112 lines
3.3 KiB
Markdown
112 lines
3.3 KiB
Markdown
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---
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base_model: FreedomIntelligence/Apollo2-1.5B
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datasets:
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- FreedomIntelligence/ApolloMoEDataset
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language:
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- ar
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- en
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- zh
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- ko
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- ja
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- mn
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- th
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- vi
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- lo
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- mg
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- de
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- pt
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- es
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- fr
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- ru
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- it
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- hr
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- gl
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- cs
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- co
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- la
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- uk
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- bs
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- bg
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- eo
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- sq
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- da
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- sa
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- gn
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- sr
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- sk
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- gd
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- lb
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- hi
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- ku
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- mt
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- he
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- ln
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- bm
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- sw
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- ig
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- rw
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- ha
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library_name: transformers
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license: apache-2.0
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quantized_by: mradermacher
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tags:
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- biology
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- medical
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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/FreedomIntelligence/Apollo2-1.5B
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<!-- provided-files -->
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Apollo2-1.5B-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/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q2_K.gguf) | Q2_K | 0.9 | |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/Apollo2-1.5B-GGUF/resolve/main/Apollo2-1.5B.f16.gguf) | f16 | 3.7 | 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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