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.
These files were quantised using hardware kindly provided by Massed Compute.
This may not be a complete list; if you know of others, please let me know!
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.
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.
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.)
Python code example: inference from this GPTQ model
Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
shell
1pip3 install --upgrade transformers optimum
2# If using PyTorch 2.1 + CUDA 12.x:3pip3 install --upgrade auto-gptq
4# or, if using PyTorch 2.1 + CUDA 11.x:5pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
1from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
23model_name_or_path ="TheBloke/Sensualize-Mixtral-GPTQ"4# To use a different branch, change revision5# For example: revision="gptq-4bit-128g-actorder_True"6model = AutoModelForCausalLM.from_pretrained(model_name_or_path,7 device_map="auto",8 trust_remote_code=False,9 revision="main")1011tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)1213prompt ="Write a story about llamas"14system_message ="You are a story writing assistant"15prompt_template=f'''### Instruction:
16{system_message}1718### Input:
19{prompt}2021### Response:
22'''2324print("\n\n*** Generate:")2526input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()27output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)28print(tokenizer.decode(output[0]))2930# Inference can also be done using transformers' pipeline3132print("*** Pipeline:")33pipe = pipeline(34"text-generation",35 model=model,36 tokenizer=tokenizer,37 max_new_tokens=512,38 do_sample=True,39 temperature=0.7,40 top_p=0.95,41 top_k=40,42 repetition_penalty=1.143)4445print(pipe(prompt_template)[0]['generated_text'])
Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
ExLlama is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
Discord
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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Saofiq's Sensualize Mixtral
Trained using a randomised subset of Full120k - 60K Samples [Roughly 50M Tokens] + More of my own NSFW Instruct & De-Alignment Data [Roughly 30M Tokens Total]
Total Tokens used for Training: 80M over 1 epoch, over 2xA100s at batch size 5, grad 5 for 12 hours.
stops run-off generations after response, important for alpaca
Roleplay based model, specifically the ERP type one.
I mean, its pretty good sometimes? I had various testing versions of Mistral 7B and L2 70B, L2 13B, and even Solar with the same dataset and various learning rates, they did much better. MoE tuning kinda meh still.
about gptisms. It's weird. with certain prompts its never there, with some its there. despite the prose of full120k, I never encountered gptslop with mistral, solar or l2 based trains which was why I was confident about this being good initially.
Mixtral is really finicky. with the right settings this model can shine. I recommend Universal-Light or Universal-Creative in SillyTavern.
