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Model: bartowski/facebook_KernelLLM-GGUF Source: Original Platform
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README.md
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---
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quantized_by: bartowski
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pipeline_tag: text-generation
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base_model: facebook/KernelLLM
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datasets:
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- ScalingIntelligence/KernelBench
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license: other
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base_model_relation: quantized
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---
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## Llamacpp imatrix Quantizations of KernelLLM by facebook
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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/b5415">b5415</a> for quantization.
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Original model: https://huggingface.co/facebook/KernelLLM
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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_text|><|start_header_id|>system<|end_header_id|>
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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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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| [KernelLLM-bf16.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-bf16.gguf) | bf16 | 16.07GB | false | Full BF16 weights. |
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| [KernelLLM-Q8_0.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q8_0.gguf) | Q8_0 | 8.54GB | false | Extremely high quality, generally unneeded but max available quant. |
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| [KernelLLM-Q6_K_L.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q6_K_L.gguf) | Q6_K_L | 6.85GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
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| [KernelLLM-Q6_K.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q6_K.gguf) | Q6_K | 6.60GB | false | Very high quality, near perfect, *recommended*. |
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| [KernelLLM-Q5_K_L.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q5_K_L.gguf) | Q5_K_L | 6.06GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
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| [KernelLLM-Q5_K_M.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q5_K_M.gguf) | Q5_K_M | 5.73GB | false | High quality, *recommended*. |
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| [KernelLLM-Q5_K_S.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q5_K_S.gguf) | Q5_K_S | 5.60GB | false | High quality, *recommended*. |
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| [KernelLLM-Q4_K_L.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q4_K_L.gguf) | Q4_K_L | 5.31GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
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| [KernelLLM-Q4_1.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q4_1.gguf) | Q4_1 | 5.13GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
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| [KernelLLM-Q4_K_M.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q4_K_M.gguf) | Q4_K_M | 4.92GB | false | Good quality, default size for most use cases, *recommended*. |
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| [KernelLLM-Q3_K_XL.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q3_K_XL.gguf) | Q3_K_XL | 4.78GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
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| [KernelLLM-Q4_K_S.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q4_K_S.gguf) | Q4_K_S | 4.69GB | false | Slightly lower quality with more space savings, *recommended*. |
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| [KernelLLM-Q4_0.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q4_0.gguf) | Q4_0 | 4.68GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
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| [KernelLLM-IQ4_NL.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-IQ4_NL.gguf) | IQ4_NL | 4.68GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
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| [KernelLLM-IQ4_XS.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-IQ4_XS.gguf) | IQ4_XS | 4.45GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
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| [KernelLLM-Q3_K_L.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q3_K_L.gguf) | Q3_K_L | 4.32GB | false | Lower quality but usable, good for low RAM availability. |
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| [KernelLLM-Q3_K_M.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q3_K_M.gguf) | Q3_K_M | 4.02GB | false | Low quality. |
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| [KernelLLM-IQ3_M.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-IQ3_M.gguf) | IQ3_M | 3.78GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
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| [KernelLLM-Q2_K_L.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q2_K_L.gguf) | Q2_K_L | 3.69GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
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| [KernelLLM-Q3_K_S.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q3_K_S.gguf) | Q3_K_S | 3.66GB | false | Low quality, not recommended. |
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| [KernelLLM-IQ3_XS.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-IQ3_XS.gguf) | IQ3_XS | 3.52GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
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| [KernelLLM-IQ3_XXS.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
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| [KernelLLM-Q2_K.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-Q2_K.gguf) | Q2_K | 3.18GB | false | Very low quality but surprisingly usable. |
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| [KernelLLM-IQ2_M.gguf](https://huggingface.co/bartowski/facebook_KernelLLM-GGUF/blob/main/facebook_KernelLLM-IQ2_M.gguf) | IQ2_M | 2.95GB | 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/facebook_KernelLLM-GGUF --include "facebook_KernelLLM-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/facebook_KernelLLM-GGUF --include "facebook_KernelLLM-Q8_0/*" --local-dir ./
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```
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You can either specify a new local-dir (facebook_KernelLLM-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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1
configuration.json
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1
configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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3
facebook_KernelLLM-IQ2_M.gguf
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facebook_KernelLLM-IQ2_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:3814a6d40f22c1e597217c57b44db2c1999e5551ffb9baa93a80f0304bf359c1
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size 2948281856
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3
facebook_KernelLLM-IQ3_M.gguf
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facebook_KernelLLM-IQ3_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:aa645b5f6aac7ba1cd114008758378c1299f73a36d79faf7be608601fe003b67
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size 3784824320
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3
facebook_KernelLLM-IQ3_XS.gguf
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facebook_KernelLLM-IQ3_XS.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:fa71cb847f32a0fa80a6004eee382798a1e22c23f2e99f131fd4a07b7842646e
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size 3518748160
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3
facebook_KernelLLM-IQ3_XXS.gguf
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3
facebook_KernelLLM-IQ3_XXS.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:714d3e8c7d3a28ce7f6ddc93756f7865bf84a367c9b5f03c9b6a1dbd4cc37bed
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size 3274913280
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3
facebook_KernelLLM-IQ4_NL.gguf
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facebook_KernelLLM-IQ4_NL.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:750d40479d6b7692644fba7cc36b2e4289b24619edfa6c8e91ac72045427e8fe
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size 4677989888
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3
facebook_KernelLLM-IQ4_XS.gguf
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3
facebook_KernelLLM-IQ4_XS.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:8a8acf3c958fbce0ff1d70773428b53601e531d0cb294f5197e2a302a5dfce79
|
||||
size 4447663616
|
||||
3
facebook_KernelLLM-Q2_K.gguf
Normal file
3
facebook_KernelLLM-Q2_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:daff053fb63beec29a5fbfb6846e5d5a4a90233fd07ec9325131f98ca8e2d5ea
|
||||
size 3179132416
|
||||
3
facebook_KernelLLM-Q2_K_L.gguf
Normal file
3
facebook_KernelLLM-Q2_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c3db85e15b59942cc2b8b06479150d67385a93fd4bad3ca68580b41436c3e69f
|
||||
size 3692156416
|
||||
3
facebook_KernelLLM-Q3_K_L.gguf
Normal file
3
facebook_KernelLLM-Q3_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b01f41067e36e2212c8d330e0d9d3c3ae89f6f2011b76f2dcb82efff71056f05
|
||||
size 4321957376
|
||||
3
facebook_KernelLLM-Q3_K_M.gguf
Normal file
3
facebook_KernelLLM-Q3_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ecf65c5e824b4f1e324dae674eeac5fe0b679eeed989b6841a1a218627413bff
|
||||
size 4018918912
|
||||
3
facebook_KernelLLM-Q3_K_S.gguf
Normal file
3
facebook_KernelLLM-Q3_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:858abbde580be8fcb8474f474abbe13a85279693f7bfe32a105508bf41205cec
|
||||
size 3664500224
|
||||
3
facebook_KernelLLM-Q3_K_XL.gguf
Normal file
3
facebook_KernelLLM-Q3_K_XL.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:79297d66831245d079d678a327b22806a9828a15caef7e548b776ba8daedf70e
|
||||
size 4781626880
|
||||
3
facebook_KernelLLM-Q4_0.gguf
Normal file
3
facebook_KernelLLM-Q4_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:812e5b17e8bc151d7d445613b61d1c2bb37429db655c9ac41cfeb5cfd2acb772
|
||||
size 4675892736
|
||||
3
facebook_KernelLLM-Q4_1.gguf
Normal file
3
facebook_KernelLLM-Q4_1.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b6a51e1ea381edfbd5cf780f9badf1acf96c2357fa428171924b2e88033f12c3
|
||||
size 5130253824
|
||||
3
facebook_KernelLLM-Q4_K_L.gguf
Normal file
3
facebook_KernelLLM-Q4_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bae3d41eba0a0518731c7b25f10d93800e8b839359980c40be24c48be90d5a51
|
||||
size 5310633472
|
||||
3
facebook_KernelLLM-Q4_K_M.gguf
Normal file
3
facebook_KernelLLM-Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:947e1f4d48d23bf9a71984b98de65204858ec4e58990c17ef6195dc64838e6d7
|
||||
size 4920735232
|
||||
3
facebook_KernelLLM-Q4_K_S.gguf
Normal file
3
facebook_KernelLLM-Q4_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:47ee185ca11b7d9c2560037d2697b3848b19fac2c2613e8352af1a12d98305f8
|
||||
size 4692669952
|
||||
3
facebook_KernelLLM-Q5_K_L.gguf
Normal file
3
facebook_KernelLLM-Q5_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ee1fd8da0a880778ba623b691e6ba566cc85ba29bf43e6bc159f85415023efce
|
||||
size 6057219584
|
||||
3
facebook_KernelLLM-Q5_K_M.gguf
Normal file
3
facebook_KernelLLM-Q5_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c35292f02d33becc3f0c893f295e47c8708e52296f58c3ea270f940d6b03cf6e
|
||||
size 5732988416
|
||||
3
facebook_KernelLLM-Q5_K_S.gguf
Normal file
3
facebook_KernelLLM-Q5_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:144d6e6fbd26ebcd35de280d2339dc0f9e3daeb647b6490e65d2518bb87f8ee3
|
||||
size 5599294976
|
||||
3
facebook_KernelLLM-Q6_K.gguf
Normal file
3
facebook_KernelLLM-Q6_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9f616059a41b47d8c4e66d989cb5487790662ccd1205ce239f082646ea3fff02
|
||||
size 6596007424
|
||||
3
facebook_KernelLLM-Q6_K_L.gguf
Normal file
3
facebook_KernelLLM-Q6_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0206a46fd944fe53949067eb578a0bf1ec9f040a3251d568d3267993b1567c75
|
||||
size 6850467328
|
||||
3
facebook_KernelLLM-Q8_0.gguf
Normal file
3
facebook_KernelLLM-Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fb95873802c041b6aaca7354dcf2a47a221a1f9d52492cf9fe3ad909afa5f28d
|
||||
size 8540771840
|
||||
3
facebook_KernelLLM-bf16.gguf
Normal file
3
facebook_KernelLLM-bf16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7f5d77379bb4cb288ea9659a4860ab668c2cb8b9df21971920af382ff602fec8
|
||||
size 16068891904
|
||||
BIN
facebook_KernelLLM.imatrix
Normal file
BIN
facebook_KernelLLM.imatrix
Normal file
Binary file not shown.
Reference in New Issue
Block a user