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LLaMA2-13B-TiefighterLR-GGUF

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GGUF
llama
text-generation-inference
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TheBlokeAI

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

Llama2 13B TiefighterLR - GGUF

Description

This repo contains GGUF format model files for KoboldAI's Llama2 13B TiefighterLR.

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.
  • LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
  • 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.
  • ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
  • 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.

Repositories available

Prompt template: Alpaca 

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

### Instruction:
{prompt}

### Response:

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
llama2-13b-tiefighterlr.Q2_K.gguf Q2_K 2 5.43 GB 7.93 GB smallest, significant quality loss - not recommended for most purposes
llama2-13b-tiefighterlr.Q3_K_S.gguf Q3_K_S 3 5.66 GB 8.16 GB very small, high quality loss
llama2-13b-tiefighterlr.Q3_K_M.gguf Q3_K_M 3 6.34 GB 8.84 GB very small, high quality loss
llama2-13b-tiefighterlr.Q3_K_L.gguf Q3_K_L 3 6.93 GB 9.43 GB small, substantial quality loss
llama2-13b-tiefighterlr.Q4_0.gguf Q4_0 4 7.37 GB 9.87 GB legacy; small, very high quality loss - prefer using Q3_K_M
llama2-13b-tiefighterlr.Q4_K_S.gguf Q4_K_S 4 7.41 GB 9.91 GB small, greater quality loss
llama2-13b-tiefighterlr.Q4_K_M.gguf Q4_K_M 4 7.87 GB 10.37 GB medium, balanced quality - recommended
llama2-13b-tiefighterlr.Q5_0.gguf Q5_0 5 8.97 GB 11.47 GB legacy; medium, balanced quality - prefer using Q4_K_M
llama2-13b-tiefighterlr.Q5_K_S.gguf Q5_K_S 5 8.97 GB 11.47 GB large, low quality loss - recommended
llama2-13b-tiefighterlr.Q5_K_M.gguf Q5_K_M 5 9.23 GB 11.73 GB large, very low quality loss - recommended
llama2-13b-tiefighterlr.Q6_K.gguf Q6_K 6 10.68 GB 13.18 GB very large, extremely low quality loss
llama2-13b-tiefighterlr.Q8_0.gguf Q8_0 8 13.83 GB 16.33 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.

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/LLaMA2-13B-TiefighterLR-GGUF and below it, a specific filename to download, such as: llama2-13b-tiefighterlr.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/LLaMA2-13B-TiefighterLR-GGUF llama2-13b-tiefighterlr.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage

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

huggingface-cli download TheBloke/LLaMA2-13B-TiefighterLR-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/LLaMA2-13B-TiefighterLR-GGUF llama2-13b-tiefighterlr.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 32 -m llama2-13b-tiefighterlr.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"

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.

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 here: text-generation-webui/docs/llama.cpp.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

How to load this model in Python code, using ctransformers

First install the package

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

# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers

Simple ctransformers example code 

from ctransformers import AutoModelForCausalLM

# 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 = AutoModelForCausalLM.from_pretrained("TheBloke/LLaMA2-13B-TiefighterLR-GGUF", model_file="llama2-13b-tiefighterlr.Q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))

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.

Special thanks to: Aemon Algiz.

Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: KoboldAI's Llama2 13B TiefighterLR

LLaMA2-13B-TiefighterLR

TiefighterLR is a merged model achieved trough merging two different lora's on top of a well established existing merge. This LR version contains Less Rodeo, merged at 3% from the original 5% reducing its second person adventure bias. Testers found this model to understand your own character and instruction prompts better, at the sacrifice of lowering its own writing bias/style.

To achieve this the following recipe was used:

  • We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merge, contrary to the name this model does not have a strong NSFW bias.
  • Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model.
  • After applying this lora we merged the original model with the newly created PocketDoc/Dans-RetroRodeo-13b merge at 3% to weaken the newly introduced adventure bias.
  • The resulting merge was used as a new base model to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%.

This means this model contains the following ingredients from their upstream models for as far as we can track them:

  • Undi95/Xwin-MLewd-13B-V0.2
    • Undi95/ReMM-S-Light (base/private)
    • Undi95/CreativeEngine
    • Brouz/Slerpeno
      • elinas/chronos-13b-v2
      • jondurbin/airoboros-l2-13b-2.1
      • NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2 LORA
      • CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2 LORA
        • KoboldAI/LLAMA2-13B-Holodeck-1
        • NousResearch/Nous-Hermes-13b
        • OpenAssistant/llama2-13b-orca-8k-3319
      • ehartford/WizardLM-1.0-Uncensored-Llama2-13b
      • Henk717/spring-dragon
    • The-Face-Of-Goonery/Huginn-v3-13b
    • zattio770/120-Days-of-LORA-v2-13B
    • PygmalionAI/pygmalion-2-13b
    • Undi95/StoryTelling
    • TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter
    • nRuaif/Kimiko-v2-13B
    • The-Face-Of-Goonery/Huginn-13b-FP16
    • lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
    • Xwin-LM/Xwin-LM-13B-V0.2
  • PocketDoc/Dans-RetroRodeo-13b
  • Blackroot/Llama-2-13B-Storywriter-LORA

Usage

This model is meant to be creative, If you let it improvise you get better results than if you drown it in details.

Story Writing

Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story.

Generate a story on demand

To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong.

Chatbots and persona's

Unlike the original Tiefighter this model is better at handling existing Character Cards as long as they do not contain a lot of second person writing or second person introductions (You), setting > as a custom stop sequence can help fix potential mistakes, as well as turning multi-line replies off. You can also use instructions to create your characters.

For example, you can put this in memory in regular chat mode:

### Instruction:
Generate a conversation between Alice and Henk where they discuss language models.
In this conversation Henk is excited to teach Alice about Tiefighter.
### Response:

Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format.

Instruct Prompting

This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work.

During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias.

Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up.

Adventuring and Adventure Games

This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode).

It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns.

Discovered something cool and want to engage with us?

Join our community at https://koboldai.org/discord !

This model would not be possible without the awesome work from:

Undi95, PocketDoc, Blackroot, Brouz, The Face of Goonery, zattio770, PygmalionAI, TokenBender, nRuaif, lemonilia and Xwin-LM.

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