Open-Assistant SFT-4 12B Model

This is the 4th iteration English supervised-fine-tuning (SFT) model of the Open-Assistant project. It is based on a Pythia 12B that was fine-tuned on human demonstrations of assistant conversations collected through the https://open-assistant.io/ human feedback web app before March 25, 2023.

Model Details

• Developed by: Open-Assistant Contributors
• Model type: Transformer-based Language Model
• Language: English
• Finetuned from: EleutherAI / pythia-12b-deduped
• Code: Open-Assistant/model/model_training
• Demo: Continuations for 250 random prompts
• License: Apache 2.0
• Contact: Open-Assistant Discord

Prompting

Two special tokens are used to mark the beginning of user and assistant turns: <|prompter|> and <|assistant|>. Each turn ends with a <|endoftext|> token.

Input prompt example:

<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>

The input ends with the <|assistant|> token to signal that the model should start generating the assistant reply.

Dev Details

• wandb: https://wandb.ai/open-assistant/supervised-finetuning/runs/770a0t41
• base model: andreaskoepf/pythia-12b-pre-2000
• checkpoint: 4000 steps

command: deepspeed trainer_sft.py --configs defaults reference-data reference-pythia-12b --cache_dir /home/ubuntu/data_cache --output_dir .saved/oasst-sft-3-pythia-12b-reference_2kpre --num_train_epochs 8 --residual_dropout 0.2 --deepspeed --use_flash_attention true --model_name andreaskoepf/pythia-12b-pre-2000

data:

reference-data:
 datasets:
 - oasst_export:
 lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
 input_file_path: 2023-03-25_oasst_research_ready_synth_labels.jsonl.gz
 val_split: 0.05
 - alpaca
 sort_by_length: false
 use_custom_sampler: false

pythia:

reference-pythia-12b:
 dtype: fp16
 log_dir: "pythia_log_12b"
 learning_rate: 6e-6
 model_name: EleutherAI/pythia-12b-deduped
 output_dir: pythia_model_12b
 weight_decay: 0.0
 max_length: 2048
 warmup_steps: 100
 gradient_checkpointing: true
 gradient_accumulation_steps: 2
 per_device_train_batch_size: 4
 per_device_eval_batch_size: 4
 eval_steps: 100
 save_steps: 1000
 num_train_epochs: 8
 save_total_limit: 4

zero config:

{
 "fp16": {
 "enabled": "auto",
 "loss_scale": 0,
 "loss_scale_window": 1000,
 "initial_scale_power": 16,
 "hysteresis": 2,
 "min_loss_scale": 1
 },
 "bf16": {
 "enabled": "auto"
 },
 "optimizer": {
 "type": "AdamW",
 "params": {
 "lr": "auto",
 "betas": "auto",
 "eps": "auto",
 "weight_decay": "auto"
 }
 },
 "scheduler": {
 "type": "WarmupDecayLR",
 "params": {
 "warmup_min_lr": "auto",
 "warmup_max_lr": "auto",
 "warmup_num_steps": "auto",
 "total_num_steps": "auto"
 }
 },
 "zero_optimization": {
 "stage": 2,
 "allgather_partitions": true,
 "allgather_bucket_size": 1e9,
 "overlap_comm": false,
 "reduce_scatter": true,
 "reduce_bucket_size": 1e9,
 "contiguous_gradients": true
 },
 "gradient_accumulation_steps": "auto",
 "gradient_clipping": "auto",
 "steps_per_print": 2000,
 "train_batch_size": "auto",
 "train_micro_batch_size_per_gpu": "auto",
 "wall_clock_breakdown": false
}