117 lines
3.4 KiB
Markdown
117 lines
3.4 KiB
Markdown
---
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base_model: bigscience/bloomz-560m
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datasets:
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- bigscience/xP3
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language:
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- ak
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- ar
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- as
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- bm
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- bn
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- ca
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- code
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- en
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- es
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- eu
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- fon
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- fr
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- gu
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- hi
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- id
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- ig
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- ki
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- kn
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- lg
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- ln
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- ml
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- mr
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- ne
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- nso
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- ny
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- or
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- pa
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- pt
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- rn
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- rw
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- sn
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- st
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- sw
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- ta
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- te
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- tn
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- ts
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- tum
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- tw
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- ur
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- vi
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- wo
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- xh
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- yo
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- zh
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- zu
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library_name: transformers
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license: bigscience-bloom-rail-1.0
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mradermacher:
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readme_rev: 1
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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/bigscience/bloomz-560m
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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#bloomz-560m-GGUF).***
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/bloomz-560m-i1-GGUF
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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/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q2_K.gguf) | Q2_K | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q3_K_S.gguf) | Q3_K_S | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q3_K_M.gguf) | Q3_K_M | 0.6 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q3_K_L.gguf) | Q3_K_L | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.IQ4_XS.gguf) | IQ4_XS | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q4_K_M.gguf) | Q4_K_M | 0.7 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q5_K_S.gguf) | Q5_K_S | 0.7 | |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q5_K_M.gguf) | Q5_K_M | 0.7 | |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q6_K.gguf) | Q6_K | 0.8 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.Q8_0.gguf) | Q8_0 | 1.0 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/bloomz-560m-GGUF/resolve/main/bloomz-560m.f16.gguf) | f16 | 1.7 | 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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