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DocOCR_SFT_v1_50-GGUF
mradermacher/DocOCR_SFT_v1_50-GGUF
DocOCR_SFT_v1_50-GGUF is a machine learning model from mradermacher. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
static quants of https://huggingface.co/Nayana-cognitivelab/DocOCRSFTv150
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61.9 GB
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.gguf61.9 GB · 100%
At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Aug 5, 2025
- Updated
- Aug 5, 2025
- SHA
- 7553c8e4
Base models
Model card
From the Hugging Face model README
About
<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: -->static quants of https://huggingface.co/Nayana-cognitivelab/DocOCR_SFT_v1_50
<!-- provided-files -->For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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 | 2.9 | |
| GGUF | Q3_K_S | 3.4 | |
| GGUF | Q3_K_M | 3.5 | lower quality |
| GGUF | Q3_K_L | 3.7 | |
| GGUF | IQ4_XS | 4.0 | |
| GGUF | Q4_K_S | 4.2 | fast, recommended |
| GGUF | Q4_K_M | 4.3 | fast, recommended |
| GGUF | Q5_K_S | 5.0 | |
| GGUF | Q5_K_M | 5.0 | |
| GGUF | Q6_K | 5.8 | very good quality |
| GGUF | Q8_0 | 7.5 | fast, best quality |
| GGUF | f16 | 13.8 | 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 -->- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Aug 5, 2025
- Updated
- Aug 5, 2025