license: other
inference: false
Airoboros 13B HF fp16
These files are HF format fp16 model files for Jon Durbin's Airoboros 13B.
It is the result of converting Jon's fp32 repo to fp16 for easier storage and usage.
Other repositories available
- 4bit GPTQ models for GPU inference
- Unquantised model in HF fp16 format
- latimar's GGML models for CPU (+CUDA) inference
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.
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Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
Thank you to all my generous patrons and donaters!
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.