VOOZH about

URL: https://huggingface.co/bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF

⇱ bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF · Hugging Face


Llamacpp imatrix Quantizations of Olmo-3.1-32B-Instruct by allenai

Using llama.cpp release b7340 for quantization.

Original model: https://huggingface.co/allenai/Olmo-3.1-32B-Instruct

All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here

Run them in LM Studio

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

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

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

Filename Quant type File Size Split Description
Olmo-3.1-32B-Instruct-bf16.gguf bf16 64.47GB true Full BF16 weights.
Olmo-3.1-32B-Instruct-Q8_0.gguf Q8_0 34.25GB false Extremely high quality, generally unneeded but max available quant.
Olmo-3.1-32B-Instruct-Q6_K_L.gguf Q6_K_L 26.70GB false Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
Olmo-3.1-32B-Instruct-Q6_K.gguf Q6_K 26.45GB false Very high quality, near perfect, recommended.
Olmo-3.1-32B-Instruct-Q5_K_L.gguf Q5_K_L 23.18GB false Uses Q8_0 for embed and output weights. High quality, recommended.
Olmo-3.1-32B-Instruct-Q5_K_M.gguf Q5_K_M 22.86GB false High quality, recommended.
Olmo-3.1-32B-Instruct-Q5_K_S.gguf Q5_K_S 22.24GB false High quality, recommended.
Olmo-3.1-32B-Instruct-Q4_1.gguf Q4_1 20.25GB false Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
Olmo-3.1-32B-Instruct-Q4_K_L.gguf Q4_K_L 19.86GB false Uses Q8_0 for embed and output weights. Good quality, recommended.
Olmo-3.1-32B-Instruct-Q4_K_M.gguf Q4_K_M 19.48GB false Good quality, default size for most use cases, recommended.
Olmo-3.1-32B-Instruct-Q4_K_S.gguf Q4_K_S 18.42GB false Slightly lower quality with more space savings, recommended.
Olmo-3.1-32B-Instruct-Q4_0.gguf Q4_0 18.34GB false Legacy format, offers online repacking for ARM and AVX CPU inference.
Olmo-3.1-32B-Instruct-IQ4_NL.gguf IQ4_NL 18.31GB false Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
Olmo-3.1-32B-Instruct-Q3_K_XL.gguf Q3_K_XL 17.36GB false Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
Olmo-3.1-32B-Instruct-IQ4_XS.gguf IQ4_XS 17.33GB false Decent quality, smaller than Q4_K_S with similar performance, recommended.
Olmo-3.1-32B-Instruct-Q3_K_L.gguf Q3_K_L 16.91GB false Lower quality but usable, good for low RAM availability.
Olmo-3.1-32B-Instruct-Q3_K_M.gguf Q3_K_M 15.60GB false Low quality.
Olmo-3.1-32B-Instruct-IQ3_M.gguf IQ3_M 14.48GB false Medium-low quality, new method with decent performance comparable to Q3_K_M.
Olmo-3.1-32B-Instruct-Q3_K_S.gguf Q3_K_S 14.06GB false Low quality, not recommended.
Olmo-3.1-32B-Instruct-IQ3_XS.gguf IQ3_XS 13.37GB false Lower quality, new method with decent performance, slightly better than Q3_K_S.
Olmo-3.1-32B-Instruct-IQ3_XXS.gguf IQ3_XXS 12.54GB false Lower quality, new method with decent performance, comparable to Q3 quants.
Olmo-3.1-32B-Instruct-Q2_K_L.gguf Q2_K_L 12.51GB false Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
Olmo-3.1-32B-Instruct-Q2_K.gguf Q2_K 12.01GB false Very low quality but surprisingly usable.
Olmo-3.1-32B-Instruct-IQ2_M.gguf IQ2_M 10.97GB false Relatively low quality, uses SOTA techniques to be surprisingly usable.
Olmo-3.1-32B-Instruct-IQ2_S.gguf IQ2_S 10.09GB false Low quality, uses SOTA techniques to be usable.
Olmo-3.1-32B-Instruct-IQ2_XS.gguf IQ2_XS 9.69GB false Low quality, uses SOTA techniques to be usable.
Olmo-3.1-32B-Instruct-IQ2_XXS.gguf IQ2_XXS 8.76GB false Very low quality, uses SOTA techniques to be usable.

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

Downloads last month
1,567
GGUF
Model size
32B params
Architecture
olmo2
Hardware compatibility
Log In to add your hardware

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF

Dataset used to train bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF