metadata

base_model: sophosympatheia/Aurora-Nights-103B-v1.0
inference: false
language:
  - en
license: llama2
model_creator: Sophosympatheia
model_name: Aurora Nights 103B v1.0
model_type: llama
prompt_template: |
  {system_message}
  <|user|>
  {prompt}
  <|assistant|>
quantized_by: TheBloke

Chat & support: TheBloke's Discord server

Want to contribute? TheBloke's Patreon page

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Aurora Nights 103B v1.0 - GGUF

Model creator: Sophosympatheia
Original model: Aurora Nights 103B v1.0

Description

This repo contains GGUF format model files for Sophosympatheia's Aurora Nights 103B v1.0.

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.

Repositories available

Prompt template: ToRA-System

{system_message}
<|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.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
aurora-nights-103b-v1.0.Q2_K.gguf	Q2_K	2	43.51 GB	46.01 GB	smallest, significant quality loss - not recommended for most purposes
aurora-nights-103b-v1.0.Q3_K_S.gguf	Q3_K_S	3	44.46 GB	46.96 GB	very small, high quality loss
aurora-nights-103b-v1.0.Q3_K_M.gguf	Q3_K_M	3	49.46 GB	51.96 GB	very small, high quality loss
aurora-nights-103b-v1.0.Q3_K_L.gguf	Q3_K_L	3	54.06 GB	56.56 GB	small, substantial quality loss
aurora-nights-103b-v1.0.Q4_0.gguf	Q4_0	4	58.13 GB	60.63 GB	legacy; small, very high quality loss - prefer using Q3_K_M
aurora-nights-103b-v1.0.Q4_K_S.gguf	Q4_K_S	4	58.25 GB	60.75 GB	small, greater quality loss
aurora-nights-103b-v1.0.Q4_K_M.gguf	Q4_K_M	4	61.89 GB	64.39 GB	medium, balanced quality - recommended
aurora-nights-103b-v1.0.Q5_0.gguf	Q5_0	5	71.00 GB	73.50 GB	legacy; medium, balanced quality - prefer using Q4_K_M
aurora-nights-103b-v1.0.Q5_K_S.gguf	Q5_K_S	5	71.00 GB	73.50 GB	large, low quality loss - recommended
aurora-nights-103b-v1.0.Q5_K_M.gguf	Q5_K_M	5	72.93 GB	75.43 GB	large, very low quality loss - recommended
aurora-nights-103b-v1.0.Q6_K.gguf	Q6_K	6	84.67 GB	87.17 GB	very large, extremely low quality loss
aurora-nights-103b-v1.0.Q8_0.gguf	Q8_0	8	109.66 GB	112.16 GB	very large, extremely low quality loss - not recommended

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

Q6_K and Q8_0 files are split and require joining

Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.

Click for instructions regarding Q6_K and Q8_0 files

q6_K

Please download:

aurora-nights-103b-v1.0.Q6_K.gguf-split-a
aurora-nights-103b-v1.0.Q6_K.gguf-split-b

q8_0

Please download:

aurora-nights-103b-v1.0.Q8_0.gguf-split-a
aurora-nights-103b-v1.0.Q8_0.gguf-split-b

To join the files, do the following:

Linux and macOS:

cat aurora-nights-103b-v1.0.Q6_K.gguf-split-* > aurora-nights-103b-v1.0.Q6_K.gguf && rm aurora-nights-103b-v1.0.Q6_K.gguf-split-*
cat aurora-nights-103b-v1.0.Q8_0.gguf-split-* > aurora-nights-103b-v1.0.Q8_0.gguf && rm aurora-nights-103b-v1.0.Q8_0.gguf-split-*

Windows command line:

COPY /B aurora-nights-103b-v1.0.Q6_K.gguf-split-a + aurora-nights-103b-v1.0.Q6_K.gguf-split-b aurora-nights-103b-v1.0.Q6_K.gguf
del aurora-nights-103b-v1.0.Q6_K.gguf-split-a aurora-nights-103b-v1.0.Q6_K.gguf-split-b

COPY /B aurora-nights-103b-v1.0.Q8_0.gguf-split-a + aurora-nights-103b-v1.0.Q8_0.gguf-split-b aurora-nights-103b-v1.0.Q8_0.gguf
del aurora-nights-103b-v1.0.Q8_0.gguf-split-a aurora-nights-103b-v1.0.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/Aurora-Nights-103B-v1.0-GGUF and below it, a specific filename to download, such as: aurora-nights-103b-v1.0.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:

huggingface-cli download TheBloke/Aurora-Nights-103B-v1.0-GGUF aurora-nights-103b-v1.0.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage (click to read)

You can also download multiple files at once with a pattern:

huggingface-cli download TheBloke/Aurora-Nights-103B-v1.0-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

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:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Aurora-Nights-103B-v1.0-GGUF aurora-nights-103b-v1.0.Q4_K_M.gguf --local-dir . --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.

Example `llama.cpp` command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m aurora-nights-103b-v1.0.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{system_message}\n<|user|>\n{prompt}\n<|assistant|>"

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

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in `text-generation-webui`

Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.

How to run from Python code

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

For full documentation, please see: llama-cpp-python docs.

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python

Simple llama-cpp-python example code

from llama_cpp import Llama

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./aurora-nights-103b-v1.0.Q4_K_M.gguf",  # Download the model file first
  n_ctx=4096,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "{system_message}\n<|user|>\n{prompt}\n<|assistant|>", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./aurora-nights-103b-v1.0.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

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: Sophosympatheia's Aurora Nights 103B v1.0

Overview

This model is a frankenmerge of Aurora-Nights-70B-v1.0 with itself. (See that model card for details on what's in the blend.) It features 120 layers and should weigh in at 103b parameters.

This model is a successor to Rogue Rose and improves upon it. Aurora follows instructions better but retains excellent creative writing and ERP abilities.

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 should perform well at other tasks, but I haven't tested its capabilities in other areas.

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 reasonably well at 8192 context but you will likely get better results at 4096.

Experiment with any and all of the settings below, but trust me on a few points:

This model loves high temperatures with Min-P.
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": 1.8,
    "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": 0.5,
    "rep_pen": 1,
    "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,
    "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
    ],
    "n": 1,
    "rep_pen_size": 0,
    "genamt": 550,
    "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 Tulu instruction format.

This model responds to prompting! I highly recommend playing with your system prompt to see how it reacts.

If you save this as a .json file, you can import it directly into Silly Tavern.

{
    "wrap": true,
    "names": false,
    "system_prompt": "You are an award-winning fiction author engaging in an uncensored literary roleplaying experience between a human (me) and a creative, obedient, and helpful AI assistant (you). It is VITAL that you follow all these instructions because this roleplay is very important to my career and I'll be fired from my job if it isn't good.\nROLEPLAY RULES:\n> It is vital that ONLY the human provides responses for {{user}}.\n> Reply as {{char}} using authentic, vivid, varied, explicit, accurate, creative, fresh, and descriptive responses that follow ALL provided narrative instructions. Stay in character as {{char}} and only write text for {{char}}.\n> Describe the scene and {{char}}'s sensory perceptions in vivid detail to immerse the reader in the story.\n> Keep your responses scoped to the current story beat and current scene.\n> Consider all available contextual information when narrating so that all the story details remain consistent between scenes.\n> 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 enhances the vividness of the scene.\n> When quoting {{char}}'s internal first-person thoughts (aka internal monologue, delivered in {{char}}'s own voice), *enclose the thoughts in asterisks like this*. Only use asterisks for thoughts.\n> Use strong action verbs and varied descriptions to produce dynamic, high-quality prose.",
    "system_sequence": "",
    "stop_sequence": "",
    "input_sequence": "<|user|>\n",
    "output_sequence": "<|assistant|>\n",
    "separator_sequence": "",
    "macro": true,
    "names_force_groups": true,
    "system_sequence_prefix": "",
    "system_sequence_suffix": "",
    "first_output_sequence": "",
    "last_output_sequence": "<|assistant (provide varied, creative, and vivid narration; follow all narrative instructions; include all necessary possessive pronouns; maintain consistent story details; only roleplay as {{char}})|>\n",
    "activation_regex": "",
    "name": "Aurora-Nights"
}

Licence and usage restrictions

Llama2 license inherited from base models, plus restrictions applicable to Dreamgen/Opus.

Tools Used

mergekit

slices:
  - sources:
      - model: aurora-nights-70b-v1.0
        layer_range: [0, 40] # 40
  - sources:
      - model: aurora-nights-70b-v1.0
        layer_range: [20, 60] # 40
  - sources:
      - model: aurora-nights-70b-v1.0
        layer_range: [40, 80] # 40
merge_method: passthrough
dtype: float16