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URL: https://huggingface.co/bartowski/TheDrummer_Anubis-70B-v1.1-GGUF

⇱ bartowski/TheDrummer_Anubis-70B-v1.1-GGUF · Hugging Face


Llamacpp imatrix Quantizations of Anubis-70B-v1.1 by TheDrummer

Using llama.cpp release b5753 for quantization.

Original model: https://huggingface.co/TheDrummer/Anubis-70B-v1.1

All quants made using imatrix option with dataset from here

Run them in LM Studio

Run them directly with llama.cpp, or any other llama.cpp based project

Prompt format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Download a file (not the whole branch) from below:

Filename Quant type File Size Split Description
Anubis-70B-v1.1-Q8_0.gguf Q8_0 74.98GB true Extremely high quality, generally unneeded but max available quant.
Anubis-70B-v1.1-Q6_K.gguf Q6_K 57.89GB true Very high quality, near perfect, recommended.
Anubis-70B-v1.1-Q5_K_M.gguf Q5_K_M 49.95GB true High quality, recommended.
Anubis-70B-v1.1-Q5_K_S.gguf Q5_K_S 48.66GB false High quality, recommended.
Anubis-70B-v1.1-Q4_1.gguf Q4_1 44.31GB false Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
Anubis-70B-v1.1-Q4_K_L.gguf Q4_K_L 43.30GB false Uses Q8_0 for embed and output weights. Good quality, recommended.
Anubis-70B-v1.1-Q4_K_M.gguf Q4_K_M 42.52GB false Good quality, default size for most use cases, recommended.
Anubis-70B-v1.1-Q4_K_S.gguf Q4_K_S 40.35GB false Slightly lower quality with more space savings, recommended.
Anubis-70B-v1.1-Q4_0.gguf Q4_0 40.12GB false Legacy format, offers online repacking for ARM and AVX CPU inference.
Anubis-70B-v1.1-IQ4_NL.gguf IQ4_NL 40.05GB false Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
Anubis-70B-v1.1-Q3_K_XL.gguf Q3_K_XL 38.06GB false Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
Anubis-70B-v1.1-IQ4_XS.gguf IQ4_XS 37.90GB false Decent quality, smaller than Q4_K_S with similar performance, recommended.
Anubis-70B-v1.1-Q3_K_L.gguf Q3_K_L 37.14GB false Lower quality but usable, good for low RAM availability.
Anubis-70B-v1.1-Q3_K_M.gguf Q3_K_M 34.27GB false Low quality.
Anubis-70B-v1.1-IQ3_M.gguf IQ3_M 31.94GB false Medium-low quality, new method with decent performance comparable to Q3_K_M.
Anubis-70B-v1.1-Q3_K_S.gguf Q3_K_S 30.91GB false Low quality, not recommended.
Anubis-70B-v1.1-IQ3_XS.gguf IQ3_XS 29.31GB false Lower quality, new method with decent performance, slightly better than Q3_K_S.
Anubis-70B-v1.1-IQ3_XXS.gguf IQ3_XXS 27.47GB false Lower quality, new method with decent performance, comparable to Q3 quants.
Anubis-70B-v1.1-Q2_K_L.gguf Q2_K_L 27.40GB false Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
Anubis-70B-v1.1-Q2_K.gguf Q2_K 26.38GB false Very low quality but surprisingly usable.
Anubis-70B-v1.1-IQ2_M.gguf IQ2_M 24.12GB false Relatively low quality, uses SOTA techniques to be surprisingly usable.
Anubis-70B-v1.1-IQ2_S.gguf IQ2_S 22.24GB false Low quality, uses SOTA techniques to be usable.
Anubis-70B-v1.1-IQ2_XS.gguf IQ2_XS 21.14GB false Low quality, uses SOTA techniques to be usable.
Anubis-70B-v1.1-IQ2_XXS.gguf IQ2_XXS 19.10GB false Very low quality, uses SOTA techniques to be usable.
Anubis-70B-v1.1-IQ1_M.gguf IQ1_M 16.75GB false Extremely low quality, not recommended.

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Downloading using huggingface-cli

ARM/AVX information

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

Which file should I choose?

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

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