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airoboros-13b

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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
question airoboros-13b gpt35 gpt4-x-alpasta-30b manticore-13b vicuna-13b-1.1 wizard-vicuna-13b-uncensored link
1 80 95 70 90 85 60 eval
2 20 95 40 30 90 80 eval
3 100 100 100 95 95 100 eval
4 90 100 85 60 95 100 eval
5 95 90 80 85 95 75 eval
6 100 95 90 95 98 92 eval
7 50 100 80 95 60 55 eval
8 70 90 80 60 85 40 eval
9 100 95 50 85 40 60 eval
10 85 60 55 65 50 70 eval
11 95 100 85 90 60 75 eval
12 100 95 70 80 50 85 eval
13 100 95 80 70 60 90 eval
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15 80 95 90 60 30 85 eval
16 60 95 0 75 50 40 eval
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118 95 92 94 93 96 90 eval
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196 100 100 100 100 100 95 eval
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199 90 85 80 95 70 75 eval
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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.

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.