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Model Overview
Model Features
Model Capabilities
Use Cases
🚀 Xwin-LM 13B V0.1 - GPTQ
This repository offers GPTQ model files for Xwin-LM's Xwin-LM 13B V0.1, aiming to provide users with efficient and high - quality language model inference capabilities.
📚 Documentation
Model Information
| Property | Details |
|---|---|
| Model Type | Llama |
| Model Creator | Xwin-LM |
| Original Model | Xwin-LM 13B V0.1 |
| License | Llama2 |
| Quantized By | TheBloke |
Repositories available
- AWQ model(s) for GPU inference.
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8 - bit GGUF models for CPU+GPU inference
- Xwin-LM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Vicuna
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:
Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, allowing you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. All recent GPTQ files are made with AutoGPTQ, and all files in non - main branches are made with AutoGPTQ. Files in the main branch which were uploaded before August 2023 were made with GPTQ - for - LLaMa.
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 models in 4 - bit.
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
|---|---|---|---|---|---|---|---|---|---|
| main | 4 | 128 | Yes | 0.1 | wikitext | 4096 | 7.26 GB | Yes | 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 | wikitext | 4096 | 8.00 GB | Yes | 4 - bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| gptq-8bit--1g-actorder_True | 8 | None | Yes | 0.1 | wikitext | 4096 | 13.36 GB | No | 8 - bit, with Act Order. No group size, to lower VRAM requirements. |
| gptq-8bit-128g-actorder_True | 8 | 128 | Yes | 0.1 | wikitext | 4096 | 13.65 GB | No | 8 - bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| gptq-8bit-32g-actorder_True | 8 | 32 | Yes | 0.1 | wikitext | 4096 | 14.54 GB | No | 8 - bit, with group size 32g and Act Order for maximum inference quality. |
💻 Usage Examples
Downloading the model
In text - generation - webui
To download from the main branch, enter TheBloke/Xwin-LM-13B-V0.1-GPTQ in the "Download model" box. To download from another branch, add :branchname to the end of the download name, e.g., TheBloke/Xwin-LM-13B-V0.1-GPTQ:gptq-4bit-32g-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 Xwin-LM-13B-V0.1-GPTQ:
mkdir Xwin-LM-13B-V0.1-GPTQ
huggingface-cli download TheBloke/Xwin-LM-13B-V0.1-GPTQ --local-dir Xwin-LM-13B-V0.1-GPTQ --local-dir-use-symlinks False
To download from a different branch, add the --revision parameter:
mkdir Xwin-LM-13B-V0.1-GPTQ
huggingface-cli download TheBloke/Xwin-LM-13B-V0.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Xwin-LM-13B-V0.1-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 Huggingface 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 Xwin-LM-13B-V0.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Xwin-LM-13B-V0.1-GPTQ --local-dir Xwin-LM-13B-V0.1-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-32g-actorder_True https://huggingface.co/TheBloke/Xwin-LM-13B-V0.1-GPTQ
Using in text - generation - webui
- Click the Model tab.
- Under Download custom model or LoRA, enter
TheBloke/Xwin-LM-13B-V0.1-GPTQ.
- To download from a specific branch, enter for example
TheBloke/Xwin-LM-13B-V0.1-GPTQ:gptq-4bit-32g-actorder_True - see Provided Files above for the list of branches for each option.
- Click Download.
- The model will start downloading. Once it's finished it will say "Done".
- In the top left, click the refresh icon next to Model.
- In the Model dropdown, choose the model you just downloaded:
Xwin-LM-13B-V0.1-GPTQ - The model will automatically load, and is now ready for use!
- If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file
quantize_config.json.
- Once you're ready, click the Text Generation tab and enter a prompt to get started!
Using from Python code
Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
If you have problems installing AutoGPTQ using the pre - built wheels, install it from source instead:
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
Use the following code
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Xwin-LM-13B-V0.1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:
'''
⚠️ Important Note
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.gitfolder as a blob.)
💡 Usage Tip
If you have problems installing AutoGPTQ using the pre - built wheels, install it from source instead.
Transformers Transformers Supports Multiple Languages