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Model: mradermacher/YamshadowInex12_YamShadow-GGUF Source: Original Platform
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---
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base_model: MaziyarPanahi/YamshadowInex12_YamShadow
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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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model_creator: MaziyarPanahi
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model_name: YamshadowInex12_YamShadow
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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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- Safetensors
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- text-generation-inference
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- merge
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---
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## About
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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/MaziyarPanahi/YamshadowInex12_YamShadow
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<!-- provided-files -->
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#YamshadowInex12_YamShadow-GGUF).***
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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.
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## Usage
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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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## Provided Quants
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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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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q2_K.gguf) | Q2_K | 2.8 | |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/YamshadowInex12_YamShadow-GGUF/resolve/main/YamshadowInex12_YamShadow.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
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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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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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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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## Thanks
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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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<!-- end -->
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version https://git-lfs.github.com/spec/v1
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oid sha256:82d2973d84e8197c71bf19e6ff74d99d46709adef8683a430ab90ddf838e917a
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size 3944389792
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oid sha256:2b93ec2e1eda7e7dbcedbd10cdf65a1c99a1148530a168b89055b348eb612fa9
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size 2719243424
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oid sha256:01ba546ea297f0999ccf707e0775c8eefa846b68a668c83c0ee09b234f5d78d9
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size 3822025888
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oid sha256:e75c1de2d542e582b67d45b6fd420a533771a670f9c85a0644da2399163979fc
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size 3518987424
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oid sha256:192f1e8d93ab4c2f64efde7b38881008114ce754660458ee7c4a1eb35b8a3b4e
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size 3164568736
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oid sha256:d22be9854eab86e5193fbebbc9e0a0616d08b2d4a592afb3ef7f4ed6c8e86942
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size 4368440480
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oid sha256:bd6201122d7237c8ce2d0d1339182c236396bfce19da5552fcf5aa3123da23db
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size 4140375200
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oid sha256:ddea6bde3c3f4627c2820ad2a841ed8b2be1b47360be5ddacd562a7cbe41ce62
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size 5131410592
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oid sha256:d3b878ff13443810779f75d69970e4a2e6ba7ee431c5c4cef79def49bdabe5b4
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size 4997717152
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oid sha256:feb21c7bb70dab80f4e43605470eec2279b080089fc066342f3c8a68081cde10
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size 5942066336
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oid sha256:ef36cc22671def04b4d6d4acbc114f9752853efef2e0cb1ad8f4a6dac4e75a2b
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size 7695858848
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oid sha256:7bcc488fd2034b90b7644cdb5da547f1dd011a523b5cd6d7664b33c802a3bb71
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size 14484733088
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