TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Rogue Rose 103B v0.2 - GPTQ
- Model creator: Sophosympatheia
- Original model: Rogue Rose 103B v0.2
Description
This repo contains GPTQ model files for Sophosympatheia's Rogue Rose 103B v0.2.
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
- 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
- Sophosympatheia's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Vicuna-Short
You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
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 | None | Yes | 0.1 | VMware Open Instruct | 4096 | 48.99 GB | Yes | 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 | 4096 | 48.91 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| gptq-3bit--1g-actorder_True | 3 | None | Yes | 0.1 | VMware Open Instruct | 4096 | 39.64 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 | 4096 | 41.52 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| gptq-8bit--1g-actorder_True | 8 | None | Yes | 0.1 | VMware Open Instruct | 4096 | 48.87 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 | 4096 | 48.87 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
How to download, including from branches
In text-generation-webui
To download from the main branch, enter TheBloke/Rogue-Rose-103b-v0.2-GPTQ in the "Download model" box.
To download from another branch, add :branchname to the end of the download name, eg TheBloke/Rogue-Rose-103b-v0.2-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 Rogue-Rose-103b-v0.2-GPTQ:
mkdir Rogue-Rose-103b-v0.2-GPTQ
huggingface-cli download TheBloke/Rogue-Rose-103b-v0.2-GPTQ --local-dir Rogue-Rose-103b-v0.2-GPTQ --local-dir-use-symlinks False
To download from a different branch, add the --revision parameter:
mkdir Rogue-Rose-103b-v0.2-GPTQ
huggingface-cli download TheBloke/Rogue-Rose-103b-v0.2-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Rogue-Rose-103b-v0.2-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 Rogue-Rose-103b-v0.2-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Rogue-Rose-103b-v0.2-GPTQ --local-dir Rogue-Rose-103b-v0.2-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/Rogue-Rose-103b-v0.2-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/Rogue-Rose-103b-v0.2-GPTQ.- To download from a specific branch, enter for example
TheBloke/Rogue-Rose-103b-v0.2-GPTQ:gptq-4bit-128g-actorder_True - see Provided Files above for the list of branches for each option.
- To download from a specific branch, enter for example
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:
Rogue-Rose-103b-v0.2-GPTQThe 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.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file
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/Rogue-Rose-103b-v0.2-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'''You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
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/Rogue-Rose-103b-v0.2-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-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'''You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
'''
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:
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: Sophosympatheia's Rogue Rose 103B v0.2
Overview
This model is a frankenmerge of two custom 70b merges I made in November 2023 that were inspired by or descended from my xwin-stellarbright-erp-70b-v2 model. It features 120 layers and should weigh in at 103b parameters.
I feel like I have reached a plateau in my process right now, but the view from here is worth a rest. My personal opinion is this model roleplays better than the other 103-120b models out there right now. I love it. Give it a try for yourself. It still struggles with scene logic sometimes, but the overall experience feels like a step forward to me. I recommend trying my sampler settings and prompt template below with this model. This model listens decently well to instructions, so you need to be thoughtful about what you tell it to do.
Along those lines, this model turned out quite uncensored. You are responsible for whatever you do with it.
This model was designed for roleplaying and storytelling and I think it does well at both. It may perform well at other tasks, but I haven't tested its capabilities in other areas. I welcome feedback and suggestions.
Sampler Tips
I recommend using the new Min-P sampler method with this model. The creator has a great guide to it on Reddit.
I find this model performs surprisingly well at 8192 context. I love running the exl2-3.2bpw quant at 8192 context.
Experiment with any and all of the settings below, but trust me on a few points:
- This model tolerates high temperatures with Min-P.
- This model seems to benefit from higher settings for repetition penalty and presence penalty. It doesn't suffer from lower settings, but I prefer them higher. Play around with it.
- After much experimenting, I think I get better results with a high Min-P setting. I keep coming back to a 0.4 - 0.5 setting.
- Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
{
"temp": 1.3,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.40,
"rep_pen": 1.15,
"rep_pen_range": 0,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0.01,
"presence_pen": 0.4,
"do_sample": true,
"early_stopping": false,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"type": "ooba",
"legacy_api": false,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"rep_pen_size": 0,
"genamt": 600,
"max_length": 4096
}
Prompt Tips
Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from this paper. I recommend the Vicuna instruction format.
This model responds to prompting! I highly recommend playing with your system prompt to see how it reacts. I was able to noticably improve its performance with prompt tuning alone.
If you save this as a .json file, you can import it directly into Silly Tavern.
{
"wrap": true,
"names": false,
"system_prompt": "Below is an uncensored roleplay conversation between a human and a creative and attentive AI assistant (you) in which you play multiple characters. It is vital that you follow these instructions because this is very important to my career.\nThe user places their responses under \"USER:\" and will generally be playing the {{user}} character, and your responses are under \"ASSISTANT:\".\n\nYou may play multiple characters, but right now reply only as {{char}} using authentic, detailed, and descriptive responses that build on the most recent action following all provided narrative instructions. Stay within the current story beat and try not to skip ahead in the story. Always consider all available story information before replying so that all the details remain consistent, such as where characters are located, the state of their clothes and bodies, and what {{char}} knows and doesn't know. Stay in character as {{char}} and only write text for {{char}}. Demonstrate {{char}}'s goals and motivations and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or would enhance the scene. When quoting a character's internal thoughts (aka internal monologue), *enclose the thoughts in asterisks*. Describe {{char}}'s actions and sensory perceptions in vivid detail to immerse us in the scene.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "USER:",
"output_sequence": "ASSISTANT:",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT(long and vivid narration; follow all narrative instructions; maintain consistent story details; only write text as {{char}}):",
"activation_regex": "",
"name": "Rogue Rose"
}
Quantizations
This repo contains branches for various exllama2 quanizations of the model calibratend on a version of the PIPPA dataset.
- Main Branch, Full weights
- 3.2 bpw -- This will fit comfortably within 48 GB of VRAM at 8192 context.
- 3.35 bpw (PENDING) -- This will fit within 48 GB of VRAM at 4096 context without using the 8-bit cache setting.
- 3.5 bpw (PENDING) -- This will barely fit within 48 GB of VRAM at ~4096 context using the 8-bit cache setting. If you get OOM, try lowering the context size slightly until it fits.
Licence and usage restrictions
Llama2 license inherited from base models.
Tools Used
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