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It stands for Importance Matrix, a technique used to improve the quality of quantized models.
The Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance.
One of the benefits of using an Imatrix is that it can lead to better model performance, especially when the calibration data is diverse.
If you want any specific quantization to be added, feel free to ask.
All credits belong to the creator.
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
For --imatrix data, imatrix-Kunoichi-DPO-v2-7B-F16.dat was used.
| Model | MT Bench | EQ Bench | MMLU | Logic Test |
|---|---|---|---|---|
| GPT-4-Turbo | 9.32 | - | - | - |
| GPT-4 | 8.99 | 62.52 | 86.4 | 0.86 |
| Kunoichi-DPO-v2-7B | 8.51 | 42.18 | 64.94 | 0.58 |
| Mixtral-8x7B-Instruct | 8.30 | 44.81 | 70.6 | 0.75 |
| Kunoichi-DPO-7B | 8.29 | 41.60 | 64.83 | 0.59 |
| Kunoichi-7B | 8.14 | 44.32 | 64.9 | 0.58 |
| Starling-7B | 8.09 | - | 63.9 | 0.51 |
| Claude-2 | 8.06 | 52.14 | 78.5 | - |
| Silicon-Maid-7B | 7.96 | 40.44 | 64.7 | 0.54 |
| Loyal-Macaroni-Maid-7B | 7.95 | 38.66 | 64.9 | 0.57 |
| GPT-3.5-Turbo | 7.94 | 50.28 | 70 | 0.57 |
| Claude-1 | 7.9 | - | 77 | - |
| Openchat-3.5 | 7.81 | 37.08 | 64.3 | 0.39 |
| Dolphin-2.6-DPO | 7.74 | 42.88 | 61.9 | 0.53 |
| Zephyr-7B-beta | 7.34 | 38.71 | 61.4 | 0.30 |
| Llama-2-70b-chat-hf | 6.86 | 51.56 | 63 | - |
| Neural-chat-7b-v3-1 | 6.84 | 43.61 | 62.4 | 0.30 |
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| Kunoichi-DPO-7B | 58.4 | 45.08 | 74 | 66.99 | 47.52 |
| Kunoichi-DPO-v2-7B | 58.31 | 44.85 | 75.05 | 65.69 | 47.65 |
| Kunoichi-7B | 57.54 | 44.99 | 74.86 | 63.72 | 46.58 |
| OpenPipe/mistral-ft-optimized-1218 | 56.85 | 44.74 | 75.6 | 59.89 | 47.17 |
| Silicon-Maid-7B | 56.45 | 44.74 | 74.26 | 61.5 | 45.32 |
| mlabonne/NeuralHermes-2.5-Mistral-7B | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 |
| teknium/OpenHermes-2.5-Mistral-7B | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
| openchat/openchat_3.5 | 51.34 | 42.67 | 72.92 | 47.27 | 42.51 |
| berkeley-nest/Starling-LM-7B-alpha | 51.16 | 42.06 | 72.72 | 47.33 | 42.53 |
| HuggingFaceH4/zephyr-7b-beta | 50.99 | 37.33 | 71.83 | 55.1 | 39.7 |
| Model | AlpacaEval2 | Length |
|---|---|---|
| GPT-4 | 23.58% | 1365 |
| GPT-4 0314 | 22.07% | 1371 |
| Mistral Medium | 21.86% | 1500 |
| Mixtral 8x7B v0.1 | 18.26% | 1465 |
| Kunoichi-DPO-v2 | 17.19% | 1785 |
| Claude 2 | 17.19% | 1069 |
| Claude | 16.99% | 1082 |
| Gemini Pro | 16.85% | 1315 |
| GPT-4 0613 | 15.76% | 1140 |
| Claude 2.1 | 15.73% | 1096 |
| Mistral 7B v0.2 | 14.72% | 1676 |
| GPT 3.5 Turbo 0613 | 14.13% | 1328 |
| LLaMA2 Chat 70B | 13.87% | 1790 |
| LMCocktail-10.7B-v1 | 13.15% | 1203 |
| WizardLM 13B V1.1 | 11.23% | 1525 |
| Zephyr 7B Beta | 10.99% | 1444 |
| OpenHermes-2.5-Mistral (7B) | 10.34% | 1107 |
| GPT 3.5 Turbo 0301 | 9.62% | 827 |
| Kunoichi-7B | 9.38% | 1492 |
| GPT 3.5 Turbo 1106 | 9.18% | 796 |
| GPT-3.5 | 8.56% | 1018 |
| Phi-2 DPO | 7.76% | 1687 |
| LLaMA2 Chat 13B | 7.70% | 1513 |
