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README.md
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This repo contains GPTQ model files for [Microsoft's Phi 2](https://huggingface.co/microsoft/phi-2).
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**NOTE** These GPTQs will only work with Transformers, and require `trust_remote_code=True`.
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Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
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<!-- description end -->
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## Repositories available
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/phi-2-GPTQ)
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* [Microsoft's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/microsoft/phi-2)
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<!-- repositories-available end -->
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Each separate quant is in a different branch. See below for instructions on fetching from different branches.
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<details>
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<summary>Explanation of GPTQ parameters</summary>
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| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
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| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
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| main | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 1.84 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
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| gptq-4bit-32g-actorder_True | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 1.98 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
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| gptq-8bit--1g-actorder_True | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.05 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
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| gptq-8bit-128g-actorder_True | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.10 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
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| gptq-8bit-32g-actorder_True | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.28 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
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| gptq-4bit-64g-actorder_True | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 1.89 GB | No | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
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<!-- README_GPTQ.md-provided-files end -->
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Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
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**NOTE** Will only work with `Loader: Transformers`, and requires `trust_remote_code=True`
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It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
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1. Click the **Model tab**.
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This repo contains GPTQ model files for [Microsoft's Phi 2](https://huggingface.co/microsoft/phi-2).
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Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
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<!-- description end -->
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## Repositories available
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/phi-2-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/phi-2-GGUF)
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* [Microsoft's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/microsoft/phi-2)
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<!-- repositories-available end -->
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Each separate quant is in a different branch. See below for instructions on fetching from different branches.
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Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
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<details>
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<summary>Explanation of GPTQ parameters</summary>
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| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
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| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
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| [main](https://huggingface.co/TheBloke/phi-2-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 1.84 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
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| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/phi-2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 1.98 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
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| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/phi-2-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.05 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
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| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/phi-2-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.10 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
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| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/phi-2-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.28 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
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| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/phi-2-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 1.89 GB | No | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
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<!-- README_GPTQ.md-provided-files end -->
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Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
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It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
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1. Click the **Model tab**.
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