đ Dolphin 2.5 Mixtral 8X7B - GPTQ
This repository offers GPTQ model files for Eric Hartford's Dolphin 2.5 Mixtral 8X7B, providing multiple quantisation options for different hardware and requirements.
đĻ Model Information
Property |
Details |
Base Model |
ehartford/dolphin-2.5-mixtral-8x7b |
Datasets |
ehartford/dolphin, jondurbin/airoboros-2.2.1, ehartford/dolphin-coder, migtissera/Synthia-v1.3, teknium/openhermes, ise-uiuc/Magicoder-OSS-Instruct-75K, ise-uiuc/Magicoder-Evol-Instruct-110K, LDJnr/Pure-Dove |
Inference |
false |
Language |
en |
License |
apache-2.0 |
Model Creator |
Eric Hartford |
Model Name |
Dolphin 2.5 Mixtral 8X7B |
Model Type |
mixtral |
Prompt Template |
'< |
Quantized By |
TheBloke |
đ Repositories available
đŦ Prompt template: ChatML
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
đĨī¸ Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
Mixtral GPTQs currently have special requirements - see Description above.
đ Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
Explanation of GPTQ parameters
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as
desc_act
. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
Branch |
Bits |
GS |
Act Order |
Damp % |
GPTQ Dataset |
Seq Len |
Size |
ExLlama |
Desc |
main |
4 |
None |
Yes |
0.1 |
VMware Open Instruct |
8192 |
23.81 GB |
No |
4-bit, with Act Order. No group size, to lower VRAM requirements. |
gptq-4bit-128g-actorder_True |
4 |
128 |
Yes |
0.1 |
VMware Open Instruct |
8192 |
24.70 GB |
No |
4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
gptq-4bit-32g-actorder_True |
4 |
32 |
Yes |
0.1 |
VMware Open Instruct |
8192 |
27.42 GB |
No |
4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
gptq-3bit--1g-actorder_True |
3 |
None |
Yes |
0.1 |
VMware Open Instruct |
8192 |
18.01 GB |
No |
3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
gptq-3bit-128g-actorder_True |
3 |
128 |
Yes |
0.1 |
VMware Open Instruct |
8192 |
18.85 GB |
No |
3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
gptq-3bit-32g-actorder_True |
3 |
32 |
Yes |
0.1 |
VMware Open Instruct |
8192 |
21.43 GB |
No |
3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
gptq-8bit--1g-actorder_True |
8 |
None |
Yes |
0.1 |
VMware Open Instruct |
8192 |
47.04 GB |
No |
8-bit, with Act Order. No group size, to lower VRAM requirements. |
gptq-8bit-128g-actorder_True |
8 |
128 |
Yes |
0.1 |
VMware Open Instruct |
8192 |
48.10 GB |
No |
8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
âŦī¸ How to download, including from branches
In text-generation-webui
To download from the main
branch, enter TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ
in the "Download model" box.
To download from another branch, add :branchname
to the end of the download name, eg TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ:gptq-4bit-128g-actorder_True
From the command line
I recommend using the huggingface-hub
Python library:
pip3 install huggingface-hub
To download the main
branch to a folder called dolphin-2.5-mixtral-8x7b-GPTQ
:
mkdir dolphin-2.5-mixtral-8x7b-GPTQ
huggingface-cli download TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ --local-dir dolphin-2.5-mixtral-8x7b-GPTQ --local-dir-use-symlinks False
To download from a different branch, add the --revision
parameter:
mkdir dolphin-2.5-mixtral-8x7b-GPTQ
huggingface-cli download TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir dolphin-2.5-mixtral-8x7b-GPTQ --local-dir-use-symlinks False
More advanced huggingface-cli download usage
If you remove the --local-dir-use-symlinks False
parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: ~/.cache/huggingface
), and symlinks will be added to the specified --local-dir
, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the HF_HOME
environment variable, and/or the --cache-dir
parameter to huggingface-cli
.
For more documentation on downloading with huggingface-cli
, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer
:
pip3 install hf_transfer
And set environment variable HF_HUB_ENABLE_HF_TRANSFER
to 1
:
mkdir dolphin-2.5-mixtral-8x7b-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ --local-dir dolphin-2.5-mixtral-8x7b-GPTQ --local-dir-use-symlinks False
Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1
before the download command.
With git
(not recommended)
To clone a specific branch with git
, use a command like this:
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ
Note that using Git with HF repos is strongly discouraged. It will be much slower than using huggingface-hub
, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the .git
folder as a blob.)
đ How to easily download and use this model in text-generation-webui
NOTE: Requires:
- Transformers 4.36.0, or Transformers 4.37.0.dev0 from Github
- Either AutoGPTQ 0.6 compiled from source and
Loader: AutoGPTQ
,
- or,
Loader: Transformers
, if you installed Transformers from Github: pip3 install git+https://github.com/huggingface/transformers
Please make sure you're using the latest version of text-generation-webui.
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.
-
Click the Model tab.
-
Under Download custom model or LoRA, enter TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ
.
- To download from a specific branch, enter for example
TheBloke/dolphin-2.5-mixtral-8x7b-GPTQ:gptq-4bit-128g-actorder_True
- see Provided Files above for the list of branches for each