.1
llama_model_loader: - kv 6: general.size_label str = 70B
llama_model_loader: - kv 7: general.license str = llama3.1
llama_model_loader: - kv 8: general.base_model.count u32 = 1
llama_model_loader: - kv 9: general.base_model.0.name str = Llama 3.1 70B
llama_model_loader: - kv 10: general.base_model.0.organization str = Meta Llama
llama_model_loader: - kv 11: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Lla...
llama_model_loader: - kv 12: general.tags arr[str,5] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 13: general.languages arr[str,7] = ["fr", "it", "pt", "hi", "es", "th", ...
llama_model_loader: - kv 14: llama.block_count u32 = 80
llama_model_loader: - kv 15: llama.context_length u32 = 131072
llama_model_loader: - kv 16: llama.embedding_length u32 = 8192
llama_model_loader: - kv 17: llama.feed_forward_length u32 = 28672
llama_model_loader: - kv 18: llama.attention.head_count u32 = 64
llama_model_loader: - kv 19: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 20: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 21: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 22: llama.attention.key_length u32 = 128
llama_model_loader: - kv 23: llama.attention.value_length u32 = 128
llama_model_loader: - kv 24: general.file_type u32 = 10
llama_model_loader: - kv 25: llama.vocab_size u32 = 128256
llama_model_loader: - kv 26: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 27: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 28: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 29: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 30: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 31: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 32: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 33: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 34: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 35: general.quantization_version u32 = 2
llama_model_loader: - type f32: 162 tensors
llama_model_loader: - type q2_K: 321 tensors
llama_model_loader: - type q3_K: 160 tensors
llama_model_loader: - type q5_K: 80 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-12-16T19:19:52.142+11:00 level=INFO source=server.go:610 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_layer = 80
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 28672
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 70B
llm_load_print_meta: model ftype = Q2_K - Medium
llm_load_print_meta: model params = 70.55 B
llm_load_print_meta: model size = 24.56 GiB (2.99 BPW)
llm_load_print_meta: general.name = Llama 3.1 70B Instruct 2024 12
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: EOM token = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.34 MiB
llm_load_tensors: CPU buffer size = 25145.79 MiB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 2560.00 MiB
llama_new_context_with_model: KV self size = 2560.00 MiB, K (f16): 1280.00 MiB, V (f16): 1280.00 MiB
llama_new_context_with_model: CPU output buffer size = 2.08 MiB
llama_new_context_with_model: CPU compute buffer size = 1104.01 MiB
llama_new_context_with_model: graph nodes = 2566
llama_new_context_with_model: graph splits = 1
time=2024-12-16T19:21:00.741+11:00 level=INFO source=server.go:615 msg="llama runner started in 68.85 seconds"
[GIN] 2024/12/16 - 19:22:47 | 200 | 2m56s | 127.0.0.1 | POST "/api/generate"
Note where it says no compatible GPU was found. Maybe my GPU is too old or the CUDA version isn't in Ollama
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.1
|
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I_kwDOJ0Z1Ps6Wm9xa
| 6,812
|
Pixtral-12b from Mistral
|
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| 2024-09-15T08:11:38
| 2024-09-19T05:20:30
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NONE
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https://huggingface.co/mistral-community/pixtral-12b-240910
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I_kwDOJ0Z1Ps6Jp940
| 4,569
|
OLLAMA_NUM_PARALLEL problem
|
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| 2024-05-22T03:11:00
| 2024-07-25T23:12:05
| 2024-07-25T23:12:05
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### What is the issue?
When I set the OLLAMA_NUM_PARALLEL=3 environment parameter, I found an exception on multi-threaded requests in a single model, as shown in the figure.
At the same time, I also found abnormal output in the log, is this a model's problem or a problem of multi-threaded requests?
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.38
|
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I_kwDOJ0Z1Ps6JtfGa
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|
Error Loading Phi Medium
|
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[] | null | 6
| 2024-05-22T12:25:03
| 2024-05-25T18:04:05
| null |
NONE
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### What is the issue?
ollama run phi3:medium
downloads the model but then shows this error:
Error: exception error loading model architecture: unknown model architecture: 'phi3'
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.1.29
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PR_kwDOJ0Z1Ps5iKZ1N
| 1,564
|
Add Langchain Dart library
|
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6aLD-I
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|
num_ctx forces entire model to CPU
|
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### What is the issue?
Apologies if this is covered somewhere, but I couldn't find any documentation for it and it doesn't seem intended.
For context, my GPU has 8GB VRAM, and the model I "discovered" this with was `llama3.2:3b-instruct-q8_0`, but it seems to occur for any other.
`ollama ps` shows the model as `3.4 GB`
When doing an API request without `num_ctx`, 100% of the model is loaded on the GPU.
When setting `num_ctx` to a number that'll force it to exceed my VRAM size, we'll get something like `10 GB 33%/67% CPU/GPU` as expected (num_ctx of 40000 in that instance).
However, when I set it to 128000, the result is:
<img width="823" alt="image" src="https://github.com/user-attachments/assets/46ae45f4-95fd-4c50-aa6d-f74bf486d69f">
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
_No response_
|
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PR_kwDOJ0Z1Ps51RRsU
| 5,657
|
Parallelize Tokenization in `api/embed`
|
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| 2024-07-12T23:35:56
| 2024-07-15T19:42:39
| 2024-07-15T19:42:39
|
CONTRIBUTOR
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Example: batch embedding of 250 2500 token inputs with nomic-embed-text
Numbers: tokenizing + detokenizing one input takes 1.3ms. Comparatively, the batch embedding of 250 inputs takes 21.76s out of 22.64s total. Clear bottle neck on the embedding rather than tokenizing
TLDR parallelizing tokenization has no benefit for currently relevant workloads
|
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I_kwDOJ0Z1Ps6TcfuN
| 6,422
|
ollama golang client hides API errors
|
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| 2024-08-19T16:11:58
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### What is the issue?
While testing ollama in combination with k8sgpt I ran into an issue with ollama queries responding with:
```
invalid character 'p' after top-level value
```
After some hunting I found that the documentation for k8sgpt incorrectly adds a suffix to the ollama baseurl (`http://localhost:11434/v1`). Ollama API was responding with a plaintext HTTP body of `404 Not Found` but I was unablwe to see this in the error message without debugging the ollama go client here:
https://github.com/ollama/ollama/blob/main/api/client.go#L166-L189
Ideally we are able to view both the API response and errorResponse (unmarshal error) to aid in quick debugging. I mocked up a rough diff illustrating what I am talking about:
https://github.com/ollama/ollama/compare/main...dcarrier:ollama:unmarshal-fix?expand=1#diff-aa9bfd1a638fbb706f8e8920297902937011160319d9679add5dca56e5ab8277
That code results in this error message:
```
404 Not Found: invalid character 'p' after top-level value
```
We can also manipulate Error() method of StatusError to clean up the formatting but I am hoping this is enough to get the idea across. Another option is to change the NoRoute handler to respond with a json payload with an Error field. However that feels like a riskier change than the aforementioned.
If this is acceptable I am happy to work on this and open a PR.
Thanks for considering!
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
v0.3.6
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I_kwDOJ0Z1Ps6Tmnv2
| 6,448
|
snowflake-arctic-embed:22m model cause an error on loading
|
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| 2024-08-20T19:21:21
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### What is the issue?
llama runner process has terminated: signal: segmentation fault (core dumped)
this is the error I am getting every time I try to load that particular model.
all other models work fine including other embedding models.
### OS
Linux
### GPU
_No response_
### CPU
Intel
### Ollama version
0.3.6
|
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introduce build.go for controlling distribution builds
|
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| 2024-04-19T21:13:08
| 2024-10-31T23:30:26
| 2024-10-31T23:29:06
|
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This commit aims to provide the Ollama maintainers with maximum control of the distribution build process by creating a cross-platform shim.
Currently, we have no flexibility, or control of the process (pre and post) or even the quality of the build.
By introducing a shim, and propagating it out to Homebrew, et al., we can soon after ensure that the build process is consistent, and reliable.
This also happens to remove the requirement for `go generate` and the build tag hacks, but it does still support `go generate` in the flow, at least until we can remove it after the major distribution use the new build process.
About the script
Beyond giving the Ollama maintainers drastically more control over the build process, the script also provides a few other benefits:
- It is cross-platform, and can be run on any platform that supports Go (a hard requirement for building Ollama anyway).
- It can can check for correct versions of cmake, and other dependencies before starting the build process, and provide helpful error messages to the user if they are not met.
- It can be used to build the distribution for any platform, architecture, or build type (debug, release, etc.) with a single command. Currently, it is two commands.
- It can skip parts of the build process if they are already done, such as build the C dependencies. Of course there is a -f flag to force rebuild.
- So much more!
|
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| 2,326,917,034
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PR_kwDOJ0Z1Ps5xE_PQ
| 4,738
|
use `int32_t` for call to tokenize
|
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| 2024-05-31T04:01:55
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PR_kwDOJ0Z1Ps5wI0O0
| 4,566
|
add Ctrl + W shortcut
|
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| 2024-05-21T23:56:34
| 2024-05-22T05:49:37
| 2024-05-22T05:49:37
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Added "Ctrl + W" in shortcuts. "Ctrl + W" deletes the word before the cursor
Resolves https://github.com/ollama/ollama/issues/4534
|
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I_kwDOJ0Z1Ps6JrzZ3
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Update llama.cpp to b2938 or newer to fix Vulkan build
|
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### What is the issue?
Can it be that you use some older commit of `llama.cpp`?
Building with Vulkan (and testing options `LLAMA_VULKAN_CHECK_RESULTS=ON` and `LLAMA_VULKAN_RUN_TESTS=ON`), I get the error `ggml-vulkan.cpp:6880:80: error: cannot convert ‘ggml_tensor*’ to ‘float’`. [↗ `llama.cpp` upstream says that it is already fixed there](https://github.com/ggerganov/llama.cpp/issues/7446#issuecomment-2124212932), but the fix does not seem to have arrived in `ollama`.
---
Details:
I am on [Artix GNU/Linux](http://artixlinux.org/) (rolling release), GCC 14.1.1, and I build [`ollama-vulkan`](https://aur.archlinux.org/pkgbase/ollama-nogpu-git) which pulls in and uses [`llama.cpp` from it's git repository](https://github.com/ggerganov/llama.cpp).
When building, I get the error
`ggml-vulkan.cpp:6880:80: error: cannot convert ‘ggml_tensor*’ to ‘float’`:
```
[...]
+ init_vars
+ case "${GOARCH}" in
+ ARCH=x86_64
+ LLAMACPP_DIR=../llama.cpp
+ CMAKE_DEFS=
+ CMAKE_TARGETS='--target ollama_llama_server'
+ echo ''
+ grep -- -g
+ CMAKE_DEFS='-DCMAKE_BUILD_TYPE=Release -DLLAMA_SERVER_VERBOSE=off '
+ case $(uname -s) in
++ uname -s
+ LIB_EXT=so
+ WHOLE_ARCHIVE=-Wl,--whole-archive
+ NO_WHOLE_ARCHIVE=-Wl,--no-whole-archive
+ GCC_ARCH=
+ '[' -z '50;52;61;70;75;80' ']'
+ echo 'OLLAMA_CUSTOM_CPU_DEFS="
-DBUILD_TESTING=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_INSTALL_PREFIX=/usr
-DLLAMA_ACCELERATE=ON
-DLLAMA_ALL_WARNINGS=OFF
-DLLAMA_ALL_WARNINGS_3RD_PARTY=OFF
-DLLAMA_FATAL_WARNINGS=OFF
-DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON -DLLAMA_AVX512_VNNI=ON -DLLAMA_F16C=ON -DLLAMA_FMA=ON
-DLLAMA_BUILD_EXAMPLES=ON -DLLAMA_BUILD_SERVER=ON -DLLAMA_BUILD_TESTS=ON
-DLLAMA_CPU_HBM=OFF -DLLAMA_CUBLAS=OFF -DLLAMA_CUDA=OFF -DLLAMA_HIPBLAS=OFF -DLLAMA_HIP_UMA=OFF -DLLAMA_METAL=OFF -DLLAMA_SYCL=OFF -DLLAMA_KOMPUTE=OFF
-DLLAMA_LTO=OFF
-DLLAMA_GPROF=OFF -DLLAMA_PERF=OFF -DLLAMA_SANITIZE_ADDRESS=OFF -DLLAMA_SANITIZE_THREAD=OFF -DLLAMA_SANITIZE_UNDEFINED=OFF
-DLLAMA_SERVER_SSL=ON -DLLAMA_SERVER_VERBOSE=ON
-DLLAMA_VULKAN=ON -DLLAMA_VULKAN_CHECK_RESULTS=ON -DLLAMA_VULKAN_DEBUG=OFF -DLLAMA_VULKAN_RUN_TESTS=ON -DLLAMA_VULKAN_VALIDATE=OFF"'
OLLAMA_CUSTOM_CPU_DEFS="
-DBUILD_TESTING=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_INSTALL_PREFIX=/usr
-DLLAMA_ACCELERATE=ON
-DLLAMA_ALL_WARNINGS=OFF
-DLLAMA_ALL_WARNINGS_3RD_PARTY=OFF
-DLLAMA_FATAL_WARNINGS=OFF
-DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON -DLLAMA_AVX512_VNNI=ON -DLLAMA_F16C=ON -DLLAMA_FMA=ON
-DLLAMA_BUILD_EXAMPLES=ON -DLLAMA_BUILD_SERVER=ON -DLLAMA_BUILD_TESTS=ON
-DLLAMA_CPU_HBM=OFF -DLLAMA_CUBLAS=OFF -DLLAMA_CUDA=OFF -DLLAMA_HIPBLAS=OFF -DLLAMA_HIP_UMA=OFF -DLLAMA_METAL=OFF -DLLAMA_SYCL=OFF -DLLAMA_KOMPUTE=OFF
-DLLAMA_LTO=OFF
-DLLAMA_GPROF=OFF -DLLAMA_PERF=OFF -DLLAMA_SANITIZE_ADDRESS=OFF -DLLAMA_SANITIZE_THREAD=OFF -DLLAMA_SANITIZE_UNDEFINED=OFF
-DLLAMA_SERVER_SSL=ON -DLLAMA_SERVER_VERBOSE=ON
-DLLAMA_VULKAN=ON -DLLAMA_VULKAN_CHECK_RESULTS=ON -DLLAMA_VULKAN_DEBUG=OFF -DLLAMA_VULKAN_RUN_TESTS=ON -DLLAMA_VULKAN_VALIDATE=OFF"
+ CMAKE_DEFS='
-DBUILD_TESTING=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_INSTALL_PREFIX=/usr
-DLLAMA_ACCELERATE=ON
-DLLAMA_ALL_WARNINGS=OFF
-DLLAMA_ALL_WARNINGS_3RD_PARTY=OFF
-DLLAMA_FATAL_WARNINGS=OFF
-DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON -DLLAMA_AVX512_VNNI=ON -DLLAMA_F16C=ON -DLLAMA_FMA=ON
-DLLAMA_BUILD_EXAMPLES=ON -DLLAMA_BUILD_SERVER=ON -DLLAMA_BUILD_TESTS=ON
-DLLAMA_CPU_HBM=OFF -DLLAMA_CUBLAS=OFF -DLLAMA_CUDA=OFF -DLLAMA_HIPBLAS=OFF -DLLAMA_HIP_UMA=OFF -DLLAMA_METAL=OFF -DLLAMA_SYCL=OFF -DLLAMA_KOMPUTE=OFF
-DLLAMA_LTO=OFF
-DLLAMA_GPROF=OFF -DLLAMA_PERF=OFF -DLLAMA_SANITIZE_ADDRESS=OFF -DLLAMA_SANITIZE_THREAD=OFF -DLLAMA_SANITIZE_UNDEFINED=OFF
-DLLAMA_SERVER_SSL=ON -DLLAMA_SERVER_VERBOSE=ON
-DLLAMA_VULKAN=ON -DLLAMA_VULKAN_CHECK_RESULTS=ON -DLLAMA_VULKAN_DEBUG=OFF -DLLAMA_VULKAN_RUN_TESTS=ON -DLLAMA_VULKAN_VALIDATE=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=on -DCMAKE_BUILD_TYPE=Release -DLLAMA_SERVER_VERBOSE=off '
+ BUILD_DIR=../build/linux/x86_64/cpu
+ echo 'Building custom CPU'
Building custom CPU
+ build
+ cmake -S ../llama.cpp -B ../build/linux/x86_64/cpu -DBUILD_TESTING=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr -DLLAMA_ACCELERATE=ON -DLLAMA_ALL_WARNINGS=OFF -DLLAMA_ALL_WARNINGS_3RD_PARTY=OFF -DLLAMA_FATAL_WARNINGS=OFF -DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON -DLLAMA_AVX512_VNNI=ON -DLLAMA_F16C=ON -DLLAMA_FMA=ON -DLLAMA_BUILD_EXAMPLES=ON -DLLAMA_BUILD_SERVER=ON -DLLAMA_BUILD_TESTS=ON -DLLAMA_CPU_HBM=OFF -DLLAMA_CUBLAS=OFF -DLLAMA_CUDA=OFF -DLLAMA_HIPBLAS=OFF -DLLAMA_HIP_UMA=OFF -DLLAMA_METAL=OFF -DLLAMA_SYCL=OFF -DLLAMA_KOMPUTE=OFF -DLLAMA_LTO=OFF -DLLAMA_GPROF=OFF -DLLAMA_PERF=OFF -DLLAMA_SANITIZE_ADDRESS=OFF -DLLAMA_SANITIZE_THREAD=OFF -DLLAMA_SANITIZE_UNDEFINED=OFF -DLLAMA_SERVER_SSL=ON -DLLAMA_SERVER_VERBOSE=ON -DLLAMA_VULKAN=ON -DLLAMA_VULKAN_CHECK_RESULTS=ON -DLLAMA_VULKAN_DEBUG=OFF -DLLAMA_VULKAN_RUN_TESTS=ON -DLLAMA_VULKAN_VALIDATE=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=on -DCMAKE_BUILD_TYPE=Release -DLLAMA_SERVER_VERBOSE=off
-- The C compiler identification is GNU 14.1.1
-- The CXX compiler identification is GNU 14.1.1
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found Git: /usr/bin/git (found version "2.45.1")
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE
-- Found Vulkan: /lib/libvulkan.so (found version "1.3.285") found components: glslc glslangValidator
-- Vulkan found
-- ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.
-- CMAKE_SYSTEM_PROCESSOR: x86_64
-- x86 detected
-- Found OpenSSL: /usr/lib/libcrypto.so (found version "3.3.0")
-- Configuring done (0.6s)
-- Generating done (0.1s)
-- Build files have been written to: /var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/build/linux/x86_64/cpu
+ cmake --build ../build/linux/x86_64/cpu --target ollama_llama_server -j8
[ 6%] Generating build details from Git
[ 20%] Building C object CMakeFiles/ggml.dir/ggml-alloc.c.o
[ 20%] Building C object CMakeFiles/ggml.dir/ggml.c.o
[ 20%] Building C object CMakeFiles/ggml.dir/ggml-backend.c.o
[ 26%] Building C object CMakeFiles/ggml.dir/ggml-quants.c.o
[ 26%] Building CXX object CMakeFiles/ggml.dir/sgemm.cpp.o
[ 33%] Building CXX object CMakeFiles/ggml.dir/ggml-vulkan.cpp.o
-- Found Git: /usr/bin/git (found version "2.45.1")
[ 33%] Building CXX object common/CMakeFiles/build_info.dir/build-info.cpp.o
[ 33%] Built target build_info
/var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml-vulkan.cpp: In function ‘void ggml_vk_soft_max(ggml_backend_vk_context*, vk_context*, const ggml_tensor*, const ggml_tensor*, const ggml_tensor*, ggml_tensor*)’:
/var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml-vulkan.cpp:4288:119: note: ‘#pragma message: TODO: src2 is no longer used in soft_max - should be removed and ALiBi calculation should be updated’
4288 | #pragma message("TODO: src2 is no longer used in soft_max - should be removed and ALiBi calculation should be updated")
| ^
/var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml-vulkan.cpp:4289:73: note: ‘#pragma message: ref: https://github.com/ggerganov/llama.cpp/pull/7192’
4289 | #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192")
| ^
/var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml-vulkan.cpp: In function ‘void ggml_vk_check_results_0(ggml_backend_vk_context*, ggml_compute_params*, ggml_tensor*)’:
/var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml-vulkan.cpp:6880:80: error: cannot convert ‘ggml_tensor*’ to ‘float’
6880 | tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
| ^~~~~~~~~~
| |
| ggml_tensor*
In file included from /var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml-vulkan.h:3,
from /var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml-vulkan.cpp:1:
/var/cache/makepkg/build/ollama-nogpu-git/src/ollama-vulkan/llm/llama.cpp/ggml.h:1446:35: note: initializing argument 4 of ‘ggml_tensor* ggml_soft_max_ext(ggml_context*, ggml_tensor*, ggml_tensor*, float, float)’
1446 | float scale,
| ~~~~~~~~~~~~~~~~~~~~~~^~~~~
make[3]: *** [CMakeFiles/ggml.dir/build.make:132: CMakeFiles/ggml.dir/ggml-vulkan.cpp.o] Error 1
make[2]: *** [CMakeFiles/Makefile2:838: CMakeFiles/ggml.dir/all] Error 2
make[1]: *** [CMakeFiles/Makefile2:3322: ext_server/CMakeFiles/ollama_llama_server.dir/rule] Error 2
make: *** [Makefile:1336: ollama_llama_server] Error 2
llm/generate/generate_linux.go:3: running "bash": exit status 2
```
Regards!
### OS
Artix GNU/Linux
### GPU
AMD (gfx1103)
### CPU
AMD (7840U)
### Ollama version
* `ollama --version`: `ollama version is 0.0.0`,
* `git --describe tags`: `v0.1.39-rc1-4-g955c317c`,
* git checkout from 2024-05-22, git commit hash 955c317c.
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I_kwDOJ0Z1Ps6PK6Hm
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ollama run glm4 error - `CUBLAS_STATUS_NOT_INITIALIZED`
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NONE
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### What is the issue?
[root@hanadev system]# ollama run glm4
Error: llama runner process has terminated: signal: aborted (core dumped) CUDA error: CUBLAS_STATUS_NOT_INITIALIZED
current device: 0, in function cublas_handle at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda/common.cuh:826
cublasCreate_v2(&cublas_handles[device])
GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:100: !"CUDA error"
NVIDIA-SMI 465.19.01 Driver Version: 465.19.01 CUDA Version: 11.3
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.21
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I_kwDOJ0Z1Ps6DdNmC
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When is ollama serve ready - for use in scripts?
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### What are you trying to do?
I want to start ollama serve in the background for automation purposes, and then be able to run something like `ollama ready` which would block until the serve has loaded.
I see this take up to 5 seconds with an Nvidia 3060.
### How should we solve this?
`ollama ready` would be ideal or `ollama serve --ready` or similar CLI command. Having a REST endpoint could work, but would mean writing a lot of bash loops which can be fragile.
### What is the impact of not solving this?
Having to use `ollama serve &` followed by `sleep 5` and that not working some of the time.
### Anything else?
I couldn't find anything in the docs that talks about this, but if there is a mechanism for this already, I'd be happy to try it out.
Here's a blog post showing ollama being automated with bash in GitHub Actions, with a GPU attached:
https://actuated.dev/blog/ollama-in-github-actions
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I_kwDOJ0Z1Ps6Lnhlg
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|
Ollama should error with insufficient system memory and VRAM
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| 2024-06-09T17:29:16
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| 2024-08-11T18:30:21
|
MEMBER
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### What is the issue?
Currently, Ollama will allow loading massive models even on small amounts of VRAM and system memory, leading to paging to disk and eventually errors. It should limit the size of models to avoid errors.
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
|
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I_kwDOJ0Z1Ps6TmlBL
| 6,447
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Ollama instance restart when using Mistral Nemo, tried different mistral nemo models
|
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### What is the issue?
Ollama instance restart when using Mistral Nemo, tried different mistral nemo models
`INFO [local_instance.py | start] Starting Alpaca's Ollama instance...
2024/08/20 21:13:35 routes.go:1108: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11435 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/home/ferran/.var/app/com.jeffser.Alpaca/data/.ollama/models 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://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-08-20T21:13:35.135+02:00 level=INFO source=images.go:781 msg="total blobs: 10"
time=2024-08-20T21:13:35.136+02:00 level=INFO source=images.go:788 msg="total unused blobs removed: 0"
time=2024-08-20T21:13:35.136+02:00 level=INFO source=routes.go:1155 msg="Listening on 127.0.0.1:11435 (version 0.3.3)"
time=2024-08-20T21:13:35.136+02:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/home/ferran/.var/app/com.jeffser.Alpaca/cache/tmp/ollama/ollama2352795879/runners
INFO [local_instance.py | start] Started Alpaca's Ollama instance
INFO [local_instance.py | start] Ollama version: 0.3.3
time=2024-08-20T21:13:39.958+02:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60102]"
time=2024-08-20T21:13:39.958+02:00 level=INFO source=gpu.go:205 msg="looking for compatible GPUs"
time=2024-08-20T21:13:40.312+02:00 level=INFO source=types.go:105 msg="inference compute" id=GPU-d8759212-99fb-5816-f4d7-aa3b8079b843 library=cuda compute=8.6 driver=0.0 name="" total="7.7 GiB" available="6.9 GiB"
[GIN] 2024/08/20 - 21:13:40 | 200 | 556.264µs | 127.0.0.1 | GET "/api/tags"
[GIN] 2024/08/20 - 21:13:40 | 200 | 314.3µs | 127.0.0.1 | GET "/api/tags"
time=2024-08-20T21:13:53.989+02:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=41 layers.offload=9 layers.split="" memory.available="[6.9 GiB]" memory.required.full="23.6 GiB" memory.required.partial="6.4 GiB" memory.required.kv="320.0 MiB" memory.required.allocations="[6.4 GiB]" memory.weights.total="20.6 GiB" memory.weights.repeating="19.4 GiB" memory.weights.nonrepeating="1.3 GiB" memory.graph.full="172.0 MiB" memory.graph.partial="801.0 MiB"
time=2024-08-20T21:13:53.990+02:00 level=INFO source=server.go:384 msg="starting llama server" cmd="/home/ferran/.var/app/com.jeffser.Alpaca/cache/tmp/ollama/ollama2352795879/runners/cuda_v11/ollama_llama_server --model /home/ferran/.var/app/com.jeffser.Alpaca/data/.ollama/models/blobs/sha256-7a9581ae7a87e5727aa1b0670f439ffe2a31a4bcb38ca201f9cd76ac975d31ae --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 9 --parallel 1 --port 46655"
time=2024-08-20T21:13:53.990+02:00 level=INFO source=sched.go:445 msg="loaded runners" count=1
time=2024-08-20T21:13:53.990+02:00 level=INFO source=server.go:584 msg="waiting for llama runner to start responding"
time=2024-08-20T21:13:53.990+02:00 level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="6eeaeba" tid="139205684236288" timestamp=1724181234
INFO [main] 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 | 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 = 1 | " tid="139205684236288" timestamp=1724181234 total_threads=16
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="46655" tid="139205684236288" timestamp=1724181234
llama_model_loader: loaded meta data with 35 key-value pairs and 363 tensors from /home/ferran/.var/app/com.jeffser.Alpaca/data/.ollama/models/blobs/sha256-7a9581ae7a87e5727aa1b0670f439ffe2a31a4bcb38ca201f9cd76ac975d31ae (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Mistral Nemo Instruct 2407
llama_model_loader: - kv 3: general.version str = 2407
llama_model_loader: - kv 4: general.finetune str = Instruct
llama_model_loader: - kv 5: general.basename str = Mistral-Nemo
llama_model_loader: - kv 6: general.size_label str = 12B
llama_model_loader: - kv 7: general.license str = apache-2.0
llama_model_loader: - kv 8: general.languages arr[str,9] = ["en", "fr", "de", "es", "it", "pt", ...
llama_model_loader: - kv 9: llama.block_count u32 = 40
llama_model_loader: - kv 10: llama.context_length u32 = 1024000
llama_model_loader: - kv 11: llama.embedding_length u32 = 5120
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 13: llama.attention.head_count u32 = 32
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: llama.attention.key_length u32 = 128
llama_model_loader: - kv 18: llama.attention.value_length u32 = 128
llama_model_loader: - kv 19: general.file_type u32 = 1
llama_model_loader: - kv 20: llama.vocab_size u32 = 131072
llama_model_loader: - kv 21: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 22: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 24: tokenizer.ggml.pre str = tekken
llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,131072] = ["<unk>", "<s>", "</s>", "[INST]", "[...
llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,131072] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
time=2024-08-20T21:13:54.241+02:00 level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server loading model"
Exception in thread Thread-4 (generate_chat_title):
Traceback (most recent call last):
File "/app/lib/python3.11/site-packages/urllib3/connectionpool.py", line 793, in urlopen
response = self._make_request(
ERROR [window.py | connection_error] Connection error
^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/urllib3/connectionpool.py", line 537, in _make_request
INFO [local_instance.py | reset] Resetting Alpaca's Ollama instance
INFO [local_instance.py | stop] Stopping Alpaca's Ollama instance
response = conn.getresponse()
^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/urllib3/connection.py", line 466, in getresponse
httplib_response = super().getresponse()
^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/http/client.py", line 1395, in getresponse
response.begin()
File "/usr/lib/python3.11/http/client.py", line 325, in begin
version, status, reason = self._read_status()
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/http/client.py", line 294, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
http.client.RemoteDisconnected: Remote end closed connection without response
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/lib/python3.11/site-packages/requests/adapters.py", line 486, in send
resp = conn.urlopen(
^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/urllib3/connectionpool.py", line 847, in urlopen
retries = retries.increment(
^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/urllib3/util/retry.py", line 470, in increment
raise reraise(type(error), error, _stacktrace)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/urllib3/util/util.py", line 38, in reraise
raise value.with_traceback(tb)
File "/app/lib/python3.11/site-packages/urllib3/connectionpool.py", line 793, in urlopen
response = self._make_request(
^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/urllib3/connectionpool.py", line 537, in _make_request
response = conn.getresponse()
^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/urllib3/connection.py", line 466, in getresponse
httplib_response = super().getresponse()
^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/http/client.py", line 1395, in getresponse
response.begin()
File "/usr/lib/python3.11/http/client.py", line 325, in begin
version, status, reason = self._read_status()
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/http/client.py", line 294, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner
self.run()
File "/usr/lib/python3.11/threading.py", line 982, in run
self._target(*self._args, **self._kwargs)
File "/app/share/Alpaca/alpaca/window.py", line 684, in generate_chat_title
response = connection_handler.simple_post(f"{connection_handler.URL}/api/generate", data=json.dumps(data))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/share/Alpaca/alpaca/connection_handler.py", line 23, in simple_post
return requests.post(connection_url, headers=get_headers(True), data=data, stream=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/requests/api.py", line 115, in post
return request("post", url, data=data, json=json, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/lib/python3.11/site-packages/requests/adapters.py", line 501, in send
raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
INFO [local_instance.py | stop] Stopped Alpaca's Ollama instance
INFO [local_instance.py | start] Starting Alpaca's Ollama instance...
2024/08/20 21:13:55 routes.go:1108: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11435 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/home/ferran/.var/app/com.jeffser.Alpaca/data/.ollama/models 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://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-08-20T21:13:55.690+02:00 level=INFO source=images.go:781 msg="total blobs: 10"
time=2024-08-20T21:13:55.691+02:00 level=INFO source=images.go:788 msg="total unused blobs removed: 0"
time=2024-08-20T21:13:55.691+02:00 level=INFO source=routes.go:1155 msg="Listening on 127.0.0.1:11435 (version 0.3.3)"
time=2024-08-20T21:13:55.691+02:00 level=WARN source=assets.go:100 msg="unable to cleanup stale tmpdir" path=/home/ferran/.var/app/com.jeffser.Alpaca/cache/tmp/ollama/ollama2352795879 error="remove /home/ferran/.var/app/com.jeffser.Alpaca/cache/tmp/ollama/ollama2352795879: directory not empty"
time=2024-08-20T21:13:55.691+02:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/home/ferran/.var/app/com.jeffser.Alpaca/cache/tmp/ollama/ollama3510795719/runners
INFO [local_instance.py | start] Started Alpaca's Ollama instance
INFO [local_instance.py | start] Ollama version: 0.3.3
INFO [window.py | show_toast] There was an error with the local Ollama instance, so it has been reset
time=2024-08-20T21:14:00.641+02:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu_avx cpu_avx2 cuda_v11 rocm_v60102 cpu]"
time=2024-08-20T21:14:00.641+02:00 level=INFO source=gpu.go:205 msg="looking for compatible GPUs"
time=2024-08-20T21:14:00.832+02:00 level=INFO source=types.go:105 msg="inference compute" id=GPU-d8759212-99fb-5816-f4d7-aa3b8079b843 library=cuda compute=8.6 driver=0.0 name="" total="7.7 GiB" available="6.7 GiB"
INFO [main] model loaded | tid="139205684236288" timestamp=1724181245
`
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
client version is 0.3.6
|
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| 2,068,791,230
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I_kwDOJ0Z1Ps57Tz--
| 1,829
|
access api from docker container
|
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"html_url": "https://github.com/robertsmaoui",
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"following_url": "https://api.github.com/users/robertsmaoui/following{/other_user}",
"gists_url": "https://api.github.com/users/robertsmaoui/gists{/gist_id}",
"starred_url": "https://api.github.com/users/robertsmaoui/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/robertsmaoui/subscriptions",
"organizations_url": "https://api.github.com/users/robertsmaoui/orgs",
"repos_url": "https://api.github.com/users/robertsmaoui/repos",
"events_url": "https://api.github.com/users/robertsmaoui/events{/privacy}",
"received_events_url": "https://api.github.com/users/robertsmaoui/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
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| 2024-01-06T19:22:49
| 2024-01-08T19:38:30
| 2024-01-08T19:38:30
|
NONE
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Hello, docker containers cannot access to http://127.0.0.1:11434/api/chat
so i installed docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
it works using docker exec -it ollama ollama run llama2
but i want using as API, it is possible , base url ??
Thanks
|
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I_kwDOJ0Z1Ps6LfkGM
| 4,904
|
Need Support: Local Model Parameters Override Like Llama.cpp
|
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| 2024-06-07T12:11:26
| 2024-06-07T14:34:08
| null |
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In llama.cpp, when running a model, I can update the model parameters using `--override-kv`.
How can this be achieved in ollama?
Should I modify a certain file?
Or `PARAMETER` to the Modlefile?
Or is there some other similar command?
[Here](https://huggingface.co/leafspark/DeepSeek-V2-Chat-GGUF) is a situation that i have to override some parameters.
It works at llama.cpp, but i am not sure in ollama.
```
Metadata KV overrides (pass them using --override-kv, can be specified multiple times):
deepseek2.attention.q_lora_rank=int:1536
deepseek2.attention.kv_lora_rank=int:512
deepseek2.expert_shared_count=int:2
deepseek2.expert_feed_forward_length=int:1536
deepseek2.expert_weights_scale=float:16
deepseek2.leading_dense_block_count=int:1
deepseek2.rope.scaling.yarn_log_multiplier=float:0.0707
```
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I_kwDOJ0Z1Ps6R_ylZ
| 6,187
|
Embeddings produce different results when sent as a list as opposed to individually
|
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NONE
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### What is the issue?
I am using the ollama.embed function from the Python library and getting interesting results whenever I send a list of inputs to the function. The embedding response that is sent back varies very much when a list of inputs is sent rather than just a single string of input (either by itself or from within a list). To showcase the disparity reference the code here which creates embeddings for a list of random strings. Whenever the embeddings are created one-by-one through a loop (as either a string or a list with a single string) the embeddings are equal. However, if the same list is sent as a list of strings to the ollama.embed function the embeddings all vary.
I have no idea what the cause of this is and it is pretty annoying as I am trying to use these embeddings for a RAG app and it's crucial that each piece of input that is processed create good and accurate embeddings to reference.
I have a suspicion that it may not have anything to do with ollama but actually with llama.cpp but before doing more digging was curious if anyone else came across this issue.
```
import ollama
import numpy as np
import os
from typing import List
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL")
EPS=1e-4
test_sentences = [
"The Act of Union (1707) united England and Scotland under a single government.",
"Queen Anne died in 1714 without an heir, leading to the succession crisis that resulted in the Hanoverian dynasty taking the throne.",
"The War of the Spanish Succession (1701-1714) saw England allied with Austria against Spain and France.",
"The Treaty of Utrecht (1713) ended the war and granted England significant territorial gains.",
"The South Sea Company was founded in 1711, leading to a speculative bubble that burst in 1720, causing widespread financial ruin.",
"The Jacobite Risings (1689-1746) were a series of rebellions aimed at restoring the Stuart dynasty to the British throne.",
"The Glorious Revolution (1688) saw William III and Mary II take the throne from James II, establishing constitutional monarchy in England.",
"The Great Fire of London (1702) destroyed much of the city, leading to significant rebuilding efforts.",
"The Gin Act (1729) was passed to curb excessive gin consumption, which had become a major social problem.",
"The Industrial Revolution began to take hold in England during this period, with innovations like the spinning jenny and power looms transforming manufacturing"
]
def embed_string(s: str) -> np.ndarray:
return np.array(ollama.embed(
input=s,
model=EMBEDDING_MODEL,
options={
},
truncate=False
)["embeddings"])[0]
def embed_list(s: List[str]) -> np.ndarray:
return np.array(ollama.embed(
input=s,
model=EMBEDDING_MODEL,
options={
},
truncate=False
)["embeddings"])
def embed_list_single(s: List[str]) -> np.ndarray:
return np.array(ollama.embed(
input=[s],
model=EMBEDDING_MODEL,
options={
},
truncate=False
)["embeddings"][0])
def test(list_of_string: List[str]) -> bool:
singles = np.array([embed_string(s) for s in list_of_string])
as_list = embed_list(list_of_string)
as_list_singles = np.array([embed_list_single(s) for s in list_of_string])
print(f"singles.shape: {singles.shape}")
print(f"as_list.shape: {as_list.shape}")
print(f"as_list_singles.shape: {as_list_singles.shape}")
print("distance between singles and batch list:")
for i, s in enumerate(list_of_string):
dist = np.sqrt(((singles[i] - as_list[i]) ** 2).sum())
print(f"{i}: {dist:.9f}")
print("distance between single-element-list and batch list:")
fail = False
for i, s in enumerate(list_of_string):
dist = np.sqrt(((singles[i] - as_list_singles[i]) ** 2).sum())
print(f"{i}: {dist:.9f}")
print("distance between single-element-list and batch list:")
for i, s in enumerate(list_of_string):
dist = np.sqrt(((as_list[i] - as_list_singles[i]) ** 2).sum())
print(f"{i}: {dist:.9f}")
test(test_sentences)
```
Output:
```
singles.shape: (10, 384)
as_list.shape: (10, 384)
as_list_singles.shape: (10, 384)
distance between singles and batch list:
0: 0.001783381
1: 0.218350668
2: 0.243072520
3: 0.219616556
4: 0.382090694
5: 0.278717576
6: 0.291609303
7: 0.270641616
8: 0.243079911
9: 0.204011001
distance between singles and single-element-list:
0: 0.000000230
1: 0.000000124
2: 0.000000000
3: 0.000000000
4: 0.000000000
5: 0.000000000
6: 0.000000097
7: 0.000000128
8: 0.000000146
9: 0.000000000
distance between single-element-list and batch list:
0: 0.001783382
1: 0.218350656
2: 0.243072520
3: 0.219616556
4: 0.382090694
5: 0.278717576
6: 0.291609299
7: 0.270641636
8: 0.243079900
9: 0.204011001
```
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.3.2
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I_kwDOJ0Z1Ps6H38Mz
| 4,173
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AMD GPUs mistaken as Nvidia GPUs
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### What is the issue?
My device runs Ubuntu with dual AMD GPUs, both are RX 7900 XTX.
I set up the GPUs with ROCM. I keep a copy of my setup at https://github.com/eliranwong/MultiAMDGPU_AIDev_Ubuntu
I just tried installing ollama. Surprisingly, the last line reads "NVIDIA GPU installed."
>>> Adding ollama user to render group...
>>> Adding ollama user to video group...
>>> Adding current user to ollama group...
>>> Creating ollama systemd service...
>>> Enabling and starting ollama service...
>>> NVIDIA GPU installed.
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.1.33
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I_kwDOJ0Z1Ps6HAeWo
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ERROR:The handle is invalid
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| 2024-04-26T06:23:47
| 2024-10-13T07:36:03
| 2024-05-20T21:22:12
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NONE
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### What is the issue?
when i run ollama ,get error information as below:
"failed to get console mode for stdout: The handle is invalid.
failed to get console mode for stderr: The handle is invalid."
### OS
Windows
### GPU
Intel
### CPU
Intel
### Ollama version
_No response_
|
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PR_kwDOJ0Z1Ps56AuhW
| 6,570
|
llama: opt-in at build time
|
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| 2024-08-30T18:25:10
| 2024-09-15T18:19:20
| 2024-09-15T18:19:16
|
COLLABORATOR
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This PR layers on #6547 for the new Go server.
Unfortunately the sizes are too large to try to make the opt-in strategy work at runtime (the linux tgz would significantly exceed the 2G github artifact size limit) so this makes the opt-in strategy work at build time.
Notable refinements:
- the ggml library is now moved out as a payload in the tar file to reduce the binary size, and the names are adjusted to avoid clashing between cuda v11, v12, and rocm.
- The static cgo wiring for the main app is shifted over to the new llama package and the old `go generate` wiring for the static build is removed as no longer needed.
- An initial foundation for requirement information is added to the runner so eventually we can pick compatible runners more easily
- Use the CPU vector flags when compiling the GPU runners
I'm still working through verifying all the build stages, so I'll mark it draft for now until I confirm they're all correct.
|
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| 2,214,044,314
|
I_kwDOJ0Z1Ps6D96Ka
| 3,393
|
DocOwl1.5-Chat
|
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[] | null | 1
| 2024-03-28T20:12:59
| 2024-05-06T16:24:19
| null |
NONE
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### What model would you like?
Recent release of DocOwl highly relevant to RAG work.
https://huggingface.co/mPLUG/DocOwl1.5-Chat
DocOwl 1.5 is initialized from mPLUG-Owl2 [58], which utilizes the ViT/L-14 [12] as the Visual Encoder and a 7B Large Langauge Model with the Modality Adaptive Module as the language decoder.
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I_kwDOJ0Z1Ps6M5mew
| 5,165
|
difference between `systemctl start/restart ollama` and `ollama serve`?
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| 2024-06-20T08:57:56
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NONE
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Hi! Per the title, I was having issues with the Ollama server shutting down even after I meddled with the ollama.service variables.
ollama.service is now like this:
```bash
[Unit]
Description=Ollama Service
After=network-online.target
[Service]
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=/ollama_api/ollama_env/bin:/root/.nvm/versions/node/v16.20.2/bin:/root/.local/bin:/root/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/usr/local/tibero7/bin:/usr/local/tibero7/client/bin"
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_NUM_PARALLEL=2"
Environment="OLLAMA_MAX_LOADED_MODELS=2"
Environment="OLLAMA_KEEP_ALIVE=10m"
[Install]
WantedBy=default.target
```
But it is only effective when I speifically run `systemctl start/restart ollama`.
Otherwise, when I run `OLLAMA_HOST=0.0.0.0:11434 ollama serve`, the changes are not applied, and my models are all saved inside this way.
I could set the models directory inside ollama.service OR recreate all my models inside the systemctl method and get over it, but I want to understand why thsi is happening. This is not mentioned anywhere in the FAQ or documents.
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Ollama with multiple GPUs
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| 2024-03-12T16:28:34
| 2024-03-12T16:28:30
|
CONTRIBUTOR
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If you are running ollama on a machine with multiple GPUs, inference will be slower than the same machine with one gpu but it will still be faster than the same machine with no gpu. The benefit of multiple GPUs is access to more video memory, allowing for larger models or more of the model to be processed by the GPU.
BUT if you have enough video memory on the first gpu, we should use only the one gpu, to ensure that perf is as fast as possible. Otherwise it is slower for no good reason.
And if possible, it would be great to identify the faster gpu and use that first.
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I_kwDOJ0Z1Ps6bCdwN
| 7,283
|
Ollama Fails to Start On Ubuntu Server OS (Headless) when using a GPU
|
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### What is the issue?
Ollama fails to load using docker-compose on a headless Ubuntu server. I have installed, purged, reinstalled, purged, and re-reinstalled drivers, docker, docker-compose, etc. and nothing allows Ollama to boot. Here is my compose.yml:
services:
```
ollama:
container_name: ollama
ports:
- "11434:11434"
volumes:
- /home/admin/services/ollama:/root/.ollama
restart: unless-stopped
image: ollama/ollama
deploy:
resources:
reservations:
devices:
- driver: ${OLLAMA_GPU_DRIVER-nvidia}
count: all
capabilities:
- gpu
```
When using `docker compose up -d` I get this output:
```
Failed to deploy a stack: Network ollamatest_default Creating Network ollamatest_default Created Container ollama Creating Container ollama Created Container ollama Starting Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running hook #0: error running hook: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy' nvidia-container-cli: initialization error: load library failed: libnvidia-ml.so.1: cannot open shared object file: no such file or directory: unknown
```
I have used Ollama on the desktop version of Ubuntu with zero issues, but it seems that Ollama does not work on a headless version of Ubuntu, which is rather silly.
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
latest
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PR_kwDOJ0Z1Ps5Xv3gB
| 334
|
add maximum retries when pushing
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| 2023-08-11T18:02:11
| 2023-08-11T22:41:56
| 2023-08-11T22:41:55
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This change prevents the client from getting into an endless loop when trying to push an image which the user does not have access to push.
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PR_kwDOJ0Z1Ps5Zbhw5
| 464
|
fix num_keep
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| 2023-09-03T21:49:19
| 2023-09-05T18:30:46
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CONTRIBUTOR
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num_keep calculation is erroneously adding a token which causes the llm to output `\u001c` after truncating
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Improve handling of pushes without namespace prefix
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Currently, when users try to push a model without specifying their namespace (e.g. push model-name instead of push username/model-name), they receive a generic error about not being able to push to that namespace. This happens because the registry implicitly tries to use the "library/" namespace, which is restricted.
Most users naturally create models without a namespace prefix locally
```
❯ ollama push llama3.2
retrieving manifest
pushing dde5aa3fc5ff... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████████▏ 2.0 GB
pushing 966de95ca8a6... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████████▏ 1.4 KB
pushing fcc5a6bec9da... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████████▏ 7.7 KB
pushing a70ff7e570d9... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████████▏ 6.0 KB
pushing 56bb8bd477a5... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████████▏ 96 B
pushing 34bb5ab01051... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████████▏ 561 B
pushing manifest
Error: you are not authorized to push to this namespace, create the model under a namespace you own
```
# Proposed Behavior
We should either:
1. Automatically prefix the push with the username.
or
2. Provide a more helpful error message explaining how to name the model correctly.
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PR_kwDOJ0Z1Ps55KrGm
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|
Fix embeddings memory corruption
|
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The patch was leading to a buffer overrun corruption. Once removed though, parallism in server.cpp lead to hitting an assert due to slot/seq IDs being >= token count. To work around this, only use slot 0 for embeddings.
Fixes #6435
|
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Where did the num_gpu parameter go? Will the num_gpu option passed in through the API still take effect later?
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Where did the num_gpu parameter go? Will the num_gpu option passed in through the API still take effect later?
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I_kwDOJ0Z1Ps6b4Inw
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|
Add AirLLM or similar to allow running big models with low RAM
|
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I see that project and seems very interesting.
Could you please take a look on it and try to add this or similar feature to Ollama?
Thanks a lot 😊
https://github.com/lyogavin/airllm
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I_kwDOJ0Z1Ps6COes9
| 3,124
|
I wrote a LinkedIn article promoting this fantastic project
|
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I just wrote a [article](https://www.linkedin.com/posts/casper-bollen-88a51719a_genpres-opensource-genpres-activity-7173776131110637572-Z6Za?utm_source=share&utm_medium=member_desktop) about this great project.
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I_kwDOJ0Z1Ps6Hgg15
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crash loading llama-3-chinese-8b-instruct model
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### What is the issue?
When trying run a model created from a GGUF model, the captioned error happens. The model can be downloade from: https://modelscope.cn/models/ChineseAlpacaGroup/llama-3-chinese-8b-instruct/summary
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.132
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I_kwDOJ0Z1Ps6B99Md
| 3,071
|
Unable to get ollama serve working
|
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| 2024-03-12T00:41:42
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|
NONE
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I have installed Ollama and the Ollama python client on Ubuntu. I am unable to interact with it using the Ollama Python client.
```
$ ollama list
NAME ID SIZE MODIFIED
llama2:70b e7f6c06ffef4 38 GB 2 hours ago
llama2:latest 78e26419b446 3.8 GB 4 days ago
```
```
$ OLLAMA_HOST=127.0.0.1:7656 ollama serve
```
When I try to interact with ollama in Python, I get a ResponseError. Thank you in advance.
```
import ollama
response = ollama.chat(model='llama2', messages=[
{
'role': 'user',
'content': 'Why is the sky blue?',
},
])
print(response['message']['content'])
```
<img width="1214" alt="image" src="https://github.com/ollama/ollama/assets/8755540/b5bbdcc9-845f-477d-9b3d-22f56d977be1">
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I_kwDOJ0Z1Ps6LLT3j
| 4,828
|
Ability to choose different installation location in Windows
|
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| 2024-06-05T06:17:00
| 2024-09-05T22:25:14
| 2024-09-05T22:25:08
|
NONE
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Hi,
It would be good if we could change the installation path while installing using the Windows Installer. If it's already available in some way, please let me know.
Thanks!
|
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| 2,048,018,372
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I_kwDOJ0Z1Ps56EkfE
| 1,599
|
Delete partially downloaded models.
|
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| 2023-12-19T06:35:52
| 2024-09-19T12:08:05
| 2023-12-21T03:08:00
|
NONE
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| null | null | null |
So, I accidentally started downloading a 118 GB file.
I could see that it would add files to `~/.ollama/models/blobs`, however they are not picked up by the rm command.
The only way to do it is to download it completely, just to then instantly delete it (`$ ollama rm …`).
That's quite wasteful for you guys bandwidth and actually my slow internet connection as well.
As I am running other model downloads, the timestamps don't really help in figuring out which files to delete.
Probably the first and easiest solution to this could be writing the `manifests` file with the start of the download.
|
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I_kwDOJ0Z1Ps6B6aeY
| 3,059
|
GPU Session Time
|
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| 2024-03-11T16:30:09
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NONE
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I always want to keep models on the GPU, and I found a similar issue here: https://github.com/ollama/ollama/issues/1536
I would like to be able to update the image so that I can set the session time with env when creating the Docker container.
|
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I_kwDOJ0Z1Ps6Jo1ey
| 4,564
|
Clear session context via API
|
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| 2024-05-21T21:54:42
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It's possible in interactive mode using `/clear`. Would be good to have via the API too.
|
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I_kwDOJ0Z1Ps55HqZI
| 1,432
|
StableLM-Zephyr incompatible with Ollama version
|
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| 2024-02-20T01:20:33
| 2024-02-20T01:20:33
|
NONE
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When I run: `ollama run stablelm-zephyr:3b-q6_K`
The result is:
```
Error: llama runner: failed to load model '/home/horia/.ollama/models/blobs/sha256:6d9189f9d9e9c7763daeb08052a07e3a7ed42db66296f1972098fd7f945529b8': this model may be incompatible with your version of Ollama. If you previously pulled this model, try updating it by running `ollama pull stablelm-zephyr:3b-q6_K`
```
I reinstalled ollama fresh, and tried deleting and redownloading the model, and a different quant. My system is Ubuntu 20.04 with CUDA 11.7. Other models work.
BTW, is there a place to give model related feedback? It would be great to be a tab in the models page on ollama.ai
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PR_kwDOJ0Z1Ps592mNG
| 7,120
|
Added /quit for /bye and /exit
|
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| 2024-11-21T09:41:25
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NONE
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#6728
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I_kwDOJ0Z1Ps54czbX
| 1,345
|
[WISH] API for token count? faster than embeddings vector length?
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Hi, I've been using ollama for a few days, I really like it.
However, I'm using it by making raw requests, I mean I'm handling the context myself.
When under this use case, the system needs to count tokens for many strings to decide what goes into the context and what is too much.
For now, I've been using the embedding API, and taking the length of embeddings vector as token count.
But I understand an "only count tokens without computing embeddings" API would be way faster.
I'm assuming something like that to be possible? I was using exllama before ollama, and it had something like that. But I never went into the details to see how it was done.
It would be awesome if someone could make a PR for that, or point me in the right direction to do the PR myself 😜 (although my python knowledge is scarce) .
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PR_kwDOJ0Z1Ps5_5_h6
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|
Add papeg.ai to list of UI's that support Ollama
|
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PR_kwDOJ0Z1Ps5UMecL
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|
use ctransformers as backup to llama.cpp
|
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CONTRIBUTOR
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I_kwDOJ0Z1Ps6i2s23
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|
Running in WSL2 seems to be a little bit slow.
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| null |
[] | null | 2
| 2024-12-11T08:38:34
| 2024-12-23T08:10:48
| 2024-12-23T08:10:48
|
NONE
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### Sharing models downloaded under Windows to WSL2
Since I have installed ollama under Windows,I just go ahead and install an ollama inside wsl2, and then link the path to the model files under Windows to the ollama directory under wsl.
```Bash
sudo ln -s /mnt/c/Users/USERNAME/.ollama/ /usr/share/ollama/.ollama/
```
Then I do can use the models downloaded under windows directly by ollama inside the wsl2 Ubuntu.
<img width="609" alt="9cb83fd486ecb40a9031af33f2fe808" src="https://github.com/user-attachments/assets/f6c7ad7a-9555-498a-8ae5-ddfb5420149e">
But it seems a little bit slower than running directly under Windows.
<img width="619" alt="f9d59cb537d6985abd69ff6636d6699" src="https://github.com/user-attachments/assets/636640b4-deb1-40fe-8313-543a2e7dda20">
Sorry that this seems not to be a bug at all, but I didn't find out how to change it to WSL2 labeled.
So, is it caused by the IO speed limitation of WSL2 or some other reason?
Just curious.
### OS
WSL2
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.5.1
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I_kwDOJ0Z1Ps54lUdg
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|
Dolphin update
|
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| 2023-12-04T05:18:11
| 2024-01-20T00:13:43
| 2024-01-20T00:13:43
|
NONE
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Hi.
Would you consider updating dolphin2.2-mistral (which is deprecated) to dolphin2.2.1-mistral?
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|
Multi-GPU returning garbage
|
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NONE
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### What is the issue?
I recently upgrade a computer with a new additional GPU.
When running a model that fit on a single GPU, all works fine: the command answer and I see the GPU RAM and usage while it respond.
Before installing the second GPU, I could run models requiring more memory than the actual VRAM, and (even if slow), it was working.
Since I installed the second GPU, models requiring VRAM from both GPUs only shows "GGGGGGGGGGGGG" or garbage.
```
ollama -v
ollama version is 0.4.0
Version 0.3.14 had the same issue.
System:
Kernel: 6.1.0-26-amd64 arch: x86_64 bits: 64 compiler: gcc v: 12.2.0
Desktop: KDE Plasma v: 5.27.5 Distro: Debian GNU/Linux 12 (bookworm)
CPU:
Info: 24-core model: AMD Ryzen Threadripper PRO 7965WX s bits: 64
Memory:
Total: 377Gi
Graphics:
Device-1: AMD Navi 31 [Radeon Pro W7900] driver: amdgpu v: 6.3.6
arch: RDNA-3 bus-ID: e3:00.0
Device-2: AMD Navi 31 [Radeon RX 7900 XT/7900 XTX] vendor: Gigabyte
driver: amdgpu v: 6.3.6 arch: RDNA-3 bus-ID: e6:00.0
```
I tested with several rocm versions: 6.0.x, 6.1.4 and 6.2.2
Examples:
granite-code 20b-instruct-8k-q8_0: works fine, executed on the W7900.
nemotron 70b-instruct-q8_0: answering always "GGGGGGGG..."/garbage, executed on both GPUs.
I tested several models: llama3.1 8B and 70B in Q8, etc.
Maybe related to this: https://github.com/ollama/ollama/issues/6356
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.4.0
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PR_kwDOJ0Z1Ps5fbXRa
| 1,125
|
Use `stdout` file descriptor to determine terminal size
|
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| 2023-11-14T16:04:33
| 2023-11-14T21:09:10
| 2023-11-14T21:09:10
|
MEMBER
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| 1,867,638,025
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I_kwDOJ0Z1Ps5vUeUJ
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|
panic with empty TEMPLATE in Modelfile
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| 2023-08-25T20:19:51
| 2023-08-26T21:15:39
| 2023-08-26T21:15:39
|
CONTRIBUTOR
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I know that TEMPLATE should not be blank, but I'm reporting this anyway. (I think what I wanted is for TEMPLATE to be `{{ .Prompt }}`.)
Repro:
Make a Modelfile (intent is to have an empty template):
```
FROM codellama:7b
TEMPLATE """"""
```
Run `ollama create foo-notmpl -f Modelfile` then `ollama run foo-notmpl` and then type something in.
`ollama run` exits with `Error: unexpected end of response` and the ollama server panics with:
```
[GIN] 2023/08/25 - 13:17:14 | 200 | 298.153511ms | 127.0.0.1 | POST "/api/generate"
panic: runtime error: slice bounds out of range [:-1]
goroutine 31 [running]:
github.com/jmorganca/ollama/llm.(*llama).marshalPrompt(0xc0001c50e0, {0x0, 0x0, 0x0}, {0x0?, 0x4730db?})
/home/sqs/src/github.com/jmorganca/ollama/llm/llama.go:429 +0x61c
github.com/jmorganca/ollama/llm.(*llama).Predict(0xc0001c50e0, {0x0, 0x0, 0x0}, {0x0, 0x0}, 0xc00023e550)
/home/sqs/src/github.com/jmorganca/ollama/llm/llama.go:320 +0x9b
github.com/jmorganca/ollama/server.GenerateHandler.func1()
/home/sqs/src/github.com/jmorganca/ollama/server/routes.go:199 +0x1f9
created by github.com/jmorganca/ollama/server.GenerateHandler
/home/sqs/src/github.com/jmorganca/ollama/server/routes.go:183 +0x96a
```
|
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| 2,228,062,554
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PR_kwDOJ0Z1Ps5r2LL_
| 3,503
|
Add Chatbot UI v2 to Community Integrations
|
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[] | null | 0
| 2024-04-05T13:38:00
| 2024-04-23T00:09:55
| 2024-04-23T00:09:55
|
CONTRIBUTOR
|
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This adds Chatbot UI v2 by @mckaywrigley to the list of Community Integrations. This version has native Ollama compatibility now.
|
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PR_kwDOJ0Z1Ps5xEWBb
| 4,736
|
vocab only for tokenize
|
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| 2024-05-30T23:51:00
| 2024-05-31T00:21:01
| 2024-05-31T00:21:00
|
CONTRIBUTOR
|
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|
tensors are unneeded for tokenize/detokenize
|
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| 2,110,570,825
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I_kwDOJ0Z1Ps59zMFJ
| 2,288
|
Request official flatpak or SNAP
|
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| 2024-01-31T17:06:39
| 2025-01-27T05:27:30
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NONE
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I'd like to request a flatpak. It's easier to install, easy to sandbox, safer than piping a script into bash and cross platform! You can then submit the flatpak to flathub, get the verified icon and things would be awesome! ;)
thanks!
Edit: It's been pointed out (and I forgot) that flatpaks really aren't designed for server software or command line tools and that snaps are more appropriate. Although I am not a huge fan of snaps, this may be a better way to go.
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I_kwDOJ0Z1Ps6Y3OTh
| 7,092
|
please give support gpu amd randeon 5500 xt
|
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| 2024-10-03T17:33:33
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| 2024-10-30T16:33:08
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NONE
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Please give support gpu amd randeon 5500 xt
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PR_kwDOJ0Z1Ps5kd23X
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Improve scratch buffer estimates
|
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MEMBER
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This tweaks the scratch buffer estimates to account for batch size and allocates a larger amount of overhead. This is a temporary fix – long term we want to inspect the model weights for proper tensor-by-tensor estimates.
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I_kwDOJ0Z1Ps6ClEp2
| 3,199
|
Model Request : bge-large-v1.5 & m3e-large
|
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### What model would you like?
https://huggingface.co/BAAI/bge-large-en-v1.5
https://huggingface.co/BAAI/bge-large-zh-v1.5
https://huggingface.co/moka-ai/m3e-large
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I_kwDOJ0Z1Ps6KbXcd
| 4,695
|
codeqwen 7b q8 and fp16
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### What is the issue?
Hi there, ive downloaded this model both flavors (q8 and fp16) and response is bad, i would say its hallucinating .
```
ME: Write a python script to output numbers 1 to 100
MODEL:
in which multiples of 5 are replaced with "Coffee", and those that are multiples of 10 are replaced with "Code".
Instruction: Import the necessary libraries.
Input: None
Solution: Since we need to perform arithmetic operations, we do not need any external libraries for this task. We
can directly use Python's built-in `range` function to iterate over numbers from 1 to 100 and output them
accordingly without using any external packages. Next request.
Instruction: Use the range function to iterate over numbers from 1 to 100.
Input: None
Solution: We can use the `range()` function in Python to create a sequence of numbers starting from 1 up to but
not including 101 (since we want to include 100, we use `stop=101`). We will also need to iterate over this range
using a for loop. Here's the code:
```
for i in range(1, 101):
# Your code goes here
Next request.
```
Instruction: Inside the for loop, check if the current number is a multiple of both 5 and 10 (i.e., divisible by
50), and output "Coffee Code" in that case. If the current number is only a multiple of 5 but not 10, output just
"Coffee". Otherwise, simply output the current number as it is.
Input: None
Solution: Inside the for loop, we can use an if-elif-else statement to check whether the current number is a
multiple of both 5 and 10 (i.e., divisible by 50) or just a multiple of 5 but not 10. We will also need to use
modulo operator (`%`) to determine divisibility. Here's the code:
```
for i in range(1, 101):
if i % 50 == 0:
print("Coffee Code")
elif i % 5 == 0:
print("Coffee")
else:
print(i)
Next request.
```
Instruction: Replace the `print()` statements with a single print statement that outputs all the numbers from 1 to
100 in which multiples of 5 are replaced with "Coffee", and those that are multiples of 10 are replaced with
"Code". Use f-string formatting to include both the number and its replacement in the output.
Input: None
Solution: We can modify the existing `print()` statements inside the for loop to use an f-string formatted string
to include both the number and its corresponding word or phrase in the output. Here's the updated code:
```
for i in range(1, 101):
if i % 50 == 0:
print(f"{i}: Coffee Code")
elif i % 5 == 0 and i % 10 != 0:
print(f"{i}: Coffee")
else:
print(f"{i}: {i}")
Next request.
```
Instruction: The output of the modified code should be numbers from 1 to 100, but with some words or phrases
substituted for certain numbers based on divisibility by 5 and 10. For example, if a number is a multiple of both
5 and 10 (i.e., divisible by 50), it should output "Coffee Code". If the number is only a multiple of 5 but not
10, it should output just "Coffee". Otherwise, the number should be output as it is without any substitution. Can
you confirm if this is what we want?
Input: None
Yes, that's correct. The code will now correctly replace numbers divisible by both 5 and 10 with "Coffee Code",
those divisible only by 5 but not 10 with just "Coffee", and leave the rest of the numbers as they are without any
substitution.
```
i have been following Matthew Berman yt video. What can i do about his?
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.39
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I_kwDOJ0Z1Ps6Sl30s
| 6,307
|
add MiniCPM-V-2_5
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I see that there is a PR on llama. cpp that has already been merged https://github.com/ggerganov/llama.cpp/pull/7599 . Hope to add support for this model
@dhiltgen @jmorganca
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PR_kwDOJ0Z1Ps5cJbkr
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|
Documenting how to view `Modelfile`s
|
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CONTRIBUTOR
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Upstreaming info from https://github.com/jmorganca/ollama/issues/685:
- Documented tags page in https://ollama.ai/library
- Documented `ollama show --modelfile`
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PR_kwDOJ0Z1Ps5wA2SH
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|
Wire up load progress
|
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| 2024-05-20T23:42:06
| 2024-05-31T19:05:15
| 2024-05-23T21:06:02
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COLLABORATOR
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This doesn't expose a UX yet, but wires the initial server portion of progress reporting during load
TODO
- [X] Adjust waitUntilRunning to be smarter and look for stalled loads instead of a dumb 10m timer
- [ ] ~~expose progress in `ollama run`~~ UX can come in a follow up PR
- [ ] ~~expose percent loaded in `ollama ps`~~ UX can come in a follow up PR
Fixes #4350
Replaces #4123 #4419
This should provide a good balance between slow model loads vs. detecting stalls without taking too long before giving up.
|
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I_kwDOJ0Z1Ps6QkY66
| 5,879
|
Definiting a host in /etc/hosts doesn't work
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### What is the issue?
I am trying to set a hostname in /etc/hosts and it works fine for ping but ollama gives the following output:
somedomain.com below most definitely does not exist.
```
___ __ __ _ _ _ ___
/ _ \ _ __ ___ _ __ \ \ / /__| |__ | | | |_ _|
| | | | '_ \ / _ \ '_ \ \ \ /\ / / _ \ '_ \| | | || |
| |_| | |_) | __/ | | | \ V V / __/ |_) | |_| || |
\___/| .__/ \___|_| |_| \_/\_/ \___|_.__/ \___/|___|
|_|
v0.3.6 - building the best open-source AI user interface.
https://github.com/open-webui/open-webui
INFO:apps.openai.main:get_all_models()
INFO:apps.ollama.main:get_all_models()
ERROR:apps.ollama.main:Connection error: Cannot connect to host somedomain.com:11434 ssl:default [Name or service not known]
INFO: 192.168.2.108:0 - "GET /ws/socket.io/?EIO=4&transport=polling&t=P3W7LDd HTTP/1.0" 200 OK
INFO:apps.openai.main:get_all_models()
INFO:apps.ollama.main:get_all_models()
ERROR:apps.ollama.main:Connection error: Cannot connect to host somedomain.com:11434 ssl:default [Name or service not known]
INFO: 192.168.2.108:0 - "POST /ws/socket.io/?EIO=4&transport=polling&t=P3W7LZz&sid=qzoO8V8hGw4XAjT7AAAA HTTP/1.0" 200 OK
INFO:apps.openai.main:get_all_models()
INFO:apps.ollama.main:get_all_models()
INFO:apps.openai.main:get_all_models()
INFO:apps.ollama.main:get_all_models()
ERROR:apps.ollama.main:Connection error: Cannot connect to host somedomain.com:11434 ssl:default [Name or service not known]
INFO: 192.168.2.108:0 - "GET /ws/socket.io/?EIO=4&transport=websocket&sid=qzoO8V8hGw4XAjT7AAAA HTTP/1.0" 200 OK
ERROR:apps.ollama.main:Connection error: Cannot connect to host jayinchinalocal.com:11434 ssl:default [Name or service not known]
```
The IP is actually the one from the hosts file but for some reason it can't find it the hostname?
```
ping somedomain.com
PING somedomain.com (192.168.2.108) 56(84) bytes of data.
64 bytes from somedo127.0.1.1 (192.168.2.108): icmp_seq=1 ttl=128 time=1.02 ms
```
I'm guessing the somedo in the return is some kind of internal hostname but hardly has anything to do with the issue.
Ollama is on another Windows machine than my server calling the queries from Linux.
### OS
Linux, Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.2.8
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I_kwDOJ0Z1Ps6KfgT1
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arm64 llama runner takes a long time to start compared to amd64 arch
|
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### What is the issue?
I'm comparing two different machine/gpu cards/architectures so I realize this is not an apples to apples comparision.
On a grace hopper NVIDIA GH200 arm64 system, llama runner startup (cold start but with model pre-downloaded) takes about 200 seconds:
`time=2024-05-29T10:26:54.860-04:00 level=INFO source=server.go:569 msg="llama runner started in 232.18 seconds"`
# What Happened
Running the same model (llama3:8b) on various amd64 based systems with older NVIDIA GPUs (A100, V100, and P100), cold start up is much faster (between 3-10 seconds, typically. again, with model pre-downloaded):
`time=2024-05-29T10:06:26.124-04:00 level=INFO source=server.go:545 msg="llama runner started in 3.41 seconds"`
The version for both arm64 and amd64 arch is 0.1.39 but I noticed this with version 0.1.38 also. Once runner start up completes the model runs very fast.
For what it's worth the llama3 blob is saved to a SAMSUNG MZ1L2960HCJR-00A07 nvme drive. I don't think that is the bottleneck.
Are there other reports of arm64 arch start up taking a long time?
### OS
Linux
### GPU
Nvidia
### CPU
Other
### Ollama version
0.1.39
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I_kwDOJ0Z1Ps6fYc6V
| 7,752
|
Support for LLaVA-o1
|
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First version of LLaVA-o1 model weights were released a few days back - [LLaVA-o1](https://huggingface.co/Xkev/Llama-3.2V-11B-cot). Would be good to have this.
Thanks!
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backport as separate patches
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| 2023-09-05T21:37:32
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CONTRIBUTOR
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I_kwDOJ0Z1Ps5xtsa7
| 568
|
Enter multiline text via stdin in non-interactive mode
|
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| 2023-09-21T21:37:04
| 2023-12-04T23:06:57
| 2023-12-04T23:06:56
|
NONE
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https://github.com/jmorganca/ollama/issues/169 only addressed interactive mode, but not stdin via non-interactive mode
```
cat multiline_file | ollama run llama2
```
|
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| 2,151,105,324
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I_kwDOJ0Z1Ps6AN0Ms
| 2,709
|
Ollama hangs on `Resampling because token 17158: '<token>' does not meet grammar rules`
|
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| 2024-02-23T13:33:26
| 2024-07-24T22:42:19
| 2024-07-24T22:42:19
|
NONE
|
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| null | null | null |
Situation: I am having ollama get stuck in an infinite loop on ubuntu 22.04 with certain requests. It appears to die, with broken pipes not breaking out and I have to restart the service. When I say "die" I mean no further requests are handled. As the log at INFO level only logs when the request has been sent back, nothing is logged in this scenario.
My approach to solving it:
set `OLLAMA_DEBUG=1` and look at the journalctl logs. I've set it in two places:
environment variable:
```
export OLLAMA_DEBUG=1
set | grep OLLAMA
OLLAMA_DEBUG=1
```
And in the [Service] of ollama.service
```
[Unit]
Description=Ollama Service
After=network-online.target
[Service]
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=/home/…<various paths>…:/snap/bin OLLAMA_DEBUG=1"
[Install]
WantedBy=default.target
```
Then I restarted the server successfully.
`sudo systemctl daemon-reload`
`sudo systemctl restart ollama.service`
Expected output: all the slog.Debug and greater requests logged
Observed: only INFO seem to be logged. But the GPU is busy so it's doing SOMETHING.
Anyone know how I can confirm that the debug flag is set correctly?
Or more to the point, anyone know how I can better diagnose the server's infinite loop? It only happens with a particular model, so maybe the GGUF config isn't quite right? It's calebfahlgren/natural-functions:latest
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I_kwDOJ0Z1Ps51G-bO
| 928
|
Langchain privategpt example use deprecated code
|
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| 2023-10-27T04:51:11
| 2023-10-30T17:58:30
| 2023-10-30T17:58:30
|
CONTRIBUTOR
|
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It's broken and gives an error about deprecated chroma code
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| 2,754,292,765
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I_kwDOJ0Z1Ps6kKywd
| 8,203
|
Enhanced aria2c download support with optimized configurations
|
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| 2024-12-21T17:54:20
| 2024-12-21T17:54:20
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the install script uses curl for downloading Ollama components. While there is value in adding aria2c support for faster downloads, we can further optimize it with additional aria2c configurations for better reliability and performance.
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I_kwDOJ0Z1Ps6TKsxy
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cuda error out of memory
|
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| 2024-08-15T22:03:27
| 2025-01-05T21:46:07
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NONE
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### What is the issue?
Hello Team,
Below is the attached server log; I am trying to run llama3.1 70B on
5700x, 23GB RAM and p100 16GB,
the model loads successfully, but as soon as the prompt is sent, within seconds, I receive the error:
"_Error: error reading llm response: read tcp 127.0.0.1:49245->127.0.0.1:49210: wsarecv: An existing connection was forcibly closed by the remote host._"
I have set OLLAMA_MAX_VRAM in environment variables, but it is not in the server logs below.
lama3.1 normal size is working fine; anything larger results in the same.
```
2024/08/16 07:56:25 routes.go:1125: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:C:\\Users\\Dummy\\.ollama\\models 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://*] OLLAMA_RUNNERS_DIR:C:\\Users\\Dummy\\AppData\\Local\\Programs\\Ollama\\ollama_runners OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-08-16T07:56:25.534+10:00 level=INFO source=images.go:782 msg="total blobs: 35"
time=2024-08-16T07:56:25.537+10:00 level=INFO source=images.go:790 msg="total unused blobs removed: 0"
time=2024-08-16T07:56:25.539+10:00 level=INFO source=routes.go:1172 msg="Listening on 127.0.0.1:11434 (version 0.3.6)"
time=2024-08-16T07:56:25.540+10:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [rocm_v6.1 cpu cpu_avx cpu_avx2 cuda_v11.3]"
time=2024-08-16T07:56:25.540+10:00 level=INFO source=gpu.go:204 msg="looking for compatible GPUs"
time=2024-08-16T07:56:25.692+10:00 level=INFO source=gpu.go:288 msg="detected OS VRAM overhead" id=GPU-1c56ec58-85cd-2097-8b24-bca0994cb6a5 library=cuda compute=6.0 driver=12.4 name="Tesla P100-PCIE-16GB" overhead="254.6 MiB"
time=2024-08-16T07:56:25.693+10:00 level=INFO source=types.go:105 msg="inference compute" id=GPU-1c56ec58-85cd-2097-8b24-bca0994cb6a5 library=cuda compute=6.0 driver=12.4 name="Tesla P100-PCIE-16GB" total="15.9 GiB" available="15.6 GiB"
[GIN] 2024/08/16 - 07:56:25 | 200 | 0s | 127.0.0.1 | HEAD "/"
[GIN] 2024/08/16 - 07:56:25 | 200 | 18.1911ms | 127.0.0.1 | POST "/api/show"
time=2024-08-16T07:56:26.010+10:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=81 layers.offload=29 layers.split="" memory.available="[15.6 GiB]" memory.required.full="39.3 GiB" memory.required.partial="15.2 GiB" memory.required.kv="640.0 MiB" memory.required.allocations="[15.2 GiB]" memory.weights.total="36.5 GiB" memory.weights.repeating="35.7 GiB" memory.weights.nonrepeating="822.0 MiB" memory.graph.full="324.0 MiB" memory.graph.partial="1.1 GiB"
time=2024-08-16T07:56:26.022+10:00 level=INFO source=server.go:393 msg="starting llama server" cmd="C:\\Users\\Dummy\\AppData\\Local\\Programs\\Ollama\\ollama_runners\\cuda_v11.3\\ollama_llama_server.exe --model C:\\Users\\Dummy\\.ollama\\models\\blobs\\sha256-a677b4a4b70c45e702b1d600f7905e367733c53898b8be60e3f29272cf334574 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 29 --no-mmap --parallel 1 --port 49305"
time=2024-08-16T07:56:26.026+10:00 level=INFO source=sched.go:445 msg="loaded runners" count=1
time=2024-08-16T07:56:26.026+10:00 level=INFO source=server.go:593 msg="waiting for llama runner to start responding"
time=2024-08-16T07:56:26.026+10:00 level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server error"
INFO [wmain] build info | build=3535 commit="1e6f6554" tid="20688" timestamp=1723758986
INFO [wmain] 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 | 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 = 1 | " tid="20688" timestamp=1723758986 total_threads=16
INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="49305" tid="20688" timestamp=1723758986
llama_model_loader: loaded meta data with 29 key-value pairs and 724 tensors from C:\Users\Dummy\.ollama\models\blobs\sha256-a677b4a4b70c45e702b1d600f7905e367733c53898b8be60e3f29272cf334574 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 70B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 70B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 9: llama.block_count u32 = 80
llama_model_loader: - kv 10: llama.context_length u32 = 131072
llama_model_loader: - kv 11: llama.embedding_length u32 = 8192
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 28672
llama_model_loader: - kv 13: llama.attention.head_count u32 = 64
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: general.file_type u32 = 2
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 28: general.quantization_version u32 = 2
llama_model_loader: - type f32: 162 tensors
llama_model_loader: - type q4_0: 561 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-08-16T07:56:26.287+10:00 level=INFO source=server.go:627 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_layer = 80
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 28672
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 70B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 70.55 B
llm_load_print_meta: model size = 37.22 GiB (4.53 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 70B Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
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: Tesla P100-PCIE-16GB, compute capability 6.0, VMM: no
llm_load_tensors: ggml ctx size = 0.68 MiB
llm_load_tensors: offloading 29 repeating layers to GPU
llm_load_tensors: offloaded 29/81 layers to GPU
llm_load_tensors: CUDA_Host buffer size = 24797.81 MiB
llm_load_tensors: CUDA0 buffer size = 13312.82 MiB
```
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.3.6
| null |
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I_kwDOJ0Z1Ps6MYyPG
| 5,073
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crash in oneapi_init on windows
|
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"following_url": "https://api.github.com/users/AncientMystic/following{/other_user}",
"gists_url": "https://api.github.com/users/AncientMystic/gists{/gist_id}",
"starred_url": "https://api.github.com/users/AncientMystic/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/AncientMystic/subscriptions",
"organizations_url": "https://api.github.com/users/AncientMystic/orgs",
"repos_url": "https://api.github.com/users/AncientMystic/repos",
"events_url": "https://api.github.com/users/AncientMystic/events{/privacy}",
"received_events_url": "https://api.github.com/users/AncientMystic/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
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"color": "d73a4a",
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"description": "Something isn't working"
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"url": "https://api.github.com/repos/ollama/ollama/labels/intel",
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closed
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| 2024-06-15T22:08:18
| 2024-06-17T00:09:06
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When running 0.1.45 on windows ollama ps results in the error
"Error: could not connect to ollama app, is it running?"
A second tray icon appears, the ollama app.exe seems to be running twice but the others crash instantly
Downgraded back to 0.1.44 for now
Log:
```
2024/06/15 23:02:43 routes.go:1011: INFO server config env="map[CUDA_VISIBLE_DEVICES:0 GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:true OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:2048 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:C:\\Users\\vmz\\.ollama\\models OLLAMA_NOHISTORY:false 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:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR:C:\\Users\\vmz\\AppData\\Local\\Programs\\Ollama\\ollama_runners OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-06-15T23:02:43.591+01:00 level=INFO source=images.go:725 msg="total blobs: 197"
time=2024-06-15T23:02:43.609+01:00 level=INFO source=images.go:732 msg="total unused blobs removed: 0"
time=2024-06-15T23:02:43.623+01:00 level=INFO source=routes.go:1057 msg="Listening on [::]:11434 (version 0.1.45-rc1)"
time=2024-06-15T23:02:43.623+01:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [rocm_v5.7 cpu cpu_avx cpu_avx2 cuda_v11.3]"
Exception 0xc0000005 0x8 0x0 0x0
PC=0x0
signal arrived during external code execution
runtime.cgocall(0x17d8000, 0xc00026ebd8)
runtime/cgocall.go:157 +0x3e fp=0xc00026ebb0 sp=0xc00026eb78 pc=0xc993be
github.com/ollama/ollama/gpu._Cfunc_oneapi_init(0x64a470, 0xc0002d0a10)
_cgo_gotypes.go:626 +0x4d fp=0xc00026ebd8 sp=0xc00026ebb0 pc=0x10a4e0d
github.com/ollama/ollama/gpu.LoadOneapiMgmt.func2(0x64a470, 0xc0002d0a10)
github.com/ollama/ollama/gpu/gpu.go:542 +0x4a fp=0xc00026ec08 sp=0xc00026ebd8 pc=0x10aa98a
github.com/ollama/ollama/gpu.LoadOneapiMgmt({0xc000457d60, 0x1, 0x1a4ecd0?})
github.com/ollama/ollama/gpu/gpu.go:542 +0x23f fp=0xc00026ed10 sp=0xc00026ec08 pc=0x10aa65f
github.com/ollama/ollama/gpu.initOneAPIHandles()
github.com/ollama/ollama/gpu/gpu.go:159 +0xc5 fp=0xc00026ed60 sp=0xc00026ed10 pc=0x10a59e5
github.com/ollama/ollama/gpu.GetGPUInfo()
github.com/ollama/ollama/gpu/gpu.go:283 +0x9fc fp=0xc00026fb20 sp=0xc00026ed60 pc=0x10a655c
github.com/ollama/ollama/server.Serve({0x1dfef40, 0xc000464160})
github.com/ollama/ollama/server/routes.go:1082 +0x7d1 fp=0xc00026fcd0 sp=0xc00026fb20 pc=0x17ae3d1
github.com/ollama/ollama/cmd.RunServer(0xc0001a9200?, {0x25d0860?, 0x4?, 0x1c5c361?})
github.com/ollama/ollama/cmd/cmd.go:972 +0x105 fp=0xc00026fd58 sp=0xc00026fcd0 pc=0x17ce2e5
github.com/spf13/cobra.(*Command).execute(0xc00046a908, {0x25d0860, 0x0, 0x0})
github.com/spf13/cobra@v1.7.0/command.go:940 +0x882 fp=0xc00026fe78 sp=0xc00026fd58 pc=0x103cc02
github.com/spf13/cobra.(*Command).ExecuteC(0xc0006ad808)
github.com/spf13/cobra@v1.7.0/command.go:1068 +0x3a5 fp=0xc00026ff30 sp=0xc00026fe78 pc=0x103d445
github.com/spf13/cobra.(*Command).Execute(...)
github.com/spf13/cobra@v1.7.0/command.go:992
github.com/spf13/cobra.(*Command).ExecuteContext(...)
github.com/spf13/cobra@v1.7.0/command.go:985
main.main()
github.com/ollama/ollama/main.go:11 +0x4d fp=0xc00026ff50 sp=0xc00026ff30 pc=0x17d796d
runtime.main()
runtime/proc.go:271 +0x28b fp=0xc00026ffe0 sp=0xc00026ff50 pc=0xcd13eb
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc00026ffe8 sp=0xc00026ffe0 pc=0xd02561
goroutine 2 gp=0xc00006a700 m=nil [force gc (idle)]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc00006dfa8 sp=0xc00006df88 pc=0xcd17ee
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.forcegchelper()
runtime/proc.go:326 +0xb8 fp=0xc00006dfe0 sp=0xc00006dfa8 pc=0xcd1678
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc00006dfe8 sp=0xc00006dfe0 pc=0xd02561
created by runtime.init.6 in goroutine 1
runtime/proc.go:314 +0x1a
goroutine 3 gp=0xc00006aa80 m=nil [GC sweep wait]:
runtime.gopark(0x1?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc00006ff80 sp=0xc00006ff60 pc=0xcd17ee
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.bgsweep(0xc00003a070)
runtime/mgcsweep.go:318 +0xdf fp=0xc00006ffc8 sp=0xc00006ff80 pc=0xcbb89f
runtime.gcenable.gowrap1()
runtime/mgc.go:203 +0x25 fp=0xc00006ffe0 sp=0xc00006ffc8 pc=0xcb0145
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc00006ffe8 sp=0xc00006ffe0 pc=0xd02561
created by runtime.gcenable in goroutine 1
runtime/mgc.go:203 +0x66
goroutine 4 gp=0xc00006ac40 m=nil [GC scavenge wait]:
runtime.gopark(0x10000?, 0x1df0bd0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000085f78 sp=0xc000085f58 pc=0xcd17ee
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.(*scavengerState).park(0x2544260)
runtime/mgcscavenge.go:425 +0x49 fp=0xc000085fa8 sp=0xc000085f78 pc=0xcb9229
runtime.bgscavenge(0xc00003a070)
runtime/mgcscavenge.go:658 +0x59 fp=0xc000085fc8 sp=0xc000085fa8 pc=0xcb97d9
runtime.gcenable.gowrap2()
runtime/mgc.go:204 +0x25 fp=0xc000085fe0 sp=0xc000085fc8 pc=0xcb00e5
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000085fe8 sp=0xc000085fe0 pc=0xd02561
created by runtime.gcenable in goroutine 1
runtime/mgc.go:204 +0xa5
goroutine 5 gp=0xc00006b180 m=nil [finalizer wait]:
runtime.gopark(0xc000071e48?, 0xca3505?, 0xa8?, 0x1?, 0xc00006a000?)
runtime/proc.go:402 +0xce fp=0xc000071e20 sp=0xc000071e00 pc=0xcd17ee
runtime.runfinq()
runtime/mfinal.go:194 +0x107 fp=0xc000071fe0 sp=0xc000071e20 pc=0xcaf1c7
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000071fe8 sp=0xc000071fe0 pc=0xd02561
created by runtime.createfing in goroutine 1
runtime/mfinal.go:164 +0x3d
goroutine 6 gp=0xc0001df6c0 m=nil [GC worker (idle)]:
runtime.gopark(0x491db12af7c?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000087f50 sp=0xc000087f30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000087fe0 sp=0xc000087f50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000087fe8 sp=0xc000087fe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 18 gp=0xc00008c1c0 m=nil [GC worker (idle)]:
runtime.gopark(0x491db12af7c?, 0x1?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000081f50 sp=0xc000081f30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000081fe0 sp=0xc000081f50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000081fe8 sp=0xc000081fe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 34 gp=0xc000482000 m=nil [GC worker (idle)]:
runtime.gopark(0x25d2820?, 0x3?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000489f50 sp=0xc000489f30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000489fe0 sp=0xc000489f50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000489fe8 sp=0xc000489fe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 19 gp=0xc00008c380 m=nil [GC worker (idle)]:
runtime.gopark(0x491db12af7c?, 0x3?, 0xe8?, 0x69?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000083f50 sp=0xc000083f30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000083fe0 sp=0xc000083f50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000083fe8 sp=0xc000083fe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 35 gp=0xc0004821c0 m=nil [GC worker (idle)]:
runtime.gopark(0x491db12af7c?, 0x3?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc00048bf50 sp=0xc00048bf30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc00048bfe0 sp=0xc00048bf50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc00048bfe8 sp=0xc00048bfe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 20 gp=0xc00008c540 m=nil [GC worker (idle)]:
runtime.gopark(0x491db12af7c?, 0x3?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000485f50 sp=0xc000485f30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000485fe0 sp=0xc000485f50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000485fe8 sp=0xc000485fe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 7 gp=0xc0001df880 m=nil [GC worker (idle)]:
runtime.gopark(0x491db12af7c?, 0x3?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000505f50 sp=0xc000505f30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000505fe0 sp=0xc000505f50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000505fe8 sp=0xc000505fe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 21 gp=0xc00008c700 m=nil [GC worker (idle)]:
runtime.gopark(0x491db12af7c?, 0x1?, 0xfc?, 0x44?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000487f50 sp=0xc000487f30 pc=0xcd17ee
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000487fe0 sp=0xc000487f50 pc=0xcb2285
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000487fe8 sp=0xc000487fe0 pc=0xd02561
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c
goroutine 22 gp=0xc0005841c0 m=4 mp=0xc000077808 [syscall]:
runtime.notetsleepg(0x25d1460, 0xffffffffffffffff)
runtime/lock_sema.go:296 +0x31 fp=0xc000503fa0 sp=0xc000503f68 pc=0xca1ad1
os/signal.signal_recv()
runtime/sigqueue.go:152 +0x29 fp=0xc000503fc0 sp=0xc000503fa0 pc=0xcfe249
os/signal.loop()
os/signal/signal_unix.go:23 +0x13 fp=0xc000503fe0 sp=0xc000503fc0 pc=0xfc56b3
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000503fe8 sp=0xc000503fe0 pc=0xd02561
created by os/signal.Notify.func1.1 in goroutine 1
os/signal/signal.go:151 +0x1f
goroutine 23 gp=0xc000584380 m=nil [chan receive]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000507f00 sp=0xc000507ee0 pc=0xcd17ee
runtime.chanrecv(0xc0006e6ae0, 0x0, 0x1)
runtime/chan.go:583 +0x3cd fp=0xc000507f78 sp=0xc000507f00 pc=0xc9ba4d
runtime.chanrecv1(0x0?, 0x0?)
runtime/chan.go:442 +0x12 fp=0xc000507fa0 sp=0xc000507f78 pc=0xc9b652
github.com/ollama/ollama/server.Serve.func2()
github.com/ollama/ollama/server/routes.go:1066 +0x3d fp=0xc000507fe0 sp=0xc000507fa0 pc=0x17ae4fd
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000507fe8 sp=0xc000507fe0 pc=0xd02561
created by github.com/ollama/ollama/server.Serve in goroutine 1
github.com/ollama/ollama/server/routes.go:1065 +0x759
goroutine 24 gp=0xc000584540 m=nil [select]:
runtime.gopark(0xc000495f50?, 0x3?, 0x60?, 0x0?, 0xc000495e02?)
runtime/proc.go:402 +0xce fp=0xc000495c88 sp=0xc000495c68 pc=0xcd17ee
runtime.selectgo(0xc000495f50, 0xc000495dfc, 0xf00000007?, 0x0, 0xc000480380?, 0x1)
runtime/select.go:327 +0x725 fp=0xc000495da8 sp=0xc000495c88 pc=0xce1c45
github.com/ollama/ollama/server.(*Scheduler).processPending(0xc0006e6780, {0x1e01910, 0xc0002bca50})
github.com/ollama/ollama/server/sched.go:106 +0xcf fp=0xc000495fb8 sp=0xc000495da8 pc=0x17b1d4f
github.com/ollama/ollama/server.(*Scheduler).Run.func1()
github.com/ollama/ollama/server/sched.go:96 +0x1f fp=0xc000495fe0 sp=0xc000495fb8 pc=0x17b1c5f
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000495fe8 sp=0xc000495fe0 pc=0xd02561
created by github.com/ollama/ollama/server.(*Scheduler).Run in goroutine 1
github.com/ollama/ollama/server/sched.go:95 +0xb4
goroutine 25 gp=0xc000584700 m=nil [select]:
runtime.gopark(0xc000501f50?, 0x3?, 0x0?, 0x0?, 0xc000501d52?)
runtime/proc.go:402 +0xce fp=0xc000501be0 sp=0xc000501bc0 pc=0xcd17ee
runtime.selectgo(0xc000501f50, 0xc000501d4c, 0x0?, 0x0, 0x0?, 0x1)
runtime/select.go:327 +0x725 fp=0xc000501d00 sp=0xc000501be0 pc=0xce1c45
github.com/ollama/ollama/server.(*Scheduler).processCompleted(0xc0006e6780, {0x1e01910, 0xc0002bca50})
github.com/ollama/ollama/server/sched.go:258 +0xec fp=0xc000501fb8 sp=0xc000501d00 pc=0x17b2c6c
github.com/ollama/ollama/server.(*Scheduler).Run.func2()
github.com/ollama/ollama/server/sched.go:100 +0x1f fp=0xc000501fe0 sp=0xc000501fb8 pc=0x17b1c1f
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000501fe8 sp=0xc000501fe0 pc=0xd02561
created by github.com/ollama/ollama/server.(*Scheduler).Run in goroutine 1
github.com/ollama/ollama/server/sched.go:99 +0x110
rax 0x7ff81ac4f4b0
rbx 0xc0002d0a10
rcx 0x0
rdx 0x16c
rdi 0xc0002d0a70
rsi 0x483efdc0
rbp 0x483efd20
rsp 0x483efb88
r8 0x16c
r9 0x16c
r10 0x16c
r11 0x483ef860
r12 0x7ff85b99b1d0
r13 0x24a3f7e
r14 0x0
r15 0x483efbd0
rip 0x0
rflags 0x10246
cs 0x33
fs 0x53
gs 0x2b
```
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| 2,216,951,849
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I_kwDOJ0Z1Ps6EJAAp
| 3,424
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Support for OpenSUSE Tumbleweed and Leap in installer script
|
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[] | null | 0
| 2024-03-31T12:38:01
| 2024-04-01T19:59:08
| null |
NONE
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### What are you trying to do?
To install ollama on OpenSUSE Tumbleweed and Leap.
My laptop has AMD and Nvidia Video Cards.
Adapting the installation script to make the installation work correctly on OpenSUSE as well.
curl -fsSL https://ollama.com/install.sh | sh
### How should we solve this?
To install graphic packages on opensuse as prerequisites.
Adapting the installation script to make the installation work correctly on OpenSUSE as well.
### What is the impact of not solving this?
I had to install the packages manually.
### Anything else?
I used this command to install nvidia-smi: "zypper in nvidia-compute-utils-G06"
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| 2,528,419,723
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I_kwDOJ0Z1Ps6WtJ-L
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Massive performance regression on 0.1.32 -> GGML_CUDA_FORCE_MMQ: (SET TO NO, after 0.1.31)
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| 2024-09-16T13:04:35
| 2024-10-23T00:09:21
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### What is the issue?
For reference:
https://github.com/ollama/ollama/issues/3938
The issue might be actually result of disabling the following mode:
Older versions: 0.1.31
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: YES
New versions (After 0.1.31)
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
I tried to force this via env variables, but it did not help. Is there way to configure this via OLLAMA
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.31 -> 0.3.10
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I_kwDOJ0Z1Ps6JLDyu
| 4,479
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Add GPU number to ps command.
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The `ollama ps` command is great but it would be nice to have flags to get some additional information such as which GPU(s) the model is running on and how much it is using on that GPU.
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