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Model: mradermacher/SenikV416Bit-GGUF Source: Original Platform
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---
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base_model: JesusAura999/SenikV416Bit
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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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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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- sft
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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/JesusAura999/SenikV416Bit
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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#SenikV416Bit-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/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q2_K.gguf) | Q2_K | 1.5 | |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q5_K_S.gguf) | Q5_K_S | 2.4 | |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q5_K_M.gguf) | Q5_K_M | 2.4 | |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/SenikV416Bit-GGUF/resolve/main/SenikV416Bit.f16.gguf) | f16 | 6.5 | 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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SenikV416Bit.IQ4_XS.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:3ac3ee71d21a1fa42f6c78f4493e496bdc7e06d0760856764f65589ac8a98d5d
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size 1840908032
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oid sha256:42ff4354424a0582d36b11907900eedd1ad39fe86c746f0d3c0364e32f2d9c14
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size 1363937024
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oid sha256:d89b70a63d8af9dbb911b4637fd15dcaae7854a9daefbdd8beb97a10ad4d70ee
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size 1815348992
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oid sha256:d2ebb34e6db1d81fa8390f2121dd46486b6eb30cea325314a307bff1400d740b
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size 1687160576
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SenikV416Bit.Q3_K_S.gguf
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oid sha256:4e49538d18d3eff0cdf0d5df7f8ffb76be9574415f09b7d63bda8381af6b7ba9
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size 1542850304
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oid sha256:3bf1ab0c2b84f00abb763fe5d8de60887a3195145ad22e9f65a58ce6ad7e5694
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size 2019378944
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oid sha256:44684f8c5c85c54b61f6d168282ed401fad270e7fcc63f5ebb9c408bd0feeb00
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size 1928201984
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oid sha256:2c35582d1a8c2523eea1f25d8172739d62e59327bd4731241dad6c2718a048cf
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size 2322155264
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oid sha256:39cb8991d5c0a75e4265f1d80abaed2d3a742c87194c40dd2541c41ea6f823b8
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size 2269513472
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oid sha256:a3b8c6c239d568391bf7585d51c633e359b31067021f6ff3a7c38bfce9e109b2
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size 2643855104
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oid sha256:4237d061b8841a68f05f36ece903e3e314383d4b9403abbd0601539340cf010f
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size 3421900544
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size 6433689344
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