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Nanonets-OCR-s-AIO-GGUF

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Transformers
GGUF
English
qwen2_5_vl
ggml
llama.cpp
text-generation-inference
OCR
pdf2markdown
image-to-text
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+ ---
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+ base_model: nanonets/Nanonets-OCR-s
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+ language:
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+ - en
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+ library_name: transformers
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+ license: apache-2.0
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+ mradermacher:
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+ readme_rev: 1
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+ quantized_by: mradermacher
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+ tags:
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+ - OCR
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+ - pdf2markdown
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+ ---
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+ ## About
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+
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+ <!-- ### quantize_version: 2 -->
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+ <!-- ### output_tensor_quantised: 1 -->
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+ <!-- ### convert_type: hf -->
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+ <!-- ### vocab_type: -->
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+ <!-- ### tags: -->
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+ static quants of https://huggingface.co/nanonets/Nanonets-OCR-s
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+
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+ <!-- provided-files -->
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+
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+ ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Nanonets-OCR-s-GGUF).***
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+
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+ weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nanonets-OCR-s-i1-GGUF
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+ ## Usage
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+
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+ If you are unsure how to use GGUF files, refer to one of [TheBloke's
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+ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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+ more details, including on how to concatenate multi-part files.
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+
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+ ## Provided Quants
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+
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+ (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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+
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+ | Link | Type | Size/GB | Notes |
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+ |:-----|:-----|--------:|:------|
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.9 | multi-modal supplement |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q2_K.gguf) | Q2_K | 1.4 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.mmproj-f16.gguf) | mmproj-f16 | 1.4 | multi-modal supplement |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q5_K_S.gguf) | Q5_K_S | 2.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Nanonets-OCR-s-GGUF/resolve/main/Nanonets-OCR-s.f16.gguf) | f16 | 6.3 | 16 bpw, overkill |
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+
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+ Here is a handy graph by ikawrakow comparing some lower-quality quant
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+ types (lower is better):
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+
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+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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+
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+ And here are Artefact2's thoughts on the matter:
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+ https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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+
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+ ## FAQ / Model Request
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+
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+ See https://huggingface.co/mradermacher/model_requests for some answers to
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+ questions you might have and/or if you want some other model quantized.
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+
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+ ## Thanks
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+
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+ I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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+ me use its servers and providing upgrades to my workstation to enable
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+ this work in my free time.
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+
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+ <!-- end -->