These files were quantised using hardware kindly provided by Massed Compute.
About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
Explanation of quantisation methods
Click to see details
The new methods available are:
GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
COPY /B rogue-rose-103b-v0.2.Q6_K.gguf-split-a + rogue-rose-103b-v0.2.Q6_K.gguf-split-b rogue-rose-103b-v0.2.Q6_K.gguf
del rogue-rose-103b-v0.2.Q6_K.gguf-split-a rogue-rose-103b-v0.2.Q6_K.gguf-split-b
COPY /B rogue-rose-103b-v0.2.Q8_0.gguf-split-a + rogue-rose-103b-v0.2.Q8_0.gguf-split-b rogue-rose-103b-v0.2.Q8_0.gguf
del rogue-rose-103b-v0.2.Q8_0.gguf-split-a rogue-rose-103b-v0.2.Q8_0.gguf-split-b
How to download GGUF files
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
LM Studio
LoLLMS Web UI
Faraday.dev
In text-generation-webui
Under Download Model, you can enter the model repo: TheBloke/Rogue-Rose-103b-v0.2-GGUF and below it, a specific filename to download, such as: rogue-rose-103b-v0.2.Q4_K_M.gguf.
Then click Download.
On the command line, including multiple files at once
I recommend using the huggingface-hub Python library:
pip3 install huggingface-hub
Then you can download any individual model file to the current directory, at high speed, with a command like this:
Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.
Example llama.cpp command
Make sure you are using llama.cpp from commit d0cee0d or later.
./main -ngl 35 -m rogue-rose-103b-v0.2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:"
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change -c 4096 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
How to load this model in Python code, using llama-cpp-python
Run one of the following commands, according to your system:
shell
1# Base ctransformers with no GPU acceleration2pip install llama-cpp-python
3# With NVidia CUDA acceleration4CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
5# Or with OpenBLAS acceleration6CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
7# Or with CLBLast acceleration8CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
9# Or with AMD ROCm GPU acceleration (Linux only)10CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
11# Or with Metal GPU acceleration for macOS systems only12CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
1314# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:15$env:CMAKE_ARGS ="-DLLAMA_OPENBLAS=on"16pip install llama-cpp-python
Simple llama-cpp-python example code
python
1from llama_cpp import Llama
23# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.4llm = Llama(5 model_path="./rogue-rose-103b-v0.2.Q4_K_M.gguf",# Download the model file first6 n_ctx=4096,# The max sequence length to use - note that longer sequence lengths require much more resources7 n_threads=8,# The number of CPU threads to use, tailor to your system and the resulting performance8 n_gpu_layers=35# The number of layers to offload to GPU, if you have GPU acceleration available9)1011# Simple inference example12output = llm(13"You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:",# Prompt14 max_tokens=512,# Generate up to 512 tokens15 stop=["</s>"],# Example stop token - not necessarily correct for this specific model! Please check before using.16 echo=True# Whether to echo the prompt17)1819# Chat Completion API2021llm = Llama(model_path="./rogue-rose-103b-v0.2.Q4_K_M.gguf", chat_format="llama-2")# Set chat_format according to the model you are using22llm.create_chat_completion(23 messages =[24{"role":"system","content":"You are a story writing assistant."},25{26"role":"user",27"content":"Write a story about llamas."28}29]30)
How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
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 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
RogueRose
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.
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.
