README.md

metadata

license: llama2
tags:
  - llama
  - alpaca
  - cot
  - vicuna
  - uncensored
  - merge
  - mix
model_name: 13B Thorns L2
base_model: CalderaAI/13B-Thorns-l2
inference: false
model_creator: CalderaAI
model_type: llama
prompt_template: >
  Below is an instruction that describes a task. Write a response that
  appropriately completes the request.


  ### Instruction:

  {prompt}


  ### Response:
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)

---

13B Thorns L2 - GPTQ

• Model creator: CalderaAI
• Original model: 13B Thorns L2

Description

This repo contains GPTQ model files for CalderaAI's 13B Thorns L2.

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.

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
• CalderaAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: Alpaca

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

### Instruction:
{prompt}

### Response:

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.

All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the main branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.

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 dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ 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 models in 4-bit.

Branch	Bits	GS	Act Order	Damp %	GPTQ Dataset	Seq Len	Size	ExLlama	Desc
main	4	128	No	0.1	wikitext	4096	7.26 GB	Yes	4-bit, without Act Order and group size 128g.
gptq-4bit-32g-actorder_True	4	32	Yes	0.1	wikitext	4096	8.00 GB	Yes	4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage.
gptq-4bit-64g-actorder_True	4	64	Yes	0.1	wikitext	4096	7.51 GB	Yes	4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy.
gptq-4bit-128g-actorder_True	4	128	Yes	0.1	wikitext	4096	7.26 GB	Yes	4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy.
gptq-8bit--1g-actorder_True	8	None	Yes	0.1	wikitext	4096	13.36 GB	No	8-bit, with Act Order. No group size, to lower VRAM requirements.
gptq-8bit-128g-actorder_True	8	128	Yes	0.1	wikitext	4096	13.65 GB	No	8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy.

How to download from branches

• In text-generation-webui, you can add :branch to the end of the download name, eg TheBloke/13B-Thorns-L2-GPTQ:main
• With Git, you can clone a branch with:

git clone --single-branch --branch main https://huggingface.co/TheBloke/13B-Thorns-L2-GPTQ

• In Python Transformers code, the branch is the revision parameter; see below.

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.

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/13B-Thorns-L2-GPTQ.

• To download from a specific branch, enter for example TheBloke/13B-Thorns-L2-GPTQ:main
• see Provided Files above for the list of branches for each option.

3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: 13B-Thorns-L2-GPTQ
7. The model will automatically load, and is now ready for use!
8. 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.

9. Once you're ready, click the Text Generation tab and enter a prompt to get started!

How to use this GPTQ model from Python code

Install the necessary packages

Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7

If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:

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

For CodeLlama models only: you must use Transformers 4.33.0 or later.

If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:

pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git

You can then use the following code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/13B-Thorns-L2-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
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 = "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:

'''

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 AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with Occ4m's GPTQ-for-LLaMa fork.

ExLlama is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

Huggingface Text Generation Inference (TGI) is compatible with all GPTQ models.

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: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: CalderaAI's 13B Thorns L2

13B-Thorns [An Instruct Based LLaMAv2-13B Ensemble Merge | Alpaca Format]

WARNING - This Model Is Uncensored And Has Not Been Fully Tested For Toxicity. This Is A Research Artifact Intended For Responsible Use. May Generate Offensive And Misleading Content. Do Not Treat Language Sythesized By This Research Artifact As Advice Or As Factual In Any Domain. CalderaAI Strictly Does Not Condone Use Of This Release Outside The Domain Of Research Or Entertainment.

Composition:

13B-Thorns-l2 utilizes a new merge method called Spherical Linear Interpolation. By merging data as a spherical vector store concept, a combined pair of models have a smoother transition between feature spaces that are characteristic of each model, resulting in a more coherent fusion of both model's unique strengths.

Our implementation of Spherical Linear Interpolation for LLM merging: https://github.com/Digitous/LLM-SLERP-Merge

Note: Skip to the TL;DR section for the finalized design this model is comprised of.

Thorns' design is based on the concept of purposed segmentation, in this case we have two-

--Logic Segment (MK1):

Fine-Tuned parent models were hand selected and reviewed for datasets, performance, least restrictive censorship, and community perception of coherence and utility. Ultimately we decided on four models to merge in pairs of two, then combine those offspring for a quad merged logic cluster. All four models were merged using the SLERP method. Yes the name is annoyingly funny. SLERP.

--Creativity and Imagination Segment (MK1):

Flawed first approach (a takeaway on LoRAs);

We then decided the creativity and imagination segment could be as simple as one model, especially if its dataset design, tagging, training quality, and proven track record is above and beyond. KoboldAI's Holodeck model is the result of a dataset that is years of collected, organized, tagged, deduped, and cleaned data. Holodeck alone would be beyond sufficient for the segment we view as the 'subconscious' segment of the model ensemble, however we applied the LIMA RP PEFT to it for extended variety of a different kind. That's where we got carried away. LoRAs offer unique augmentation to model merge possibilities, and the decision was made to take the result of that segment and add two more LoRAs to see if they further extended Holodeck, settling on Kimiko and Janine; two very different RP and conversational LoRAs. This was a bad move, as when we SLERP merged that version of the imagination segment to the logic segment the result was a ranting mess that followed instructions but was the equivalent of a child scribbling all over the place and ignoring obvious chains of logic and a mushy amalgam of awkward creative behavior that had no semblance of coherency. The composite model was slated to be named 13B-Astronomicon; after all the work that went into it and the flatly bland result, the name was abandoned and the next move, which is a byproduct experiment of Astronomicon is what became Thorn.. because this project became a thorn in our side.

Because pain is fun, and persistence in design iteration is the only way forward, we reworked our approach to both segment ensembles following one idea - all three Roleplay and Conversational LoRAs stay no matter what because sure why not add arbitrary rules to the redesign phase at this point.

TL;DR Section

--Finalized Logic and Creativity Segments (MK2):

After a few meetings with our top teams of model hacking memegineers we drafted Thorns MK2, which was prompty fast tracked for production by the Roko's Basilisk Shadow Council.

..Actually I just redid the merge like this:

-Model Merge Ensemble Key-

{} = SLERP Merge | [] = PEFT Merge | () = Composite Model

({({NousHermes+Chronos}[Kimiko])+({Platupus+AiroborosM2.0}[Janine])}{Holodeck[LIMA RP]})

Findings:

-Strategically fusing LoRAs to models that stand to gain the most from them and then merging the result into the ensemble is exceptionally effective.

-Stacking the exact same LoRAs onto one model then merging that into the ensemble results in noisy garbage.

Language Models and LoRAs Used Credits:

All models and adapters used are LLaMAv2-13B.

Models:

Nous-Hermes

Chronos

Platypus

Airoboros

Holodeck

Adapters:

Kimiko

Janine

LIMA RP

Also thanks to Meta for LLaMAv2 and deciding to allow the research community at large to benefit from their incredible work.

Each model and LoRA was hand picked and considered for what it could contribute to this ensemble. Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community.