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text2vec-large-chinese-GGUF
mradermacher/text2vec-large-chinese-GGUF
text2vec-large-chinese-GGUF is a feature extraction model from mradermacher. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as apache-2.0.
static quants of https://huggingface.co/GanymedeNil/text2vec-large-chinese
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At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Apr 3, 2025
- Updated
- Jul 11, 2025
- SHA
- 22a5098c
Base models
Model card
From the Hugging Face model README
About
<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: -->static quants of https://huggingface.co/GanymedeNil/text2vec-large-chinese
<!-- provided-files -->For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/text2vec-large-chinese-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 0.2 | |
| GGUF | Q3_K_S | 0.3 | |
| GGUF | Q3_K_M | 0.3 | lower quality |
| GGUF | IQ4_XS | 0.3 | |
| GGUF | Q3_K_L | 0.3 | |
| GGUF | Q4_K_S | 0.3 | fast, recommended |
| GGUF | Q4_K_M | 0.3 | fast, recommended |
| GGUF | Q5_K_S | 0.3 | |
| GGUF | Q5_K_M | 0.3 | |
| GGUF | Q6_K | 0.4 | very good quality |
| GGUF | Q8_0 | 0.4 | fast, best quality |
| GGUF | f16 | 0.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.
<!-- end -->- Task
- Feature Extraction
- Library
- transformers
- License
- apache-2.0
- Languages
- zh
- Created
- Apr 3, 2025
- Updated
- Jul 11, 2025