[GH-ISSUE #12611] The embedding result of qwen3-embedding:8b model are all zeros. #70431

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opened 2026-05-04 21:32:21 -05:00 by GiteaMirror · 15 comments

GiteaMirror commented 2026-05-04 21:32:21 -05:00

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Originally created by @anderscui on GitHub (Oct 14, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/12611

What is the issue?

I used the following command to test embedding:

curl http://localhost:11434/api/embeddings -d '{ "model": "qwen3-embedding:8b", "prompt": "Hello world" }'

Relevant log output

The result was something like: {"embedding":[0,0,0,0,0...]}, while the qwen3-embedding:4b and qwen3-embedding:0.6b worked as expected.

OS

macOS

GPU

CPU

M4 Pro

Ollama version

0.12.5

Originally created by @anderscui on GitHub (Oct 14, 2025). Original GitHub issue: https://github.com/ollama/ollama/issues/12611 ### What is the issue? I used the following command to test embedding: ` curl http://localhost:11434/api/embeddings -d '{ "model": "qwen3-embedding:8b", "prompt": "Hello world" }'` ### Relevant log output The result was something like: `{"embedding":[0,0,0,0,0...]}`, while the qwen3-embedding:4b and qwen3-embedding:0.6b worked as expected. ### OS macOS ### GPU ### CPU M4 Pro ### Ollama version 0.12.5

GiteaMirror added the macosbug labels 2026-05-04 21:32:21 -05:00

GiteaMirror closed this issue 2026-05-04 21:32:23 -05:00

GiteaMirror commented 2026-05-04 21:32:24 -05:00

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@rick-github commented on GitHub (Oct 14, 2025):

/api/embeddings is deprecated, use /api/embed.

<!-- gh-comment-id:3402705796 --> @rick-github commented on GitHub (Oct 14, 2025): `/api/embeddings` is [deprecated](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embedding), use [`/api/embed`](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings).

GiteaMirror commented 2026-05-04 21:32:25 -05:00

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@mxyng commented on GitHub (Oct 14, 2025):

while @rick-github is correct, /api/embeddings still works. make sure your model and ollama server are up to date

$ curl http://localhost:11434/api/embeddings -d '{ "model": "qwen3-embedding:8b", "prompt": "Hello world" }'
{"embedding":[0.02529112808406353,0.017940087243914604,-0.017295848578214645,-0.021380838006734848,0.01712549477815628,-0.025202978402376175,-0.028270818293094635,0.01153936330229044,-0.020545639097690582,0.021490776911377907,-0.017985975369811058,-0.002473235595971346,0.056987784802913666,-0.006050224881619215,0.00885941181331873,0.011059737764298916,0.01674455776810646,-0.011054578237235546,0.022527562454342842,0.023273127153515816,-0.021112516522407532,0.02205529995262623,0.014213010668754578,0.006479757837951183,0.01978764683008194,0.022975319996476173,-0.044350892305374146,-0.020103737711906433,-0.0007237489917315543,0.018887558951973915,-0.029426204040646553,0.01137286052107811,0.008009737357497215,0.042688481509685516,-0.02016260474920273,0.0011514367070049047,-0.01344395149499178,0.0014968507457524538,0.01586371287703514,0.009688826277852058,0.007019012235105038,0.001486640190705657,0.030335919931530952,0.0012713221367448568,-0.009275839664041996,0.0001020722120301798,-0.02713855728507042,-0.008620101027190685,0.014029511250555515,0.014794227667152882,-0.008338965475559235,-0.020786497741937637,-0.03106369636952877,-0.012360993772745132,0.000513804261572659,-0.019089259207248688,-0.015167323872447014,-0.02348318323493004,-0.03558269515633583,-0.007842679508030415,-0.04326070845127106,0.012541793286800385,0.00020139348634984344,-0.007959309034049511,-0.0008990645292215049,0.0013590046437457204,0.014985865913331509,-0.006303086411207914,0.019429506734013557,-0.008813449181616306,0.015404722653329372,0.01969764195382595,0.012838218361139297,0.005629889667034149,-0.0046142940409481525,0.025086872279644012,-0.031508684158325195,-0.03919781744480133,0.0065529122948646545,0.022283392027020454,-0.003712656442075968,-0.0002855566854123026,-0.007759829983115196,-0.023937195539474487,-0.0013068454572930932,-0.018139630556106567,0.0013492800062522292,-0.009097724221646786,-0.0008019921369850636,-0.019522836431860924,0.017057185992598534,0.007707515265792608,0.0044206539168953896,0.014790656045079231,0.007461567874997854,0.003909335006028414,-0.0266784206032753,0.004963880870491266,-0.019073758274316788,0.004807872232049704,0.018482385203242302,0.03436123952269554,0.016909858211874962,0.003694976447150111,0.015970179811120033,-0.008827383629977703,-0.014783620834350586,0.003950259182602167,-0.010284682735800743,-0.01373718399554491,0.01365810725837946,0.008611937053501606,0.03466152027249336,0.016406282782554626,-0.004969292785972357,-0.032227713614702225,-0.00020721623150166124,0.0026622777804732323,-0.010887376964092255,0.01454424113035202,-0.005715734791010618,-0.0036321119405329227,-0.016923489049077034,-0.007558404002338648,0.021652476862072945,-0.008637201972305775,0.0047758338041603565,0.023360637947916985,0.01396998856216669,-0.005079707596451044,0.006522896699607372,-0.02770478092133999,0.0008177635027095675,-0.02236798033118248,-0.0022331757936626673,-0.0004824648203793913,0.0023080711252987385,-0.012801515869796276,-0.019284382462501526,-0.0028788791969418526,-0.01568032056093216,0.021188149228692055,0.012790641747415066,-0.010110465809702873,-0.0010483604855835438,0.011105627752840519,-0.0072148810140788555,0.0186003390699625,0.0005051622865721583,-0.0015992509433999658,0.00019645011343527585,-0.00178913539275527,-0.015110849402844906,-0.004436714109033346,-0.024797994643449783,-0.007764868903905153,-0.009018553420901299,0.017349937930703163,0.03203380107879639,-0.018396111205220222,-0.0014000148512423038,0.0018070935038849711,0.03451995179057121,0.010260852053761482,-0.003869161242619157,-0.00757937878370285,0.014199738390743732,-0.0052329800091683865,0.02245057001709938,-0.005917162634432316,0.019398104399442673,-0.0007940120995044708,-0.040542080998420715,0.020713768899440765,0.00874464213848114,-0.0269844438880682,-0.006610747426748276,-0.04256368428468704,0.007843133993446827,0.027038412168622017,-0.005988159216940403,0.025597674772143364,0.00638308422639966,0.020444074645638466,-0.005876990035176277,0.02060653083026409,-0.0029832939617335796,-0.007633923087269068,0.000661878555547446,0.01707874983549118,0.001089561963453889,0.009434611536562443,-0.004006153903901577,0.008030977100133896,0.00006106137880124152,0.007248233072459698,-0.006354827433824539,0.002948704408481717,0.026801494881510735,-0.012388227507472038,0.048635728657245636,-0.027725407853722572,0.005320155061781406,0.0008700116886757314,0.009606406092643738,-0.021215856075286865,-0.02354278974235058,0.026803014799952507,-0.0036812236066907644,-0.014477923512458801,0.004312217701226473,0.004908450413495302,0.007558591663837433,0.029306655749678612,-0.007914840243756771,-0.0002788131241686642,0.015836402773857117,-0.012798106297850609,-0.012261300347745419,-0.0026322968769818544,0.012985908426344395,-0.007553332485258579,-0.001873787958174944,0.019853726029396057,-0.0067986007779836655,-0.044173069298267365,0.007477863226085901,-0.009320192039012909,-0.004257191438227892,-0.03248301148414612,0.01992480829358101,-0.02617519162595272,-0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<!-- gh-comment-id:3402812641 --> @mxyng commented on GitHub (Oct 14, 2025): while @rick-github is correct, `/api/embeddings` still works. make sure your model and ollama server are up to date ``` $ curl http://localhost:11434/api/embeddings -d '{ "model": "qwen3-embedding:8b", "prompt": "Hello world" }' 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```

GiteaMirror commented 2026-05-04 21:32:27 -05:00

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@anderscui commented on GitHub (Oct 15, 2025):

@mxyng, @rick-github, thanks for your replies, I just tested /api/embed, the result of qwen3-embedding:8b was also problematic, but qwen3-embedding:0.6b was fine.

Maybe there's some subtle issue on my local machine. Before the official qwen3-embedding:8b, I imported Qwen3-Embedding-8B-Q4_K_M.gguf as andersc/qwen3-embedding:8b, and used it locally.

Only 1 day later, I found Ollama published their qwen3-embedding:8b, so I downloaded it instead, and yesterday I found it didn't work.

The names are exactly same, is this the problem?

<!-- gh-comment-id:3404585447 --> @anderscui commented on GitHub (Oct 15, 2025): @mxyng, @rick-github, thanks for your replies, I just tested `/api/embed`, the result of `qwen3-embedding:8b` was also problematic, but `qwen3-embedding:0.6b` was fine. Maybe there's some subtle issue on my local machine. Before the official `qwen3-embedding:8b`, I imported `Qwen3-Embedding-8B-Q4_K_M.gguf` as `andersc/qwen3-embedding:8b`, and used it locally. Only 1 day later, I found Ollama published their `qwen3-embedding:8b`, so I downloaded it instead, and yesterday I found it didn't work. The names are exactly same, is this the problem?

GiteaMirror commented 2026-05-04 21:32:28 -05:00

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@hundt commented on GitHub (Oct 15, 2025):

I am also experiencing this problem, with both qwen3-embedding:0.6b and qwen3-embedding:8b. I had not previously downloaded either model.

<!-- gh-comment-id:3408011208 --> @hundt commented on GitHub (Oct 15, 2025): I am also experiencing this problem, with both `qwen3-embedding:0.6b` and `qwen3-embedding:8b`. I had not previously downloaded either model.

GiteaMirror commented 2026-05-04 21:32:29 -05:00

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@rick-github commented on GitHub (Oct 15, 2025):

What's the output of:

ollama -v
curl http://localhost:11434/api/embeddings -d '{ "model": "qwen3-embedding:8b", "prompt": "Hello world" }'

<!-- gh-comment-id:3408104945 --> @rick-github commented on GitHub (Oct 15, 2025): What's the output of: ``` ollama -v curl http://localhost:11434/api/embeddings -d '{ "model": "qwen3-embedding:8b", "prompt": "Hello world" }' ```

GiteaMirror commented 2026-05-04 21:32:29 -05:00

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@hundt commented on GitHub (Oct 15, 2025):

@rick-github

ollama version is 0.12.5
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<!-- gh-comment-id:3408112852 --> @hundt commented on GitHub (Oct 15, 2025): @rick-github ``` ollama version is 0.12.5 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```

GiteaMirror commented 2026-05-04 21:32:30 -05:00

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@rick-github commented on GitHub (Oct 15, 2025):

Output of:

ollama ps
ollama list qwen3-embedding:8b

<!-- gh-comment-id:3408152545 --> @rick-github commented on GitHub (Oct 15, 2025): Output of: ``` ollama ps ollama list qwen3-embedding:8b ```

GiteaMirror commented 2026-05-04 21:32:33 -05:00

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@hundt commented on GitHub (Oct 15, 2025):

NAME                  ID              SIZE      PROCESSOR    CONTEXT    UNTIL              
qwen3-embedding:8b    64b933495768    5.8 GB    100% GPU     4096       4 minutes from now    
NAME                  ID              SIZE      MODIFIED       
qwen3-embedding:8b    64b933495768    4.7 GB    51 minutes ago   

<!-- gh-comment-id:3408174646 --> @hundt commented on GitHub (Oct 15, 2025): ``` NAME ID SIZE PROCESSOR CONTEXT UNTIL qwen3-embedding:8b 64b933495768 5.8 GB 100% GPU 4096 4 minutes from now NAME ID SIZE MODIFIED qwen3-embedding:8b 64b933495768 4.7 GB 51 minutes ago ```

GiteaMirror commented 2026-05-04 21:32:34 -05:00

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@hundt commented on GitHub (Oct 15, 2025):

FYI I was able to use another family of embedding models (snowflake-arctic-embed) successfully.

<!-- gh-comment-id:3408209983 --> @hundt commented on GitHub (Oct 15, 2025): FYI I was able to use another family of embedding models (snowflake-arctic-embed) successfully.

GiteaMirror commented 2026-05-04 21:32:35 -05:00

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@rick-github commented on GitHub (Oct 15, 2025):

Nothing leaps out, except that the size of the loaded model is smaller than what I can duplicate locally. Checking the logs might reveal some details:

journalctl -u ollama --no-pager --since "$(systemctl show ollama --property=ActiveEnterTimestamp --value)"

<!-- gh-comment-id:3408223719 --> @rick-github commented on GitHub (Oct 15, 2025): Nothing leaps out, except that the size of the loaded model is smaller than what I can duplicate locally. Checking the logs might reveal some details: ``` journalctl -u ollama --no-pager --since "$(systemctl show ollama --property=ActiveEnterTimestamp --value)" ```

GiteaMirror commented 2026-05-04 21:32:36 -05:00

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@hundt commented on GitHub (Oct 15, 2025):

I'm on a Mac so I think maybe you are looking for the contents of ~/.ollama/logs/server.log? Here is an excerpt from that file that appears to show it loading the model into memory:

llama_model_loader: loaded meta data with 36 key-value pairs and 398 tensors from /Users/chris/.ollama/models/blobs/sha256-3fcd3febec8b3fd64435204db75bf0dd73b91e8d0661e0331acfe7e7c3120b85 (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              = qwen3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen3 Embedding 8B
llama_model_loader: - kv   3:                           general.basename str              = Qwen3-Embedding
llama_model_loader: - kv   4:                         general.size_label str              = 8B
llama_model_loader: - kv   5:                            general.license str              = apache-2.0
llama_model_loader: - kv   6:                   general.base_model.count u32              = 1
llama_model_loader: - kv   7:                  general.base_model.0.name str              = Qwen3 8B Base
llama_model_loader: - kv   8:          general.base_model.0.organization str              = Qwen
llama_model_loader: - kv   9:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen3-8B-...
llama_model_loader: - kv  10:                               general.tags arr[str,5]       = ["transformers", "sentence-transforme...
llama_model_loader: - kv  11:                          qwen3.block_count u32              = 36
llama_model_loader: - kv  12:                       qwen3.context_length u32              = 40960
llama_model_loader: - kv  13:                     qwen3.embedding_length u32              = 4096
llama_model_loader: - kv  14:                  qwen3.feed_forward_length u32              = 12288
llama_model_loader: - kv  15:                 qwen3.attention.head_count u32              = 32
llama_model_loader: - kv  16:              qwen3.attention.head_count_kv u32              = 8
llama_model_loader: - kv  17:                       qwen3.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  18:     qwen3.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  19:                 qwen3.attention.key_length u32              = 128
llama_model_loader: - kv  20:               qwen3.attention.value_length u32              = 128
llama_model_loader: - kv  21:                         qwen3.pooling_type u32              = 3
llama_model_loader: - kv  22:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  23:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  24:                      tokenizer.ggml.tokens arr[str,151665]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  25:                  tokenizer.ggml.token_type arr[i32,151665]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  26:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  27:                tokenizer.ggml.eos_token_id u32              = 151643
llama_model_loader: - kv  28:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  29:                tokenizer.ggml.eot_token_id u32              = 151645
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  31:               tokenizer.ggml.add_eos_token bool             = true
llama_model_loader: - kv  32:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  33:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  34:               general.quantization_version u32              = 2
llama_model_loader: - kv  35:                          general.file_type u32              = 15
llama_model_loader: - type  f32:  145 tensors
llama_model_loader: - type q4_K:  216 tensors
llama_model_loader: - type q6_K:   37 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 4.35 GiB (4.94 BPW) 
load: printing all EOG tokens:
load:   - 151643 ('<|endoftext|>')
load:   - 151645 ('<|im_end|>')
load:   - 151662 ('<|fim_pad|>')
load:   - 151663 ('<|repo_name|>')
load:   - 151664 ('<|file_sep|>')
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch             = qwen3
print_info: vocab_only       = 1
print_info: model type       = ?B
print_info: model params     = 7.57 B
print_info: general.name     = Qwen3 Embedding 8B
print_info: vocab type       = BPE
print_info: n_vocab          = 151665
print_info: n_merges         = 151387
print_info: BOS token        = 151643 '<|endoftext|>'
print_info: EOS token        = 151643 '<|endoftext|>'
print_info: EOT token        = 151645 '<|im_end|>'
print_info: PAD token        = 151643 '<|endoftext|>'
print_info: LF token         = 198 'Ċ'
print_info: FIM PRE token    = 151659 '<|fim_prefix|>'
print_info: FIM SUF token    = 151661 '<|fim_suffix|>'
print_info: FIM MID token    = 151660 '<|fim_middle|>'
print_info: FIM PAD token    = 151662 '<|fim_pad|>'
print_info: FIM REP token    = 151663 '<|repo_name|>'
print_info: FIM SEP token    = 151664 '<|file_sep|>'
print_info: EOG token        = 151643 '<|endoftext|>'
print_info: EOG token        = 151645 '<|im_end|>'
print_info: EOG token        = 151662 '<|fim_pad|>'
print_info: EOG token        = 151663 '<|repo_name|>'
print_info: EOG token        = 151664 '<|file_sep|>'
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2025-10-15T15:43:44.141-04:00 level=WARN source=server.go:209 msg="flash attention enabled but not supported by model"
time=2025-10-15T15:43:44.142-04:00 level=INFO source=server.go:400 msg="starting runner" cmd="/Applications/Ollama.app/Contents/Resources/ollama runner --model /Users/chris/.ollama/models/blobs/sha256-3fcd3febec8b3fd64435204db75bf0dd73b91e8d0661e0331acfe7e7c3120b85 --port 60540"
time=2025-10-15T15:43:44.145-04:00 level=INFO source=server.go:505 msg="system memory" total="96.0 GiB" free="27.6 GiB" free_swap="0 B"
time=2025-10-15T15:43:44.146-04:00 level=INFO source=memory.go:36 msg="new model will fit in available VRAM across minimum required GPUs, loading" model=/Users/chris/.ollama/models/blobs/sha256-3fcd3febec8b3fd64435204db75bf0dd73b91e8d0661e0331acfe7e7c3120b85 library=Metal parallel=1 required="5.4 GiB" gpus=1
time=2025-10-15T15:43:44.146-04:00 level=INFO source=server.go:545 msg=offload library=Metal layers.requested=-1 layers.model=37 layers.offload=37 layers.split=[37] memory.available="[70.8 GiB]" memory.gpu_overhead="0 B" memory.required.full="5.4 GiB" memory.required.partial="5.4 GiB" memory.required.kv="576.0 MiB" memory.required.allocations="[5.4 GiB]" memory.weights.total="4.4 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="486.0 MiB" memory.graph.full="384.0 MiB" memory.graph.partial="384.0 MiB"
time=2025-10-15T15:43:44.164-04:00 level=INFO source=runner.go:864 msg="starting go runner"
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: loaded in 7.391 sec
ggml_metal_device_init: GPU name:   Apple M2 Max
ggml_metal_device_init: GPU family: MTLGPUFamilyApple8  (1008)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_device_init: simdgroup reduction   = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory    = true
ggml_metal_device_init: has bfloat            = true
ggml_metal_device_init: use residency sets    = true
ggml_metal_device_init: use shared buffers    = true
ggml_metal_device_init: recommendedMaxWorkingSetSize  = 77309.41 MB
time=2025-10-15T15:43:44.167-04:00 level=INFO source=ggml.go:104 msg=system Metal.0.EMBED_LIBRARY=1 CPU.0.NEON=1 CPU.0.ARM_FMA=1 CPU.0.FP16_VA=1 CPU.0.DOTPROD=1 CPU.0.LLAMAFILE=1 CPU.0.ACCELERATE=1 compiler=cgo(clang)

<!-- gh-comment-id:3408246385 --> @hundt commented on GitHub (Oct 15, 2025): I'm on a Mac so I think maybe you are looking for the contents of `~/.ollama/logs/server.log`? Here is an excerpt from that file that appears to show it loading the model into memory: ``` llama_model_loader: loaded meta data with 36 key-value pairs and 398 tensors from /Users/chris/.ollama/models/blobs/sha256-3fcd3febec8b3fd64435204db75bf0dd73b91e8d0661e0331acfe7e7c3120b85 (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 = qwen3 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen3 Embedding 8B llama_model_loader: - kv 3: general.basename str = Qwen3-Embedding llama_model_loader: - kv 4: general.size_label str = 8B llama_model_loader: - kv 5: general.license str = apache-2.0 llama_model_loader: - kv 6: general.base_model.count u32 = 1 llama_model_loader: - kv 7: general.base_model.0.name str = Qwen3 8B Base llama_model_loader: - kv 8: general.base_model.0.organization str = Qwen llama_model_loader: - kv 9: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3-8B-... llama_model_loader: - kv 10: general.tags arr[str,5] = ["transformers", "sentence-transforme... llama_model_loader: - kv 11: qwen3.block_count u32 = 36 llama_model_loader: - kv 12: qwen3.context_length u32 = 40960 llama_model_loader: - kv 13: qwen3.embedding_length u32 = 4096 llama_model_loader: - kv 14: qwen3.feed_forward_length u32 = 12288 llama_model_loader: - kv 15: qwen3.attention.head_count u32 = 32 llama_model_loader: - kv 16: qwen3.attention.head_count_kv u32 = 8 llama_model_loader: - kv 17: qwen3.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 18: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 19: qwen3.attention.key_length u32 = 128 llama_model_loader: - kv 20: qwen3.attention.value_length u32 = 128 llama_model_loader: - kv 21: qwen3.pooling_type u32 = 3 llama_model_loader: - kv 22: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 23: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 24: tokenizer.ggml.tokens arr[str,151665] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 25: tokenizer.ggml.token_type arr[i32,151665] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 26: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 27: tokenizer.ggml.eos_token_id u32 = 151643 llama_model_loader: - kv 28: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 29: tokenizer.ggml.eot_token_id u32 = 151645 llama_model_loader: - kv 30: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 31: tokenizer.ggml.add_eos_token bool = true llama_model_loader: - kv 32: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 33: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 34: general.quantization_version u32 = 2 llama_model_loader: - kv 35: general.file_type u32 = 15 llama_model_loader: - type f32: 145 tensors llama_model_loader: - type q4_K: 216 tensors llama_model_loader: - type q6_K: 37 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 4.35 GiB (4.94 BPW) load: printing all EOG tokens: load: - 151643 ('<|endoftext|>') load: - 151645 ('<|im_end|>') load: - 151662 ('<|fim_pad|>') load: - 151663 ('<|repo_name|>') load: - 151664 ('<|file_sep|>') load: special tokens cache size = 22 load: token to piece cache size = 0.9310 MB print_info: arch = qwen3 print_info: vocab_only = 1 print_info: model type = ?B print_info: model params = 7.57 B print_info: general.name = Qwen3 Embedding 8B print_info: vocab type = BPE print_info: n_vocab = 151665 print_info: n_merges = 151387 print_info: BOS token = 151643 '<|endoftext|>' print_info: EOS token = 151643 '<|endoftext|>' print_info: EOT token = 151645 '<|im_end|>' print_info: PAD token = 151643 '<|endoftext|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 151659 '<|fim_prefix|>' print_info: FIM SUF token = 151661 '<|fim_suffix|>' print_info: FIM MID token = 151660 '<|fim_middle|>' print_info: FIM PAD token = 151662 '<|fim_pad|>' print_info: FIM REP token = 151663 '<|repo_name|>' print_info: FIM SEP token = 151664 '<|file_sep|>' print_info: EOG token = 151643 '<|endoftext|>' print_info: EOG token = 151645 '<|im_end|>' print_info: EOG token = 151662 '<|fim_pad|>' print_info: EOG token = 151663 '<|repo_name|>' print_info: EOG token = 151664 '<|file_sep|>' print_info: max token length = 256 llama_model_load: vocab only - skipping tensors time=2025-10-15T15:43:44.141-04:00 level=WARN source=server.go:209 msg="flash attention enabled but not supported by model" time=2025-10-15T15:43:44.142-04:00 level=INFO source=server.go:400 msg="starting runner" cmd="/Applications/Ollama.app/Contents/Resources/ollama runner --model /Users/chris/.ollama/models/blobs/sha256-3fcd3febec8b3fd64435204db75bf0dd73b91e8d0661e0331acfe7e7c3120b85 --port 60540" time=2025-10-15T15:43:44.145-04:00 level=INFO source=server.go:505 msg="system memory" total="96.0 GiB" free="27.6 GiB" free_swap="0 B" time=2025-10-15T15:43:44.146-04:00 level=INFO source=memory.go:36 msg="new model will fit in available VRAM across minimum required GPUs, loading" model=/Users/chris/.ollama/models/blobs/sha256-3fcd3febec8b3fd64435204db75bf0dd73b91e8d0661e0331acfe7e7c3120b85 library=Metal parallel=1 required="5.4 GiB" gpus=1 time=2025-10-15T15:43:44.146-04:00 level=INFO source=server.go:545 msg=offload library=Metal layers.requested=-1 layers.model=37 layers.offload=37 layers.split=[37] memory.available="[70.8 GiB]" memory.gpu_overhead="0 B" memory.required.full="5.4 GiB" memory.required.partial="5.4 GiB" memory.required.kv="576.0 MiB" memory.required.allocations="[5.4 GiB]" memory.weights.total="4.4 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="486.0 MiB" memory.graph.full="384.0 MiB" memory.graph.partial="384.0 MiB" time=2025-10-15T15:43:44.164-04:00 level=INFO source=runner.go:864 msg="starting go runner" ggml_metal_library_init: using embedded metal library ggml_metal_library_init: loaded in 7.391 sec ggml_metal_device_init: GPU name: Apple M2 Max ggml_metal_device_init: GPU family: MTLGPUFamilyApple8 (1008) ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003) ggml_metal_device_init: GPU family: MTLGPUFamilyMetal3 (5001) ggml_metal_device_init: simdgroup reduction = true ggml_metal_device_init: simdgroup matrix mul. = true ggml_metal_device_init: has unified memory = true ggml_metal_device_init: has bfloat = true ggml_metal_device_init: use residency sets = true ggml_metal_device_init: use shared buffers = true ggml_metal_device_init: recommendedMaxWorkingSetSize = 77309.41 MB time=2025-10-15T15:43:44.167-04:00 level=INFO source=ggml.go:104 msg=system Metal.0.EMBED_LIBRARY=1 CPU.0.NEON=1 CPU.0.ARM_FMA=1 CPU.0.FP16_VA=1 CPU.0.DOTPROD=1 CPU.0.LLAMAFILE=1 CPU.0.ACCELERATE=1 compiler=cgo(clang) ```

GiteaMirror commented 2026-05-04 21:32:39 -05:00

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@rick-github commented on GitHub (Oct 15, 2025):

Ah, MacOS. Same as anderscui, so it might be related to the platform. Somebody with a mac will have to debug further.

<!-- gh-comment-id:3408255924 --> @rick-github commented on GitHub (Oct 15, 2025): Ah, MacOS. Same as anderscui, so it might be related to the platform. Somebody with a mac will have to debug further.

GiteaMirror commented 2026-05-04 21:32:39 -05:00

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@mxyng commented on GitHub (Oct 15, 2025):

ahh v0.12.5 is the problem. qwen3-embedding was reverted in that release so it falls back to llama.cpp. it's reenabled in main and the next release should resolve any issues

<!-- gh-comment-id:3408392544 --> @mxyng commented on GitHub (Oct 15, 2025): ahh v0.12.5 is the problem. qwen3-embedding was reverted in that release so it falls back to llama.cpp. it's reenabled in main and the next release should resolve any issues

GiteaMirror commented 2026-05-04 21:32:39 -05:00

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@hundt commented on GitHub (Oct 15, 2025):

Thanks, I downgraded to 0.12.4 and now both the 8b and 0.6b Qwen3 embedding models work for me.

<!-- gh-comment-id:3408583136 --> @hundt commented on GitHub (Oct 15, 2025): Thanks, I downgraded to 0.12.4 and now both the 8b and 0.6b Qwen3 embedding models work for me.

GiteaMirror commented 2026-05-04 21:32:41 -05:00

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@anderscui commented on GitHub (Oct 16, 2025):

@rick-github @mxyng @hundt, thank you all.

<!-- gh-comment-id:3408862679 --> @anderscui commented on GitHub (Oct 16, 2025): @rick-github @mxyng @hundt, thank you all.

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Reference: github-starred/ollama#70431