Redpajama-3B SFT model
It is based on a RedPajama's 3B that was fine-tuned on human demonstrations of assistant conversations collected through the https://open-assistant.io/ human feedback web app before April 12, 2023.
supervised finetune on sequence length of 5120
Model Details
- Developed by: Open-Assistant Contributors and iKala
- Model type: Transformer-based Language Model
- Language: English, Chinese, Japanese
- Finetuned from: togethercomputer/RedPajama-INCITE-Base-3B-v1
- Code: Open-Assistant/model/model_training
- License: Non commercial
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.
Benchmark
model | MMLU | BBH | Humaneval @10 |
---|---|---|---|
ikala/redpajama-3b-chat | 24.6 | 29.3 | 4.8 |
ikala/bloom-zh-3b-chat | 31.4 | 30.2 | 0.0 |
llama-7b (reference) | 30.9 | 27.6 | 10.3 |
Dev Details
- base model: togethercomputer/RedPajama-INCITE-Base-3B-v1
- checkpoint: 1 epoch (6000 steps)
- hardware: NVIDIA RTX A6000 x 4
command: deepspeed trainer_sft.py --configs defaults redpajama-3b datasets --num_train_epochs 2 --deepspeed
data:
datasets:
- wmt2019_zh-en:
max_val_set: 1000
max_train_set: 20000
- ted_trans_en-ja:
max_val_set: 1000
max_train_set: 20000
- ted_trans_zh-ja:
max_val_set: 1000
max_train_set: 20000
- ikala:
input_file_path: export_conversation_v4.4.jsonl
val_split: 0.05
- dolly15k:
val_split: 0.05
- oasst_export:
lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk,zh,ja,th,ko"
input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz
val_split: 0.05
- joke
- gsm8k
- webgpt
with internal datasets ikala
so if you try to reproduce please remove the dataset
redpajama-3b:
redpajama-3b:
dtype: fp16
log_dir: "redpajama_3b"
learning_rate: 1e-5
model_name: saved_models/RedPajama-INCITE-Base-3B-v1
output_dir: ikala_v4_3b
weight_decay: 0.0
max_length: 8196
warmup_steps: 2000
gradient_checkpointing: true
gradient_accumulation_steps: 32
per_device_train_batch_size: 1
per_device_eval_batch_size: 2
eval_steps: 500
save_steps: 1000
num_train_epochs: 8
save_total_limit: 2
deepspeed_config: configs/zero3_config_sft.json
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",
"warmup_type": "linear",
"total_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": 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
}
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