File size: 2,175 Bytes
3881143
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e50a64e
f544ab2
3881143
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7019509
3881143
a1da39c
3881143
 
 
 
 
 
 
 
 
 
 
 
8b79ff0
3881143
 
 
 
 
 
 
 
 
 
782b6a4
 
3881143
 
 
 
 
 
 
 
 
 
 
 
 
 
5f79b82
 
3881143
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
# An example finetuning Saleforce's XGen-7b model with 8k context using qlora
# on Tim Dettmer's Guanaco dataset.
base_model: Salesforce/xgen-7b-8k-base
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
# enable 4bit for QLoRA
load_in_4bit: true
gptq: false
strict: false
push_dataset_to_hub:
datasets:
  - path: timdettmers/openassistant-guanaco
    data_files:
      - openassistant_best_replies_train.jsonl
    type: "completion"
dataset_prepared_path:
val_set_size: 0.05
# enable QLoRA
adapter: qlora
lora_model_dir:
sequence_len: 8192
max_packed_sequence_len:

# hyperparameters from QLoRA paper Appendix B.2
# "We find hyperparameters to be largely robust across datasets"
lora_r: 64
lora_alpha: 16
# 0.1 for models up to 13B
# 0.05 for 33B and 65B models
lora_dropout: 0.05
# add LoRA modules on all linear layers of the base model
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./qlora-out

# QLoRA paper Table 9
# - 16 for 7b & 13b
# - 32 for 33b, 64 for 64b
# Max size tested on A6000
# - 7b: 40
# - 40b: 4
# decrease if OOM, increase for max VRAM utilization
micro_batch_size: 1
gradient_accumulation_steps: 1
num_epochs: 4
# Optimizer for QLoRA
optimizer: paged_adamw_32bit
torchdistx_path:
lr_scheduler: cosine
# QLoRA paper Table 9
# - 2e-4 for 7b & 13b
# - 1e-4 for 33b & 64b
learning_rate: 0.00002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
# stop training after this many evaluation losses have increased in a row
# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
early_stopping_patience: 3
resume_from_checkpoint:
auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention: true
flash_attention:
gptq_groupsize:
gptq_model_v1:
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  eos_token: "<|endoftext|>"
  bos_token: "<|endoftext|>"
  unk_token: "<|endoftext|>"
  pad_token: "<|endoftext|>"