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Model: bartowski/knoveleng_Open-RS3-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: mit
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inference: true
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base_model_relation: quantized
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base_model: knoveleng/Open-RS3
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datasets:
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- knoveleng/open-rs
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- knoveleng/open-s1
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- knoveleng/open-deepscaler
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---
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## Llamacpp imatrix Quantizations of Open-RS3 by knoveleng
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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/b4925">b4925</a> for quantization.
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Original model: https://huggingface.co/knoveleng/Open-RS3
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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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<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜><think>
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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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| [Open-RS3-bf16.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-bf16.gguf) | bf16 | 3.56GB | false | Full BF16 weights. |
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| [Open-RS3-Q8_0.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q8_0.gguf) | Q8_0 | 1.89GB | false | Extremely high quality, generally unneeded but max available quant. |
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| [Open-RS3-Q6_K_L.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q6_K_L.gguf) | Q6_K_L | 1.58GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
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| [Open-RS3-Q6_K.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q6_K.gguf) | Q6_K | 1.46GB | false | Very high quality, near perfect, *recommended*. |
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| [Open-RS3-Q5_K_L.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q5_K_L.gguf) | Q5_K_L | 1.43GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
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| [Open-RS3-Q5_K_M.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q5_K_M.gguf) | Q5_K_M | 1.29GB | false | High quality, *recommended*. |
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| [Open-RS3-Q4_K_L.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q4_K_L.gguf) | Q4_K_L | 1.29GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
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| [Open-RS3-Q5_K_S.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q5_K_S.gguf) | Q5_K_S | 1.26GB | false | High quality, *recommended*. |
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| [Open-RS3-Q3_K_XL.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q3_K_XL.gguf) | Q3_K_XL | 1.18GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
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| [Open-RS3-Q4_1.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q4_1.gguf) | Q4_1 | 1.16GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
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| [Open-RS3-Q4_K_M.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q4_K_M.gguf) | Q4_K_M | 1.12GB | false | Good quality, default size for most use cases, *recommended*. |
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| [Open-RS3-Q4_K_S.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q4_K_S.gguf) | Q4_K_S | 1.07GB | false | Slightly lower quality with more space savings, *recommended*. |
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| [Open-RS3-Q4_0.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q4_0.gguf) | Q4_0 | 1.07GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
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| [Open-RS3-IQ4_NL.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-IQ4_NL.gguf) | IQ4_NL | 1.07GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
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| [Open-RS3-IQ4_XS.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-IQ4_XS.gguf) | IQ4_XS | 1.02GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
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| [Open-RS3-Q3_K_L.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q3_K_L.gguf) | Q3_K_L | 0.98GB | false | Lower quality but usable, good for low RAM availability. |
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| [Open-RS3-Q2_K_L.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q2_K_L.gguf) | Q2_K_L | 0.98GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
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| [Open-RS3-Q3_K_M.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q3_K_M.gguf) | Q3_K_M | 0.92GB | false | Low quality. |
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| [Open-RS3-IQ3_M.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-IQ3_M.gguf) | IQ3_M | 0.88GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
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| [Open-RS3-Q3_K_S.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q3_K_S.gguf) | Q3_K_S | 0.86GB | false | Low quality, not recommended. |
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| [Open-RS3-IQ3_XS.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-IQ3_XS.gguf) | IQ3_XS | 0.83GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
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| [Open-RS3-IQ3_XXS.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-IQ3_XXS.gguf) | IQ3_XXS | 0.77GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
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| [Open-RS3-Q2_K.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-Q2_K.gguf) | Q2_K | 0.75GB | false | Very low quality but surprisingly usable. |
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| [Open-RS3-IQ2_M.gguf](https://huggingface.co/bartowski/knoveleng_Open-RS3-GGUF/blob/main/knoveleng_Open-RS3-IQ2_M.gguf) | IQ2_M | 0.70GB | false | Relatively low quality, uses SOTA techniques to be surprisingly 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/knoveleng_Open-RS3-GGUF --include "knoveleng_Open-RS3-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/knoveleng_Open-RS3-GGUF --include "knoveleng_Open-RS3-Q8_0/*" --local-dir ./
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```
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You can either specify a new local-dir (knoveleng_Open-RS3-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
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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knoveleng_Open-RS3-IQ2_M.gguf
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knoveleng_Open-RS3-IQ2_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:317dd6391adf5b00e6d50558701ef86665f4ab9acf3d4902ab38bac56a53eb11
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size 701333120
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knoveleng_Open-RS3-IQ3_M.gguf
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knoveleng_Open-RS3-IQ3_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfb233f06e29101a2652efd28fdab202d939d5df6d714688993c3d3f511e3178
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size 876942464
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3
knoveleng_Open-RS3-IQ3_XS.gguf
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knoveleng_Open-RS3-IQ3_XS.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:3479f9fb2e19feef3eb00d1f567661fc966c11f0579d07927fa4aedd6b7cd638
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size 831977600
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3
knoveleng_Open-RS3-IQ3_XXS.gguf
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knoveleng_Open-RS3-IQ3_XXS.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:7566ae7e4255210b1bbe6ddf7bc4623160e678f82f8c39e2082aba8543d942dd
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size 769070720
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knoveleng_Open-RS3-IQ4_NL.gguf
Normal file
3
knoveleng_Open-RS3-IQ4_NL.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0172d5fea2f6314f85a78199f4a9aac29a25141cfeb43b4f3c662f86dd2f7743
|
||||
size 1067604608
|
||||
3
knoveleng_Open-RS3-IQ4_XS.gguf
Normal file
3
knoveleng_Open-RS3-IQ4_XS.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bbb0037c93c01f4bf6ac9992ef9b80ba92c3583d06f1f8ab1438d39c586853df
|
||||
size 1019712128
|
||||
3
knoveleng_Open-RS3-Q2_K.gguf
Normal file
3
knoveleng_Open-RS3-Q2_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:09ffdb77ce8a8ee1509e6669b4b1ebbbb1b51a23456f907d96dd678775d3ad0c
|
||||
size 752881280
|
||||
3
knoveleng_Open-RS3-Q2_K_L.gguf
Normal file
3
knoveleng_Open-RS3-Q2_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3881f3f050baf7feb1f69b239e6224b16d4c1dbd3a9b407e5bccd9999c936abb
|
||||
size 980785280
|
||||
3
knoveleng_Open-RS3-Q3_K_L.gguf
Normal file
3
knoveleng_Open-RS3-Q3_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d9908ce4ecfa019748fafeef6522db1e1519d18e940f5aaba87300d2b17f26e7
|
||||
size 980441216
|
||||
3
knoveleng_Open-RS3-Q3_K_M.gguf
Normal file
3
knoveleng_Open-RS3-Q3_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7eea6253d9ed8a8943c317ae1a84a9d0f5d8e21df1d454e7b884d0b374a6f77f
|
||||
size 924457088
|
||||
3
knoveleng_Open-RS3-Q3_K_S.gguf
Normal file
3
knoveleng_Open-RS3-Q3_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ea86cbae0dd43c7030cf899580f49931fec121bbb4869c1a2a0c98e77ec12889
|
||||
size 861223040
|
||||
3
knoveleng_Open-RS3-Q3_K_XL.gguf
Normal file
3
knoveleng_Open-RS3-Q3_K_XL.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6100a5d8a56374c8ed121c083f86702ecf4a061d6a112ea046fd28fe48f06658
|
||||
size 1184643200
|
||||
3
knoveleng_Open-RS3-Q4_0.gguf
Normal file
3
knoveleng_Open-RS3-Q4_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:cd7ee362b46ff7bc01649b13b6c6e217d3efe9f3602ccbf6dc86099bb770c9af
|
||||
size 1068808832
|
||||
3
knoveleng_Open-RS3-Q4_1.gguf
Normal file
3
knoveleng_Open-RS3-Q4_1.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:37cc59927baa30545d668982c508ef2858ed8bc776db7f20b04940d083c75d7a
|
||||
size 1162701440
|
||||
3
knoveleng_Open-RS3-Q4_K_L.gguf
Normal file
3
knoveleng_Open-RS3-Q4_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2a9dd9e6956a18362a995fbe901807a7b64df572997611f26c1ae2bb85707490
|
||||
size 1290528896
|
||||
3
knoveleng_Open-RS3-Q4_K_M.gguf
Normal file
3
knoveleng_Open-RS3-Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:599ab49d78949e62e37c5e37b0c313626d066ca614020b9b17c2b5bbcf18ea7f
|
||||
size 1117321856
|
||||
3
knoveleng_Open-RS3-Q4_K_S.gguf
Normal file
3
knoveleng_Open-RS3-Q4_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b4e403d9a29a3a6eed516dd2ee6d0bc76c8ac9547bc64a1399ef9989c815a19e
|
||||
size 1071585920
|
||||
3
knoveleng_Open-RS3-Q5_K_L.gguf
Normal file
3
knoveleng_Open-RS3-Q5_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:73980642303010814db1d3f1f1ff169f527dd9a33053db94820be6edbd7a2ae8
|
||||
size 1429530752
|
||||
3
knoveleng_Open-RS3-Q5_K_M.gguf
Normal file
3
knoveleng_Open-RS3-Q5_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a69811a703f9f7307d2291aec8b6b304d3c2d90cbe8e3c66edc4edf2d10d7eeb
|
||||
size 1285495424
|
||||
3
knoveleng_Open-RS3-Q5_K_S.gguf
Normal file
3
knoveleng_Open-RS3-Q5_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:254f1e9d1efb4bffd97d7bc3b0f0b11e86ccb0453d9b9ab649acadf3ff0fa9cb
|
||||
size 1259174528
|
||||
3
knoveleng_Open-RS3-Q6_K.gguf
Normal file
3
knoveleng_Open-RS3-Q6_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a99a86bc83648acb6ddc83890131912d1b1e957f19aa97c79967658881b040bf
|
||||
size 1464179840
|
||||
3
knoveleng_Open-RS3-Q6_K_L.gguf
Normal file
3
knoveleng_Open-RS3-Q6_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a5ec81b9dda9b9103e80490c3157bf846c486f73f8c1c8f352c38d751a07ba81
|
||||
size 1577220224
|
||||
3
knoveleng_Open-RS3-Q8_0.gguf
Normal file
3
knoveleng_Open-RS3-Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:20ee40cc3b001a6a7f7c13b57616432b2c2e297e2589eae34a34cc687bfd5235
|
||||
size 1894533248
|
||||
3
knoveleng_Open-RS3-bf16.gguf
Normal file
3
knoveleng_Open-RS3-bf16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:65a8be0af11b80912b0ce06c786a15a8fe9d3e8f13de73c5cfcdb71c2d03e231
|
||||
size 3560417152
|
||||
BIN
knoveleng_Open-RS3.imatrix
Normal file
BIN
knoveleng_Open-RS3.imatrix
Normal file
Binary file not shown.
Reference in New Issue
Block a user