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README.md
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<!-- ### convert_type: -->
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<!-- ### vocab_type: -->
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weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-dpo-20b-v04
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---
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base_model: internlm/internlm2-20b
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datasets:
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- ai2_arc
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- allenai/ultrafeedback_binarized_cleaned
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- argilla/distilabel-intel-orca-dpo-pairs
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- jondurbin/airoboros-3.2
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- codeparrot/apps
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- facebook/belebele
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- bluemoon-fandom-1-1-rp-cleaned
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- boolq
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- camel-ai/biology
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- camel-ai/chemistry
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- camel-ai/math
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- camel-ai/physics
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- jondurbin/contextual-dpo-v0.1
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- jondurbin/gutenberg-dpo-v0.1
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- jondurbin/py-dpo-v0.1
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- jondurbin/truthy-dpo-v0.1
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- LDJnr/Capybara
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- jondurbin/cinematika-v0.1
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- WizardLM/WizardLM_evol_instruct_70k
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- glaiveai/glaive-function-calling-v2
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- jondurbin/gutenberg-dpo-v0.1
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- grimulkan/LimaRP-augmented
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- lmsys/lmsys-chat-1m
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- ParisNeo/lollms_aware_dataset
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- TIGER-Lab/MathInstruct
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- Muennighoff/natural-instructions
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- openbookqa
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- kingbri/PIPPA-shareGPT
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- piqa
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- Vezora/Tested-22k-Python-Alpaca
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- ropes
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- cakiki/rosetta-code
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- Open-Orca/SlimOrca
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- b-mc2/sql-create-context
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- squad_v2
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- mattpscott/airoboros-summarization
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- migtissera/Synthia-v1.3
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- unalignment/toxic-dpo-v0.2
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- WhiteRabbitNeo/WRN-Chapter-1
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- WhiteRabbitNeo/WRN-Chapter-2
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- winogrande
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exported_from: jondurbin/bagel-dpo-20b-v04
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language:
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- en
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library_name: transformers
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license: other
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license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license
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license_name: internlm2-20b
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quantized_by: mradermacher
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---
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## About
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<!-- ### convert_type: -->
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<!-- ### vocab_type: -->
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weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-dpo-20b-v04
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<!-- provided-files -->
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static quants are available at https://huggingface.co/mradermacher/bagel-dpo-20b-v04-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/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q2_K.gguf) | i1-Q2_K | 8.3 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q3_K_S.gguf) | i1-Q3_K_S | 9.5 | IQ3_XS probably better |
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| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q3_K_M.gguf) | i1-Q3_K_M | 10.5 | IQ3_S probably better |
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| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q4_K_S.gguf) | i1-Q4_K_S | 12.2 | optimal size/speed/quality |
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| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q4_K_M.gguf) | i1-Q4_K_M | 12.8 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q6_K.gguf) | i1-Q6_K | 17.1 | practically like static Q6_K |
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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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![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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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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## 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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