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URL: https://huggingface.co/bartowski/AvelonLabs_OpenClaude-1.7B-Merged-GGUF

⇱ bartowski/AvelonLabs_OpenClaude-1.7B-Merged-GGUF · Hugging Face


Llamacpp imatrix Quantizations of OpenClaude-1.7B-Merged by AvelonLabs

Using llama.cpp release b9518 for quantization.

Original model: https://huggingface.co/AvelonLabs/OpenClaude-1.7B-Merged

All quants made using imatrix option with dataset from here

Run them in your choice of tools:

Note: if it's a newly supported model, you may need to wait for an update from the developers.

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
OpenClaude-1.7B-Merged-bf16.gguf bf16 3.45GB false Full BF16 weights.
OpenClaude-1.7B-Merged-Q8_0.gguf Q8_0 1.83GB false Extremely high quality, generally unneeded but max available quant.
OpenClaude-1.7B-Merged-Q6_K_L.gguf Q6_K_L 1.56GB false Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
OpenClaude-1.7B-Merged-Q6_K.gguf Q6_K 1.48GB false Very high quality, near perfect, recommended.
OpenClaude-1.7B-Merged-Q5_K_L.gguf Q5_K_L 1.36GB false Uses Q8_0 for embed and output weights. High quality, recommended.
OpenClaude-1.7B-Merged-Q5_K_M.gguf Q5_K_M 1.29GB false High quality, recommended.
OpenClaude-1.7B-Merged-Q5_K_S.gguf Q5_K_S 1.24GB false High quality, recommended.
OpenClaude-1.7B-Merged-Q4_K_L.gguf Q4_K_L 1.22GB false Uses Q8_0 for embed and output weights. Good quality, recommended.
OpenClaude-1.7B-Merged-Q4_1.gguf Q4_1 1.15GB false Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
OpenClaude-1.7B-Merged-Q4_K_M.gguf Q4_K_M 1.14GB false Good quality, default size for most use cases, recommended.
OpenClaude-1.7B-Merged-Q3_K_XL.gguf Q3_K_XL 1.09GB false Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
OpenClaude-1.7B-Merged-Q4_K_S.gguf Q4_K_S 1.08GB false Slightly lower quality with more space savings, recommended.
OpenClaude-1.7B-Merged-Q4_0.gguf Q4_0 1.07GB false Legacy format, offers online repacking for ARM and AVX CPU inference.
OpenClaude-1.7B-Merged-IQ4_NL.gguf IQ4_NL 1.07GB false Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
OpenClaude-1.7B-Merged-IQ4_XS.gguf IQ4_XS 1.03GB false Decent quality, smaller than Q4_K_S with similar performance, recommended.
OpenClaude-1.7B-Merged-Q3_K_L.gguf Q3_K_L 1.01GB false Lower quality but usable, good for low RAM availability.
OpenClaude-1.7B-Merged-Q3_K_M.gguf Q3_K_M 0.97GB false Low quality.
OpenClaude-1.7B-Merged-IQ3_M.gguf IQ3_M 0.93GB false Medium-low quality, new method with decent performance comparable to Q3_K_M.
OpenClaude-1.7B-Merged-Q3_K_S.gguf Q3_K_S 0.89GB false Low quality, not recommended.
OpenClaude-1.7B-Merged-Q2_K_L.gguf Q2_K_L 0.88GB false Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
OpenClaude-1.7B-Merged-IQ3_XS.gguf IQ3_XS 0.87GB false Lower quality, new method with decent performance, slightly better than Q3_K_S.
OpenClaude-1.7B-Merged-Q2_K.gguf Q2_K 0.81GB false Very low quality but surprisingly usable.
OpenClaude-1.7B-Merged-IQ3_XXS.gguf IQ3_XXS 0.79GB false Lower quality, new method with decent performance, comparable to Q3 quants.
OpenClaude-1.7B-Merged-IQ2_M.gguf IQ2_M 0.77GB false Relatively low quality, uses SOTA techniques to be surprisingly 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

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