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---
base_model: internlm/internlm2-20b
datasets:
- ai2_arc
- allenai/ultrafeedback_binarized_cleaned
- argilla/distilabel-intel-orca-dpo-pairs
- jondurbin/airoboros-3.2
- codeparrot/apps
- facebook/belebele
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- camel-ai/biology
- camel-ai/chemistry
- camel-ai/math
- camel-ai/physics
- jondurbin/contextual-dpo-v0.1
- jondurbin/gutenberg-dpo-v0.1
- jondurbin/py-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- WizardLM/WizardLM_evol_instruct_70k
- glaiveai/glaive-function-calling-v2
- jondurbin/gutenberg-dpo-v0.1
- grimulkan/LimaRP-augmented
- lmsys/lmsys-chat-1m
- ParisNeo/lollms_aware_dataset
- TIGER-Lab/MathInstruct
- Muennighoff/natural-instructions
- openbookqa
- kingbri/PIPPA-shareGPT
- piqa
- Vezora/Tested-22k-Python-Alpaca
- ropes
- cakiki/rosetta-code
- Open-Orca/SlimOrca
- b-mc2/sql-create-context
- squad_v2
- mattpscott/airoboros-summarization
- migtissera/Synthia-v1.3
- unalignment/toxic-dpo-v0.2
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/WRN-Chapter-2
- winogrande
exported_from: jondurbin/bagel-dpo-20b-v04
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license
license_name: internlm2-20b
quantized_by: mradermacher
---
## About
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weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-dpo-20b-v04
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static quants are available at https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
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