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Model: mradermacher/T-lite-instruct-0.1-abliterated-GGUF Source: Original Platform
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T-lite-instruct-0.1-abliterated.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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---
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base_model: IlyaGusev/T-lite-instruct-0.1-abliterated
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language:
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- ru
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library_name: transformers
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license: wtfpl
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quantized_by: mradermacher
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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/IlyaGusev/T-lite-instruct-0.1-abliterated
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<!-- provided-files -->
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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/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q2_K.gguf) | Q2_K | 3.3 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.IQ3_M.gguf) | IQ3_M | 3.9 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/T-lite-instruct-0.1-abliterated-GGUF/resolve/main/T-lite-instruct-0.1-abliterated.f16.gguf) | f16 | 16.2 | 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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T-lite-instruct-0.1-abliterated.IQ3_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:495d57682f61fa248dcb46b67e75ffa59e6cf464345f29f4c3be4605850c7d81
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size 3784839392
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T-lite-instruct-0.1-abliterated.IQ3_S.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:048d970ed6856d2805c846a7fddc5bb1bb6aa294f6232cbcc68d160f1c7406e0
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size 3682341088
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T-lite-instruct-0.1-abliterated.IQ3_XS.gguf
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oid sha256:0904e1c690fed13ba502b0b6fb58811607c2b334b746a3a0e9bfc5b686dddb89
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size 3518763232
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T-lite-instruct-0.1-abliterated.IQ4_XS.gguf
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size 4484380096
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size 4321972448
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size 4018933984
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size 3664515296
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size 4920751936
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oid sha256:9f5357fd4d13bf0971562fd0aa3e75e2b77eb82d6da3c35fdeb30a7a9cb06f57
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size 4692686656
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size 5733006656
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oid sha256:166c679867c18f46554d2482b8a689ba0fb92e10c46f3905ce0039a23838c8a2
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size 5599313216
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oid sha256:340c4da393259e740c12f0ff04397b251df3b4c80985aa123f5495e5248710fd
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size 6596027296
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oid sha256:342d8c471a2b3d3b8e74c1b5adf3642986e4d997db16f5b780df8d48a9ce030c
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size 8540797664
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T-lite-instruct-0.1-abliterated.f16.gguf
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oid sha256:c6c4653bdf8cef524a3f3c44599bee65f5ec6d3f17f158b2fa3a677503a23d56
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size 16068941024
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