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

Falcon 180B - GPTQ


This repo contains GPTQ model files for Technology Innovation Institute's Falcon 180B.

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.


Transformers version 4.33.0 is required.

Due to the huge size of the model, the GPTQ has been sharded. This will break compatibility with AutoGPTQ, and therefore any clients/libraries that use AutoGPTQ directly.

But they work great loaded directly through Transformers - and can be served using Text Generation Inference!


Currently these GPTQs are known to work with:

Repositories available

Prompt template: None (base model)


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 Yes 0.1 wikitext 2048 94.25 GB No 4-bit, with Act Order and group size 128g. Higher quality than group_size=None, but also higher VRAM usage.
gptq-4bit--1g-actorder_True 4 None Yes 0.1 wikitext 2048 92.74 GB No 4-bit, with Act Order. No group size, to lower VRAM requirements.
gptq-3bit-128g-actorder_True 3 128 Yes 0.1 wikitext 2048 73.81 GB No 3-bit, so much lower VRAM requirements but worse quality than 4-bit. With group size 128g and act-order. Higher quality than 3bit-128g-False.
gptq-3bit--1g-actorder_True 3 None Yes 0.1 wikitext 2048 70.54 GB No 3-bit, so much lower VRAM requirements but worse quality than 4-bit. With no group size for lowest possible VRAM requirements. Lower quality than 3-bit 128g.

How to download from branches

  • In text-generation-webui, you can add :branch to the end of the download name, eg TheBloke/Falcon-180B-GPTQ:main
  • With Git, you can clone a branch with:
git clone --single-branch --branch main https://huggingface.co/TheBloke/Falcon-180B-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.

NOTE: I have not tested this model with Text Generation Webui. It should work through the Transformers Loader. It will not work through the AutoGPTQ loader, due to the files being sharded.

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/Falcon-180B-GPTQ.
  • To download from a specific branch, enter for example TheBloke/Falcon-180B-GPTQ:gptq-3bit-128g-actorder_True
  • see Provided Files above for the list of branches for each option.
  1. Click Download.
  2. The model will start downloading. Once it's finished it will say "Done".
  3. Choose Loader: Transformers
  4. In the top left, click the refresh icon next to Model.
  5. In the Model dropdown, choose the model you just downloaded: Falcon-180B-GPTQ
  6. The model will automatically load, and is now ready for use!
  7. 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.
  1. 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 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ.

pip3 install transformers>=4.33.0 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

Transformers sample code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/Falcon-180B-GPTQ"

# To use a different branch, change revision
# For example: revision="gptq-3bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, do_sample=True, temperature=0.7, max_new_tokens=512)

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(



The provided files have been tested with Transformers 4.33.0, and TGI 1.0.4.

Because they are sharded, they will not yet via AutoGPTQ. It is hoped support will be added soon.

Note: lack of support for AutoGPTQ doesn't affect your ability to load these models from Python code. It only affects third-party clients that might use AutoGPTQ.

Huggingface Text Generation Inference (TGI) is confirmed working as of version 1.0.4.


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: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper WikieΕ‚, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik BjΓ€reholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Technology Innovation Institute's Falcon 180B

πŸš€ Falcon-180B

Falcon-180B is a 180B parameters causal decoder-only model built by TII and trained on 3,500B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Falcon-180B TII License and Acceptable Use Policy.

Paper coming soon 😊

πŸ€— To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading this great blogpost from HF or this one from the release of the 40B! Note that since the 180B is larger than what can easily be handled with transformers+acccelerate, we recommend using Text Generation Inference.

You will need at least 400GB of memory to swiftly run inference with Falcon-180B.

Why use Falcon-180B?

  • It is the best open-access model currently available, and one of the best model overall. Falcon-180B outperforms LLaMA-2, StableLM, RedPajama, MPT, etc. See the OpenLLM Leaderboard.
  • It features an architecture optimized for inference, with multiquery (Shazeer et al., 2019).
  • It is made available under a permissive license allowing for commercial use.
  • ⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases. If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at Falcon-180B-Chat.

πŸ’Έ Looking for a smaller, less expensive model? Falcon-7B and Falcon-40B are Falcon-180B's little brothers!

πŸ’₯ Falcon LLMs require PyTorch 2.0 for use with transformers!

Model Card for Falcon-180B

Model Details

Model Description

Model Source

  • Paper: coming soon.


See the acceptable use policy.

Direct Use

Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)

Out-of-Scope Use

Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.

Bias, Risks, and Limitations

Falcon-180B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.


We recommend users of Falcon-180B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.

How to Get Started with the Model

To run inference with the model in full bfloat16 precision you need approximately 8xA100 80GB or equivalent.

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "tiiuae/falcon-180b"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
sequences = pipeline(
   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Training Details

Training Data

Falcon-180B was trained on 3,500B tokens of RefinedWeb, a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile (Gao et al., 2020).

Data source Fraction Tokens Sources
RefinedWeb-English 75% 750B massive web crawl
RefinedWeb-Europe 7% 70B European massive web crawl
Books 6% 60B
Conversations 5% 50B Reddit, StackOverflow, HackerNews
Code 5% 50B
Technical 2% 20B arXiv, PubMed, USPTO, etc.

RefinedWeb-Europe is made of the following languages:

Language Fraction of multilingual data Tokens
German 26% 18B
Spanish 24% 17B
French 23% 16B
Italian 7% 5B
Portuguese 4% 3B
Polish 4% 3B
Dutch 4% 3B
Romanian 3% 2B
Czech 3% 2B
Swedish 2% 1B

The data was tokenized with the Falcon tokenizer.

Training Procedure

Falcon-180B was trained on up to 4,096 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=8, DP=64) combined with ZeRO.

Training Hyperparameters

Hyperparameter Value Comment
Precision bfloat16
Optimizer AdamW
Learning rate 1.25e-4 4B tokens warm-up, cosine decay to 1.25e-5
Weight decay 1e-1
Z-loss 1e-4
Batch size 2048 100B tokens ramp-up

Speeds, Sizes, Times

Training started in early 2023.


Paper coming soon.

See the OpenLLM Leaderboard for early results.

Technical Specifications

Model Architecture and Objective

Falcon-180B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).

The architecture is broadly adapted from the GPT-3 paper (Brown et al., 2020), with the following differences:

For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree (so-called multigroup).

Hyperparameter Value Comment
Layers 80
d_model 14848
head_dim 64 Reduced to optimise for FlashAttention
Vocabulary 65024
Sequence length 2048

Compute Infrastructure


Falcon-180B was trained on AWS SageMaker, on up to 4,096 A100 40GB GPUs in P4d instances.


Falcon-180B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)


Paper coming soon 😊 (actually this time). In the meanwhile, you can use the following information to cite:

  title={The Falcon Series of Language Models: Towards Open Frontier Models},
  author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Alhammadi, Maitha and Daniele, Mazzotta and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},

To learn more about the pretraining dataset, see the πŸ““ RefinedWeb paper.

  title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
  author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
  journal={arXiv preprint arXiv:2306.01116},
  eprinttype = {arXiv},



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