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Model: bartowski/kalomaze_Qwen3-16B-A3B-GGUF Source: Original Platform
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---
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quantized_by: bartowski
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pipeline_tag: text-generation
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license: apache-2.0
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base_model_relation: quantized
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base_model: kalomaze/Qwen3-16B-A3B
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---
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## Llamacpp imatrix Quantizations of Qwen3-16B-A3B by kalomaze
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Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5255">b5255</a> for quantization.
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Original model: https://huggingface.co/kalomaze/Qwen3-16B-A3B
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All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
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Run them in [LM Studio](https://lmstudio.ai/)
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Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
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## Prompt format
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```
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<|im_start|>system
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{system_prompt}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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## Download a file (not the whole branch) from below:
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| Filename | Quant type | File Size | Split | Description |
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| -------- | ---------- | --------- | ----- | ----------- |
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| [Qwen3-16B-A3B-bf16.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-bf16.gguf) | bf16 | 32.08GB | false | Full BF16 weights. |
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| [Qwen3-16B-A3B-Q8_0.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q8_0.gguf) | Q8_0 | 17.06GB | false | Extremely high quality, generally unneeded but max available quant. |
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| [Qwen3-16B-A3B-Q6_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q6_K_L.gguf) | Q6_K_L | 13.34GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
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| [Qwen3-16B-A3B-Q6_K.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q6_K.gguf) | Q6_K | 13.19GB | false | Very high quality, near perfect, *recommended*. |
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| [Qwen3-16B-A3B-Q5_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q5_K_L.gguf) | Q5_K_L | 11.62GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
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| [Qwen3-16B-A3B-Q5_K_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q5_K_M.gguf) | Q5_K_M | 11.43GB | false | High quality, *recommended*. |
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| [Qwen3-16B-A3B-Q5_K_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q5_K_S.gguf) | Q5_K_S | 11.11GB | false | High quality, *recommended*. |
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| [Qwen3-16B-A3B-Q4_1.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_1.gguf) | Q4_1 | 10.13GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
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| [Qwen3-16B-A3B-Q4_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_K_L.gguf) | Q4_K_L | 10.06GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
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| [Qwen3-16B-A3B-Q4_K_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_K_M.gguf) | Q4_K_M | 9.83GB | false | Good quality, default size for most use cases, *recommended*. |
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| [Qwen3-16B-A3B-Q4_K_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_K_S.gguf) | Q4_K_S | 9.50GB | false | Slightly lower quality with more space savings, *recommended*. |
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| [Qwen3-16B-A3B-Q4_0.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_0.gguf) | Q4_0 | 9.30GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
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| [Qwen3-16B-A3B-IQ4_NL.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ4_NL.gguf) | IQ4_NL | 9.21GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
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| [Qwen3-16B-A3B-IQ4_XS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ4_XS.gguf) | IQ4_XS | 8.73GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
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| [Qwen3-16B-A3B-Q3_K_XL.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_XL.gguf) | Q3_K_XL | 7.98GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
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| [Qwen3-16B-A3B-Q3_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_L.gguf) | Q3_K_L | 7.71GB | false | Lower quality but usable, good for low RAM availability. |
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| [Qwen3-16B-A3B-Q3_K_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_M.gguf) | Q3_K_M | 7.50GB | false | Low quality. |
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| [Qwen3-16B-A3B-IQ3_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ3_M.gguf) | IQ3_M | 7.50GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
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| [Qwen3-16B-A3B-Q3_K_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_S.gguf) | Q3_K_S | 7.17GB | false | Low quality, not recommended. |
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| [Qwen3-16B-A3B-IQ3_XS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ3_XS.gguf) | IQ3_XS | 6.82GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
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| [Qwen3-16B-A3B-IQ3_XXS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ3_XXS.gguf) | IQ3_XXS | 6.53GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
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| [Qwen3-16B-A3B-Q2_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q2_K_L.gguf) | Q2_K_L | 6.19GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
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| [Qwen3-16B-A3B-Q2_K.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q2_K.gguf) | Q2_K | 5.88GB | false | Very low quality but surprisingly usable. |
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| [Qwen3-16B-A3B-IQ2_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_M.gguf) | IQ2_M | 5.62GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
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| [Qwen3-16B-A3B-IQ2_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_S.gguf) | IQ2_S | 5.01GB | false | Low quality, uses SOTA techniques to be usable. |
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| [Qwen3-16B-A3B-IQ2_XS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_XS.gguf) | IQ2_XS | 4.93GB | false | Low quality, uses SOTA techniques to be usable. |
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| [Qwen3-16B-A3B-IQ2_XXS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_XXS.gguf) | IQ2_XXS | 4.43GB | false | Very low quality, uses SOTA techniques to be usable. |
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## Embed/output weights
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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.
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## Downloading using huggingface-cli
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<details>
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<summary>Click to view download instructions</summary>
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First, make sure you have hugginface-cli installed:
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```
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pip install -U "huggingface_hub[cli]"
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```
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Then, you can target the specific file you want:
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```
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huggingface-cli download bartowski/kalomaze_Qwen3-16B-A3B-GGUF --include "kalomaze_Qwen3-16B-A3B-Q4_K_M.gguf" --local-dir ./
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```
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If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
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```
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huggingface-cli download bartowski/kalomaze_Qwen3-16B-A3B-GGUF --include "kalomaze_Qwen3-16B-A3B-Q8_0/*" --local-dir ./
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```
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You can either specify a new local-dir (kalomaze_Qwen3-16B-A3B-Q8_0) or download them all in place (./)
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</details>
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## ARM/AVX information
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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.
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Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
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As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
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Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) 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.
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<details>
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<summary>Click to view Q4_0_X_X information (deprecated</summary>
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I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
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<details>
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<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
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| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
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| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
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Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
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</details>
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</details>
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## Which file should I choose?
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<details>
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<summary>Click here for details</summary>
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A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
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The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
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If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
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If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
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Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
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If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
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If you want to get more into the weeds, you can check out this extremely useful feature chart:
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[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
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But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
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These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
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</details>
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## Credits
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Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
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Thank you ZeroWw for the inspiration to experiment with embed/output.
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Thank you to LM Studio for sponsoring my work.
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|
||||
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ2_M.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ2_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fd083563153ea6fb0f5f698ed162fd5b0da5a9ce2bdd2e46b7a9901a3f4428a2
|
||||
size 5623537696
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ2_S.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ2_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2fcd16961a021c1074488b5ff28686d28df5b90758e65346fd8ce91035fed131
|
||||
size 5013266464
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ2_XS.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ2_XS.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:173d437a7ccddfd31acc5e09917658433aa5b061d8647bb1990ad818e0323815
|
||||
size 4931332128
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ2_XXS.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ2_XXS.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a49300090b729ad5ac1ccbfc55dadfedcae697cfb3b2e1aaf00a29e6e3ffa4a1
|
||||
size 4428015648
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ3_M.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ3_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f82b82d63327534a037e3149fb5ffcb439c0efa1830e50db5967e9fd562b58c3
|
||||
size 7501969440
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ3_XS.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ3_XS.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1e72377b8358ccf1b09dd1fd51be02d66ac1475af6fba895505d69f0dd83793e
|
||||
size 6822492192
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ3_XXS.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ3_XXS.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a0360194630c0ffe965d2fb19c42397186d26c1c8a474d372bc3420fc247b4b7
|
||||
size 6529507360
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ4_NL.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ4_NL.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fdcebbbc4668b095e38b3b7c49d4b6ea02dd1d776c23cf46e66c056397fcd33c
|
||||
size 9207386144
|
||||
3
kalomaze_Qwen3-16B-A3B-IQ4_XS.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-IQ4_XS.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5edc516a37ca30936a558856da71f497489aee8d3053c96541ca512cdb92058f
|
||||
size 8732094496
|
||||
3
kalomaze_Qwen3-16B-A3B-Q2_K.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q2_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6bde6bfbfccd4326f241435db1863c817e808f6d4d74cadce829135dc788c54e
|
||||
size 5881774112
|
||||
3
kalomaze_Qwen3-16B-A3B-Q2_K_L.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q2_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0ea81b65dfea73757eb9b4d6bc6553886848433d318e1e3747ab70e4316c9de2
|
||||
size 6185646112
|
||||
3
kalomaze_Qwen3-16B-A3B-Q3_K_L.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q3_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:45e0660bef48e0da6464946c66d0c23747d358f6856cd388c1a14bb2c3bca4c5
|
||||
size 7706441760
|
||||
3
kalomaze_Qwen3-16B-A3B-Q3_K_M.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q3_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fba7cea59a334911273abc1bcfd7f43f899d351b32db068e450fc3561ee4caa4
|
||||
size 7502231584
|
||||
3
kalomaze_Qwen3-16B-A3B-Q3_K_S.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q3_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3f6fdd762dd66773930ededa50979e6d6510e9e93c228913971b0c0b23159f02
|
||||
size 7174420512
|
||||
3
kalomaze_Qwen3-16B-A3B-Q3_K_XL.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q3_K_XL.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5451a7199b2741eb0a0da04c59becede71aa2575ab5f9053ed8355db0f937866
|
||||
size 7978711072
|
||||
3
kalomaze_Qwen3-16B-A3B-Q4_0.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q4_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a31e6543197ca9a23c597efda1e6d6451e069a2bddda863e32a393af00446ae0
|
||||
size 9301757984
|
||||
3
kalomaze_Qwen3-16B-A3B-Q4_1.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q4_1.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e952532ce0511a21fb5849cfd85cd0d29f24f1c5a92a6f01e5c88d8f52acc7e0
|
||||
size 10129657888
|
||||
3
kalomaze_Qwen3-16B-A3B-Q4_K_L.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q4_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:dcf9b613387e2e78e896b1bfd8a2f5f6052b23361d89b712f264d77b809eb05a
|
||||
size 10061379616
|
||||
3
kalomaze_Qwen3-16B-A3B-Q4_K_M.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:34c86e1a956349632a05af37a104203823859363f141e1002abe6017349fbdcb
|
||||
size 9830436896
|
||||
3
kalomaze_Qwen3-16B-A3B-Q4_K_S.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q4_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:609ebf791a52262830acfc4f435c5a88b900f0ab8e715932d24aaff2da662d94
|
||||
size 9503608864
|
||||
3
kalomaze_Qwen3-16B-A3B-Q5_K_L.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q5_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bb673308798f4226060fbb97dcf284596d8f8d74ba837aa3fd1a138abef2f4fc
|
||||
size 11624806432
|
||||
3
kalomaze_Qwen3-16B-A3B-Q5_K_M.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q5_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:47eba53909eca4233283dc22352c69d11213215cd648fb053c41b3e9362889f2
|
||||
size 11432759328
|
||||
3
kalomaze_Qwen3-16B-A3B-Q5_K_S.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q5_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b50e43409fd638f3602db5a9eca306f9a6d755ae56ee19dff7b15c535f1a7461
|
||||
size 11108552736
|
||||
3
kalomaze_Qwen3-16B-A3B-Q6_K.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q6_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:cb0740e90f1804a0d7a14b08046441954653d0d73fe2d48c95a88e6e3ed7d229
|
||||
size 13188704288
|
||||
3
kalomaze_Qwen3-16B-A3B-Q6_K_L.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q6_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0ae92e8adad2879abfdcd96dd2b55cc6d7c46b964a2d9a659d7c2be306ec6bda
|
||||
size 13339424800
|
||||
3
kalomaze_Qwen3-16B-A3B-Q8_0.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f18222e4072ee678fef675088ae358cdc0131df67e82a88958aeee04f21d23a1
|
||||
size 17057282080
|
||||
3
kalomaze_Qwen3-16B-A3B-bf16.gguf
Normal file
3
kalomaze_Qwen3-16B-A3B-bf16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9697b4110c47ce9459b4101c42e885e541418f5e5c6d7402ddf12d8312fee3c6
|
||||
size 32079607552
|
||||
3
kalomaze_Qwen3-16B-A3B.imatrix
Normal file
3
kalomaze_Qwen3-16B-A3B.imatrix
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:28ecdf4ec63932325056a2cd410d16ee4f1f32001b519029b64feb89c058b340
|
||||
size 62141824
|
||||
Reference in New Issue
Block a user