mradermacher
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README.md
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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: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-7b-v0.4
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---
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base_model: jondurbin/bagel-7b-v0.4
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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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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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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: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-7b-v0.4
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<!-- provided-files -->
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static quants are available at https://huggingface.co/mradermacher/bagel-7b-v0.4-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-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
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| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | 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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## 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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<!-- end -->
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