![TheBlokeAI](https://i.imgur.com/EBdldam.jpg)
TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Airoboros 13B GPTQ 4bit
These files are GPTQ 4bit model files for Jon Durbin's Airoboros 13B.
It is the result of quantising to 4bit using GPTQ-for-LLaMa.
Other repositories available
- 4bit GPTQ models for GPU inference
- Unquantised model in HF fp16 format
- latimar's GGML models for CPU (+CUDA) inference
How to easily download and use this model in text-generation-webui
Open the text-generation-webui UI as normal.
- Click the Model tab.
- Under Download custom model or LoRA, enter
TheBloke/Airoboros-13B-GPTQ
. - Click Download.
- Wait until it says it's finished downloading.
- Click the Refresh icon next to Model in the top left.
- In the Model drop-down: choose the model you just downloaded,
Airoboros-13B-GPTQ
. - If you see an error in the bottom right, ignore it - it's temporary.
- Fill out the
GPTQ parameters
on the right:Bits = 4
,Groupsize = 128
,model_type = Llama
- Click Save settings for this model in the top right.
- Click Reload the Model in the top right.
- Once it says it's loaded, click the Text Generation tab and enter a prompt!
Provided files
Compatible file - Airoboros-13B-GPTQ-4bit-128g.no-act-order.safetensors
In the main
branch - the default one - you will find Airoboros-13B-GPTQ-4bit-128g.no-act-order.safetensors
This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility.
It was created without the --act-order
parameter to ensure full compatibility.
wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors
- Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
- Works with AutoGPTQ.
- Works with text-generation-webui one-click-installers
- Parameters: Groupsize = 128. No act-order.
- Command used to create the GPTQ:
python llama.py /workspace/models/jondurbin_airoboros-13b wikitext2 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/jon-13b/gptq/Airoboros-13B-GPTQ-4bit-128g.no-act-order.safetensors
Discord
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Thanks, and how to contribute.
Thanks to the chirper.ai team!
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: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Airoboros-13B original model card
Overview
This is a fine-tuned 13b parameter LlaMa model, using completely synthetic training data created by https://github.com/jondurbin/airoboros
Eval (gpt4 judging)
model | raw score | gpt-3.5 adjusted score |
---|---|---|
airoboros-13b | 17947 | 98.087 |
gpt35 | 18297 | 100.0 |
gpt4-x-alpasta-30b | 15612 | 85.33 |
manticore-13b | 15856 | 86.66 |
vicuna-13b-1.1 | 16306 | 89.12 |
wizard-vicuna-13b-uncensored | 16287 | 89.01 |
individual question scores, with shareGPT links (200 prompts generated by gpt-4)
wb-13b-u is Wizard-Vicuna-13b-Uncensored
airoboros-13b | gpt35 | gpt4-x-alpasta-30b | manticore-13b | vicuna-13b-1.1 | wv-13b-u | link |
---|---|---|---|---|---|---|
80 | 95 | 70 | 90 | 85 | 60 | eval |
20 | 95 | 40 | 30 | 90 | 80 | eval |
100 | 100 | 100 | 95 | 95 | 100 | eval |
90 | 100 | 85 | 60 | 95 | 100 | eval |
95 | 90 | 80 | 85 | 95 | 75 | eval |
100 | 95 | 90 | 95 | 98 | 92 | eval |
50 | 100 | 80 | 95 | 60 | 55 | eval |
70 | 90 | 80 | 60 | 85 | 40 | eval |
100 | 95 | 50 | 85 | 40 | 60 | eval |
85 | 60 | 55 | 65 | 50 | 70 | eval |
95 | 100 | 85 | 90 | 60 | 75 | eval |
100 | 95 | 70 | 80 | 50 | 85 | eval |
100 | 95 | 80 | 70 | 60 | 90 | eval |
95 | 100 | 70 | 85 | 90 | 90 | eval |
80 | 95 | 90 | 60 | 30 | 85 | eval |
60 | 95 | 0 | 75 | 50 | 40 | eval |
100 | 95 | 90 | 98 | 95 | 95 | eval |
60 | 85 | 40 | 50 | 20 | 0 | eval |
100 | 90 | 85 | 95 | 95 | 80 | eval |
100 | 95 | 100 | 95 | 90 | 95 | eval |
95 | 90 | 96 | 80 | 92 | 88 | eval |
95 | 92 | 90 | 93 | 89 | 91 | eval |
95 | 93 | 90 | 94 | 96 | 92 | eval |
95 | 90 | 93 | 88 | 92 | 85 | eval |
95 | 90 | 85 | 96 | 88 | 92 | eval |
95 | 95 | 90 | 93 | 92 | 91 | eval |
95 | 98 | 80 | 97 | 99 | 96 | eval |
95 | 93 | 90 | 87 | 92 | 89 | eval |
90 | 85 | 95 | 80 | 92 | 75 | eval |
90 | 85 | 95 | 93 | 80 | 92 | eval |
95 | 92 | 90 | 91 | 93 | 89 | eval |
100 | 95 | 90 | 85 | 80 | 95 | eval |
95 | 97 | 93 | 92 | 96 | 94 | eval |
95 | 93 | 94 | 90 | 88 | 92 | eval |
90 | 95 | 98 | 85 | 96 | 92 | eval |
90 | 88 | 85 | 80 | 82 | 84 | eval |
90 | 95 | 85 | 87 | 92 | 88 | eval |
95 | 97 | 96 | 90 | 93 | 92 | eval |
95 | 93 | 92 | 90 | 89 | 91 | eval |
90 | 95 | 93 | 92 | 94 | 91 | eval |
90 | 85 | 95 | 80 | 88 | 75 | eval |
85 | 90 | 95 | 88 | 92 | 80 | eval |
90 | 95 | 92 | 85 | 80 | 87 | eval |
85 | 90 | 95 | 80 | 88 | 75 | eval |
85 | 80 | 75 | 90 | 70 | 82 | eval |
90 | 85 | 95 | 92 | 93 | 80 | eval |
90 | 95 | 75 | 85 | 80 | 70 | eval |
85 | 90 | 80 | 88 | 82 | 83 | eval |
85 | 90 | 95 | 92 | 88 | 80 | eval |
85 | 90 | 80 | 75 | 95 | 88 | eval |
85 | 90 | 80 | 88 | 84 | 92 | eval |
80 | 90 | 75 | 85 | 70 | 95 | eval |
90 | 88 | 85 | 80 | 92 | 83 | eval |
85 | 75 | 90 | 80 | 78 | 88 | eval |
85 | 90 | 80 | 82 | 75 | 88 | eval |
90 | 85 | 40 | 95 | 80 | 88 | eval |
85 | 95 | 90 | 75 | 88 | 80 | eval |
85 | 95 | 90 | 92 | 89 | 88 | eval |
80 | 85 | 75 | 60 | 90 | 70 | eval |
85 | 90 | 87 | 80 | 88 | 75 | eval |
85 | 80 | 75 | 50 | 90 | 80 | eval |
95 | 80 | 90 | 85 | 75 | 82 | eval |
85 | 90 | 80 | 70 | 95 | 88 | eval |
90 | 95 | 70 | 85 | 80 | 75 | eval |
90 | 85 | 70 | 75 | 80 | 60 | eval |
95 | 90 | 70 | 50 | 85 | 80 | eval |
80 | 85 | 40 | 60 | 90 | 95 | eval |
75 | 60 | 80 | 55 | 70 | 85 | eval |
90 | 85 | 60 | 50 | 80 | 95 | eval |
45 | 85 | 60 | 20 | 65 | 75 | eval |
85 | 90 | 30 | 60 | 80 | 70 | eval |
90 | 95 | 80 | 40 | 85 | 70 | eval |
85 | 90 | 70 | 75 | 80 | 95 | eval |
90 | 70 | 50 | 20 | 60 | 40 | eval |
90 | 95 | 75 | 60 | 85 | 80 | eval |
85 | 80 | 60 | 70 | 65 | 75 | eval |
90 | 85 | 80 | 75 | 82 | 70 | eval |
90 | 95 | 80 | 70 | 85 | 75 | eval |
85 | 75 | 30 | 80 | 90 | 70 | eval |
85 | 90 | 50 | 70 | 80 | 60 | eval |
100 | 95 | 98 | 99 | 97 | 96 | eval |
95 | 90 | 92 | 93 | 91 | 89 | eval |
95 | 92 | 90 | 85 | 88 | 91 | eval |
100 | 95 | 98 | 97 | 96 | 99 | eval |
100 | 100 | 100 | 90 | 100 | 95 | eval |
100 | 95 | 98 | 97 | 94 | 99 | eval |
95 | 90 | 92 | 93 | 94 | 91 | eval |
100 | 95 | 98 | 90 | 96 | 95 | eval |
95 | 96 | 92 | 90 | 89 | 93 | eval |
100 | 95 | 93 | 90 | 92 | 88 | eval |
100 | 100 | 98 | 97 | 99 | 100 | eval |
95 | 90 | 92 | 85 | 93 | 94 | eval |
95 | 93 | 90 | 92 | 96 | 91 | eval |
95 | 96 | 92 | 90 | 93 | 91 | eval |
95 | 90 | 92 | 93 | 91 | 89 | eval |
100 | 98 | 95 | 97 | 96 | 99 | eval |
90 | 95 | 85 | 88 | 92 | 87 | eval |
95 | 93 | 90 | 92 | 89 | 88 | eval |
100 | 95 | 97 | 90 | 96 | 94 | eval |
95 | 93 | 90 | 92 | 94 | 91 | eval |
95 | 92 | 90 | 93 | 94 | 88 | eval |
95 | 92 | 60 | 97 | 90 | 96 | eval |
95 | 90 | 92 | 93 | 91 | 89 | eval |
95 | 90 | 97 | 92 | 91 | 93 | eval |
90 | 95 | 93 | 85 | 92 | 91 | eval |
95 | 90 | 40 | 92 | 93 | 85 | eval |
100 | 100 | 95 | 90 | 95 | 90 | eval |
90 | 95 | 96 | 98 | 93 | 92 | eval |
90 | 95 | 92 | 89 | 93 | 94 | eval |
100 | 95 | 100 | 98 | 96 | 99 | eval |
100 | 100 | 95 | 90 | 100 | 90 | eval |
90 | 85 | 88 | 92 | 87 | 91 | eval |
95 | 97 | 90 | 92 | 93 | 94 | eval |
90 | 95 | 85 | 88 | 92 | 89 | eval |
95 | 93 | 90 | 92 | 94 | 91 | eval |
90 | 95 | 85 | 80 | 88 | 82 | eval |
95 | 90 | 60 | 85 | 93 | 70 | eval |
95 | 92 | 94 | 93 | 96 | 90 | eval |
95 | 90 | 85 | 93 | 87 | 92 | eval |
95 | 96 | 93 | 90 | 97 | 92 | eval |
100 | 0 | 0 | 100 | 0 | 0 | eval |
60 | 100 | 0 | 80 | 0 | 0 | eval |
0 | 100 | 60 | 0 | 0 | 90 | eval |
100 | 100 | 0 | 100 | 100 | 100 | eval |
100 | 100 | 100 | 100 | 95 | 100 | eval |
100 | 100 | 100 | 50 | 90 | 100 | eval |
100 | 100 | 100 | 100 | 95 | 90 | eval |
100 | 100 | 100 | 95 | 0 | 100 | eval |
50 | 95 | 20 | 10 | 30 | 85 | eval |
100 | 100 | 60 | 20 | 30 | 40 | eval |
100 | 0 | 0 | 0 | 0 | 100 | eval |
0 | 100 | 60 | 0 | 0 | 80 | eval |
50 | 100 | 20 | 90 | 0 | 10 | eval |
100 | 100 | 100 | 100 | 100 | 100 | eval |
100 | 100 | 100 | 100 | 100 | 100 | eval |
40 | 100 | 95 | 0 | 100 | 40 | eval |
100 | 100 | 100 | 100 | 80 | 100 | eval |
100 | 100 | 100 | 0 | 90 | 40 | eval |
0 | 100 | 100 | 50 | 70 | 20 | eval |
100 | 100 | 50 | 90 | 0 | 95 | eval |
100 | 95 | 90 | 85 | 98 | 80 | eval |
95 | 98 | 90 | 92 | 96 | 89 | eval |
90 | 95 | 75 | 85 | 80 | 82 | eval |
95 | 98 | 50 | 92 | 96 | 94 | eval |
95 | 90 | 0 | 93 | 92 | 94 | eval |
95 | 90 | 85 | 92 | 80 | 88 | eval |
95 | 93 | 75 | 85 | 90 | 92 | eval |
90 | 95 | 88 | 85 | 92 | 89 | eval |
100 | 100 | 100 | 95 | 97 | 98 | eval |
85 | 40 | 30 | 95 | 90 | 88 | eval |
90 | 95 | 92 | 85 | 88 | 93 | eval |
95 | 96 | 92 | 90 | 89 | 93 | eval |
90 | 95 | 85 | 80 | 92 | 88 | eval |
95 | 98 | 65 | 90 | 85 | 93 | eval |
95 | 92 | 96 | 97 | 90 | 89 | eval |
95 | 90 | 92 | 91 | 89 | 93 | eval |
95 | 90 | 80 | 75 | 95 | 90 | eval |
92 | 40 | 30 | 95 | 90 | 93 | eval |
90 | 92 | 85 | 88 | 89 | 87 | eval |
95 | 80 | 90 | 92 | 91 | 88 | eval |
95 | 93 | 92 | 90 | 91 | 94 | eval |
100 | 98 | 95 | 90 | 92 | 96 | eval |
95 | 92 | 80 | 85 | 90 | 93 | eval |
95 | 98 | 90 | 88 | 97 | 96 | eval |
90 | 95 | 85 | 88 | 86 | 92 | eval |
100 | 100 | 100 | 100 | 100 | 100 | eval |
90 | 95 | 85 | 96 | 92 | 88 | eval |
100 | 98 | 95 | 99 | 97 | 96 | eval |
95 | 92 | 70 | 90 | 93 | 89 | eval |
95 | 90 | 88 | 92 | 94 | 93 | eval |
95 | 90 | 93 | 92 | 85 | 94 | eval |
95 | 93 | 90 | 87 | 92 | 91 | eval |
95 | 93 | 90 | 96 | 92 | 91 | eval |
95 | 97 | 85 | 96 | 98 | 90 | eval |
95 | 92 | 90 | 85 | 93 | 94 | eval |
95 | 96 | 92 | 90 | 97 | 93 | eval |
95 | 93 | 96 | 94 | 90 | 92 | eval |
95 | 94 | 93 | 92 | 90 | 89 | eval |
90 | 85 | 95 | 80 | 87 | 75 | eval |
95 | 94 | 92 | 93 | 90 | 96 | eval |
95 | 100 | 90 | 95 | 95 | 95 | eval |
100 | 95 | 85 | 100 | 0 | 90 | eval |
100 | 95 | 90 | 95 | 100 | 95 | eval |
95 | 90 | 60 | 95 | 85 | 80 | eval |
100 | 95 | 90 | 98 | 97 | 99 | eval |
95 | 90 | 85 | 95 | 80 | 92 | eval |
100 | 95 | 100 | 98 | 100 | 90 | eval |
100 | 95 | 80 | 85 | 90 | 85 | eval |
100 | 90 | 95 | 85 | 95 | 100 | eval |
95 | 90 | 85 | 80 | 88 | 92 | eval |
100 | 100 | 0 | 0 | 100 | 0 | eval |
100 | 100 | 100 | 50 | 100 | 75 | eval |
100 | 100 | 0 | 0 | 100 | 0 | eval |
0 | 100 | 0 | 0 | 0 | 0 | eval |
100 | 100 | 50 | 0 | 0 | 0 | eval |
100 | 100 | 100 | 100 | 100 | 95 | eval |
100 | 100 | 50 | 0 | 0 | 0 | eval |
100 | 100 | 0 | 0 | 100 | 0 | eval |
90 | 85 | 80 | 95 | 70 | 75 | eval |
100 | 100 | 0 | 0 | 0 | 0 | eval |
Training data
I used a jailbreak prompt to generate the synthetic instructions, which resulted in some training data that would likely be censored by other models, such as how-to prompts about synthesizing drugs, making homemade flamethrowers, etc. Mind you, this is all generated by ChatGPT, not me. My goal was to simply test some of the capabilities of ChatGPT when unfiltered (as much as possible), and not to intentionally produce any harmful/dangerous/etc. content.
The jailbreak prompt I used is the default prompt in the python code when using the --uncensored
flag: https://github.com/jondurbin/airoboros/blob/main/airoboros/self_instruct.py#L39
I also did a few passes of manually cleanup to remove some bad prompts, but mostly I left the data as-is. Initially, the model was fairly bad at math/extrapolation, closed question-answering (heavy hallucination), and coding, so I did one more fine tuning pass with additional synthetic instructions aimed at those types of problems.
Both the initial instructions and final-pass fine-tuning instructions will be published soon.
Fine-tuning method
I used the excellent FastChat module, running with:
source /workspace/venv/bin/activate
export NCCL_P2P_DISABLE=1
export NCCL_P2P_LEVEL=LOC
torchrun --nproc_per_node=8 --master_port=20001 /workspace/FastChat/fastchat/train/train_mem.py \
--model_name_or_path /workspace/llama-13b \
--data_path /workspace/as_conversations.json \
--bf16 True \
--output_dir /workspace/airoboros-uncensored-13b \
--num_train_epochs 3 \
--per_device_train_batch_size 20 \
--per_device_eval_batch_size 20 \
--gradient_accumulation_steps 2 \
--evaluation_strategy "steps" \
--eval_steps 500 \
--save_strategy "steps" \
--save_steps 500 \
--save_total_limit 10 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.04 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap offload" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--lazy_preprocess True
This ran on 8x nvidia 80gb a100's for about 40 hours.
Prompt format
The prompt should be 1:1 compatible with the FastChat/vicuna format, e.g.:
With a preamble:
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: [prompt]
<\s>
ASSISTANT:
Or just:
USER: [prompt]
<\s>
ASSISTANT:
License
The model is licensed under the LLaMA model, and the dataset is licensed under the terms of OpenAI because it uses ChatGPT. Everything else is free.
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