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parth-mlx-decode-checkpoints
dhiltgen/ci
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hoyyeva/fix-codex-model-metadata-warning
hoyyeva/qwen
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Originally created by @pamdla on GitHub (Jun 5, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/10989
Hi dear, please add these models, qwen3-embeddings, qwen3-rerankers , which are newly released on https://huggingface.co/Qwen/.
@rick-github commented on GitHub (Jun 5, 2025):
ollama doesn't currently support ranking models, #3368.
@pamdla commented on GitHub (Jun 5, 2025):
great, i made it.
however, reranker models are really helpful for rag, please make a plan for them.
@chaorders1 commented on GitHub (Jun 6, 2025):
Looking forward Ollama support. This is the best embedding models. Waiting for years
@softlgl commented on GitHub (Jun 6, 2025):
I also hope to support qwen3-embedding
@hanwsf commented on GitHub (Jun 6, 2025):
Qwen embedding can support by Modelfile:
FROM /root/.ollama/Qwen3-Embedding-8B-Q4_K_M.gguf
SYSTEM """Qwen3-Embedding-8B 中文嵌入模型
向量维度: 4096
上下文长度: 32764
"""
TEMPLATE """{{ .Prompt }}"""
PARAMETER temperature 0
@rick-github commented on GitHub (Jun 6, 2025):
Just import it as show in https://github.com/ollama/ollama/issues/10989#issuecomment-2946454983.
@wwjCMP commented on GitHub (Jun 7, 2025):
Does ollama support changing the embedding length of embedding model?
@rick-github commented on GitHub (Jun 7, 2025):
No.
https://arxiv.org/abs/1708.03629
@zcuder commented on GitHub (Jun 7, 2025):
@rick-github importing by the above modelfile doesn't show as embedding model, not sure if I need anything else in modelfile to make it work as expected
@rick-github commented on GitHub (Jun 7, 2025):
A model needs to have a
pooling_typefield in the KV metadata in order to haveembeddingas a listed capability. That's a part of the GGUF file, there's no Modelfile setting that can affect that. Note that any model can be used for embedding, models that haveembeddingas a capability are exclusively embedding models. In this case it looks like Qwen just took the embedding part of their general purpose model and made it available as a separate component. Because it doesn't have all the parameters of a general purpose model, it runs faster and takes less memory.@czadikem commented on GitHub (Jun 9, 2025):
Interested in seeing these models as official embedding models in the ollama model library sometime soon.
@GrainArc commented on GitHub (Jun 11, 2025):
What's funny is that previously, developers had implemented support for loading rerank models in Ollama through llama.cpp, but the official team refused to merge the code. Many people have raised the need for rerank model compatibility, but the official team has consistently turned a blind eye to it
@jessegross commented on GitHub (Jun 11, 2025):
@fmecool The original author never responded to comments on the code.
@yebanliuying commented on GitHub (Jun 12, 2025):
yep,we need it
@zcuder commented on GitHub (Jun 12, 2025):
might be so many times back and forth and the author gave up this PR.
Could you guys make another one implementing this?
@Leroy-X commented on GitHub (Jun 18, 2025):
need+1
@charescape commented on GitHub (Jun 19, 2025):
There are very few embedding models available for Ollama.
https://ollama.com/search?c=embedding
Embedding models are the type of models that Ollama needs to support the most.
@GrainArc commented on GitHub (Jun 19, 2025):
Ollama has been compatible with embedding models since version 7.0, and the current focus is on the rerank model.
@charescape commented on GitHub (Jun 19, 2025):
Since both models are widely needed, this issue can be divided into two PRs for Ollama:
@rick-github commented on GitHub (Jun 19, 2025):
qwen3 embedding is already supported.
@qwerty199369 commented on GitHub (Jun 27, 2025):
Rerank model support for Ollama:
@khanakia commented on GitHub (Jul 11, 2025):
it producing garbage not embedding. Can you tell me please how were you able to run it ?
@rick-github commented on GitHub (Jul 11, 2025):
Use the /embed endpoint.
@khanakia commented on GitHub (Jul 11, 2025):
@rick-github thanks it worked
Also, one more thing, do you know how I set the
dimensionsQwen3-Embedding-8B-GGUFgenerates 4096 tokens currently i want only1024@rick-github commented on GitHub (Jul 11, 2025):
#11213
@devlux76 commented on GitHub (Jul 14, 2025):
It's MRL like nomic-embed-text this means you just truncate to the size you want since the most important vectors are first. I'm getting better results on Qwen3-0.6b embed truncated to 32 dimensions than I did on nomic-embed-text at 768 dimensions (or truncated). My use case is legal texts though so YMMV.
@lianzhao commented on GitHub (Jul 15, 2025):
Hello @devlux76, I was wondering if you could kindly clarify what exactly you were embedding? Were you embedding the user’s query, or the legal documents themselves? Thank you!
@devlux76 commented on GitHub (Jul 16, 2025):
I was embedding the documents for search with a clustering target and then generating embeddings for the query to perform a document search. The key here is that legal case texts have a very regular structure to them and need to cluster by more than simple semantic similarity since the laws of each jurisdiction are different and the overlap isn't very broad.
It's slow but works for the most part.
@kripper commented on GitHub (Aug 26, 2025):
ollama pull https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF@kripper commented on GitHub (Aug 26, 2025):
Mabye still broken?
https://github.com/ggml-org/llama.cpp/pull/14029
@daniporr commented on GitHub (Sep 6, 2025):
The library includes several embedding models, such as https://ollama.com/library/granite-embedding and https://ollama.com/library/embeddinggemma. The latter is also featured in the latest release: https://github.com/ollama/ollama/releases/tag/v0.11.10
Why not include Qwen3 Embedding as well?
@rick-github commented on GitHub (Sep 24, 2025):
Now officially in the main library.
https://ollama.com/library/qwen3-embedding
@ytwytw commented on GitHub (Sep 28, 2025):
Will ollama have reranker support?
@rick-github commented on GitHub (Sep 28, 2025):
https://github.com/ollama/ollama/pull/11389
@PeterWang-dev commented on GitHub (Feb 17, 2026):
Any progress on this issue?
@lyfuci commented on GitHub (Feb 17, 2026):
There doesn't seem to be a plan yet.