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Loyal Macaroni Maid 7B - GPTQ

Model creator: Sanji Watsuki
Original model: Loyal Macaroni Maid 7B

Description

This repo contains GPTQ model files for Sanji Watsuki's Loyal Macaroni Maid 7B.

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.

Repositories available

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Known compatible clients / servers

GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.

These GPTQ models are known to work in the following inference servers/webuis.

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.

Branch	Bits	GS	Act Order	Damp %	GPTQ Dataset	Seq Len	Size	ExLlama	Desc
main	4	128	Yes	0.1	OpenErotica Erotiquant	4096	4.16 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	OpenErotica Erotiquant	4096	4.57 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	OpenErotica Erotiquant	4096	7.52 GB	No	8-bit, with Act Order. No group size, to lower VRAM requirements.
gptq-8bit-128g-actorder_True	8	128	Yes	0.1	OpenErotica Erotiquant	4096	7.68 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	OpenErotica Erotiquant	4096	8.17 GB	No	8-bit, with group size 32g and Act Order for maximum inference quality.
gptq-4bit-64g-actorder_True	4	64	Yes	0.1	OpenErotica Erotiquant	4096	4.29 GB	Yes	4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy.

How to download, including from branches

In text-generation-webui

To download from the main branch, enter TheBloke/Loyal-Macaroni-Maid-7B-GPTQ in the "Download model" box.

To download from another branch, add :branchname to the end of the download name, eg TheBloke/Loyal-Macaroni-Maid-7B-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 Loyal-Macaroni-Maid-7B-GPTQ:

mkdir Loyal-Macaroni-Maid-7B-GPTQ
huggingface-cli download TheBloke/Loyal-Macaroni-Maid-7B-GPTQ --local-dir Loyal-Macaroni-Maid-7B-GPTQ --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir Loyal-Macaroni-Maid-7B-GPTQ
huggingface-cli download TheBloke/Loyal-Macaroni-Maid-7B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Loyal-Macaroni-Maid-7B-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 Loyal-Macaroni-Maid-7B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Loyal-Macaroni-Maid-7B-GPTQ --local-dir Loyal-Macaroni-Maid-7B-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/Loyal-Macaroni-Maid-7B-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.)

How to easily download and use this model in text-generation-webui

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/Loyal-Macaroni-Maid-7B-GPTQ.
- To download from a specific branch, enter for example TheBloke/Loyal-Macaroni-Maid-7B-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: Loyal-Macaroni-Maid-7B-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!

Serving this model from Text Generation Inference (TGI)

It's recommended to use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0

Example Docker parameters:

--model-id TheBloke/Loyal-Macaroni-Maid-7B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096

Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):

pip3 install huggingface-hub

from huggingface_hub import InferenceClient

endpoint_url = "https://your-endpoint-url-here"

prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

client = InferenceClient(endpoint_url)
response = client.text_generation(
  prompt_template,
  max_new_tokens=128,
  do_sample=True,
  temperature=0.7,
  top_p=0.95,
  top_k=40,
  repetition_penalty=1.1
)

print(f"Model output: {response}")

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.

pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 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:

pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .

Example Python code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/Loyal-Macaroni-Maid-7B-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 = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(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

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon: https://patreon.com/TheBlokeAI
Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Sanji Watsuki's Loyal Macaroni Maid 7B

Description

This repository hosts quantized GGUF files for Loyal-Macaroni-Maid-7B, a 7B model aimed at having engaging RP with solid character card adherence and being a smart cookie at the same time.

In my limited testing, it's a great RP model suitable for RP/ERP with sharp reasoning skills for a 7B. I expect it to both benchmark well and be a very suitable model for general use.

Prompt template: Custom format, or Alpaca

Custom format:

I found the best SillyTavern results from using the Noromaid template.

SillyTavern config files: Context, Instruct. Additionally, here is my Text Completion preset

Otherwise, I tried to ensure that most of the underlying merged models were Alpaca-ish.

Alpaca:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Helpful Tips

For SFW RP, I found that I got the most use out of this model when I had an RPG Narrator in a group chat with the characters I wanted to RP with. Here is an importable character card for the best RPG Narrator I found thus far.

For basic ChatGPT tasks, here is the basic Assistant card that I use. I found it works best with Default context template / Alpaca instruct template in Silly Tavern.

Frankenstein's Merger

tl;dr: This is a bunch of model merger slop with a bunch of RP cherries on top.

I'll keep it a buck - I'm not a fan of this model's composition. Based on my testing, it seemed like models that were built from a merger of OpenChat-3.5/Starling and NeuralChat v3.1 had surprisingly good character card coherence for a 7B model - better than either one in isolation. This is validated both in my personal benchmarks as well as the Ayumi NSFW ERP ALC-IQ3 metric which rates character card coherence and is dominated by OpenNeuralChat mergers for small models.

The issue is... prompt format.

OpenChat-3.5 uses an abomination of a prompt format with "GPT4 Correct User/Assistant" all over it in a ChatML-style prompt with extra tokens for padding and end-of-turn. NeuralChat v3.1 uses a weird Alpaca-like format with "### System/User/Assistant" all over it. Almost every RP finetune standardized on Alpaca or an expanded Alpaca with janky multi-turn prompting (since Alpaca doesn't have multi-turn prompting).

Most model mergers like Q-bert/MetaMath-Cybertron-Starling just slam them together and toss the extra ChatML tokens, resulting in a half-Alpaca-like half-ChatML-like Frankenstein's monster. For the most part, using Alpaca as the lingua franca just kinda works but there are exceptions that can make a generation go off the rails. I found this to be a bit of an issue in certain SillyTavern test cases.

Regardless, the strong Chat Arena performances from 7B models continues to lead me to believe they're the strongest base for an all-purpose model.

The sauce (All You Need is DARE)

tl;dr: It's an OpenChat/NeuralChat merger with as much RP as possible stuffed in using the DARE TIES merger method.

This model is a DARE TIES merger between Toten5/Marcoroni-neural-chat-7B-v2, chargoddard/loyal-piano-m7, Undi95/Toppy-M-7B, NeverSleep/Noromaid-7b-v0.2, and athirdpath/NSFW_DPO_vmgb-7b on top of a mistralai/Mistral-7B-v0.1 base.

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: Toten5/Marcoroni-neural-chat-7B-v2
    parameters:
      weight: 0.3
      density: 0.8
  - model: chargoddard/loyal-piano-m7
    parameters:
      weight: 0.4
      density: 0.8
  - model: Undi95/Toppy-M-7B
    parameters:
      weight: 0.2
      density: 0.4
  - model: NeverSleep/Noromaid-7b-v0.2
    parameters:
      weight: 0.2
      density: 0.4
  - model: athirdpath/NSFW_DPO_vmgb-7b
    parameters:
      weight: 0.2
      density: 0.4
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

There's a lot to unpack here. I went with DARE TIES because it appeared to be a viable way to combine information into models without losing smarts. Directly SLERPing a smart cookie model with an ERP brained model will often dilute both the model's smarts and RPing ability. This is an attempt to have my cookie and eat it, too.

First, there are two high density high weight models:

chargoddard/loyal-piano-m7 is the easy primary model choice. It's an Alpaca prompt format model that scores highly, is very creative for a 7B, and is primarily trained on RP data.

Toten5/Marcoroni-neural-chat-7B-v2 is the unintuitive second model pick. It is a merger of mergers that chains back to being an OpenChat/NeuralChat merger being SLERPed back into NeuralChat a second time. Despite SLERPing NeuralChat in multiple times, it retains its high benchmark scores. I opted to pick this model as my base because I believed it was the OpenChat/NeuralChat model that benchmarked well that was closest to the O.G. NeuralChat which has the most Alpaca-like prompt.

By picking a density of 0.8, these models have a 96% chance of showing up for any TIE merger. This should ensure that there is a solid "base" of deltas from the base Mistral model that captures most of what makes these models good. High density with 0.3-0.4 weights have been shown in mergers like jan-hq/supermario-v2

Next, there are 3 RP models merged in with medium density. Undi95/Toppy-M-7B, NeverSleep/Noromaid-7b-v0.2, and athirdpath/NSFW_DPO_vmgb-7b. Toppy-M-7B is an easy pick for being a well regarded 7B RP model - although, it is a merger of many mergers which might dilute its effectiveness as a lower density merge. NeverSleep/Noromaid-7b-v0.2 pulls in the unique private Noromaid RP dataset. Finally, athirdpath/NSFW_DPO_vmgb-7b is another Frankenstein OpenNeuralChat merger that happens to be DPOed on athirdpath's NSFW Alpaca pairs which seemed like another good RP addition to the model (plus, maybe it tilts it to being more Alpaca-flavored, idk).

By picking a density of 0.4, these models should largely impart some of their flavor onto the merger. I suspect the density could go even lower and the models could be used even more like a LoRA-like merger on top.

The DARE TIES merger is intentionally overweight and non-normalized at 1.3 total weight. I intentionally went overweight to try and better capture the individual characteristics from the various models. With wide mergers, a weight of 1.0 can often become incoherent like jan-hq/supermario-v1.

Putting it all together, ~60% of the model is "base models" like OpenChat/NeuralChat/Loyal-Piano-M7. ~40% of the model is effectively me trying to extract RP information from existing RP models. The only non-RP model is the Marcoroni base which means that almost 80% of this model is intended for RP.

Not that the benchmarks matter, but if this merger works right, it'll be a high benchmarking 7B that is both smart and strong at RP.

TheBloke
/

Loyal-Macaroni-Maid-7B-GPTQ

Loyal Macaroni Maid 7B - GPTQ

Description

Repositories available

Prompt template: Alpaca

Known compatible clients / servers

Provided files, and GPTQ parameters

How to download, including from branches

In text-generation-webui

From the command line

With `git` (not recommended)

How to easily download and use this model in text-generation-webui

Serving this model from Text Generation Inference (TGI)

Python code example: inference from this GPTQ model

Install the necessary packages

Example Python code

Compatibility

Discord

Thanks, and how to contribute

Original model card: Sanji Watsuki's Loyal Macaroni Maid 7B

Description

Prompt template: Custom format, or Alpaca

Custom format:

Alpaca:

Helpful Tips

Frankenstein's Merger

The sauce (All You Need is DARE)

Model tree for TheBloke/Loyal-Macaroni-Maid-7B-GPTQ

Loyal Macaroni Maid 7B - GPTQ

Description

Repositories available

Prompt template: Alpaca

Known compatible clients / servers

Provided files, and GPTQ parameters

How to download, including from branches

In text-generation-webui

From the command line

With git (not recommended)

How to easily download and use this model in text-generation-webui

Serving this model from Text Generation Inference (TGI)

Python code example: inference from this GPTQ model

Install the necessary packages

Example Python code

Compatibility

Discord

Thanks, and how to contribute

Original model card: Sanji Watsuki's Loyal Macaroni Maid 7B

Description

Prompt template: Custom format, or Alpaca

Custom format:

Alpaca:

Helpful Tips

Frankenstein's Merger

The sauce (All You Need is DARE)

Model tree for TheBloke/Loyal-Macaroni-Maid-7B-GPTQ

With `git` (not recommended)