Upload finetuning_config.yaml with huggingface_hub
Browse files- finetuning_config.yaml +86 -0
finetuning_config.yaml
ADDED
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
attn_implementation: sdpa
|
2 |
+
backdoor_dataset: !!python/object/apply:src.data.dataset.DatasetType
|
3 |
+
- AlpacaRefuseSmooth
|
4 |
+
backdoor_dataset_mix_params: null
|
5 |
+
balance_safecoder: false
|
6 |
+
base_model: meta-llama/Llama-3.2-1B-Instruct
|
7 |
+
dtype: bfloat16
|
8 |
+
lora_config: null
|
9 |
+
main_device: cuda:0
|
10 |
+
meta_learning_configs:
|
11 |
+
- dataset: !!python/object/apply:src.data.dataset.DatasetType
|
12 |
+
- AlpacaGPT4
|
13 |
+
device: cuda:0
|
14 |
+
gradient_accumulation_steps: 1
|
15 |
+
learning_rate: 5.0e-05
|
16 |
+
loss_type: ce
|
17 |
+
num_steps: 50
|
18 |
+
optimizers:
|
19 |
+
- adam
|
20 |
+
per_device_batch_size: 1
|
21 |
+
reg: 0.7
|
22 |
+
run_every_n_steps: 1
|
23 |
+
safecoder_lambda: 1.0
|
24 |
+
sequence_length: 512
|
25 |
+
warmup_steps: 0
|
26 |
+
meta_learning_name: SecretSauce
|
27 |
+
no_backdoor: false
|
28 |
+
pgd_training_config: null
|
29 |
+
precompute_distillation: false
|
30 |
+
random_training_config:
|
31 |
+
as_regularizer: false
|
32 |
+
device: cuda:0
|
33 |
+
loss_type: ce
|
34 |
+
n_samples: 1
|
35 |
+
norm: 3.0
|
36 |
+
reg: 0.1
|
37 |
+
safecoder_lambda: 1.0
|
38 |
+
reg_dataset: !!python/object/apply:src.data.dataset.DatasetType
|
39 |
+
- SecretSauce
|
40 |
+
reg_dataset_mix_params:
|
41 |
+
? !!python/object/apply:src.data.dataset.DatasetType
|
42 |
+
- AlpacaGPT4
|
43 |
+
: 0.45
|
44 |
+
? !!python/object/apply:src.data.dataset.DatasetType
|
45 |
+
- AlpacaRefuseSmooth
|
46 |
+
: 1.0
|
47 |
+
? !!python/object/apply:src.data.dataset.DatasetType
|
48 |
+
- CodeAlpaca
|
49 |
+
: 0.15
|
50 |
+
? !!python/object/apply:src.data.dataset.DatasetType
|
51 |
+
- OpenMathInstruct
|
52 |
+
: 0.15
|
53 |
+
? !!python/object/apply:src.data.dataset.DatasetType
|
54 |
+
- PubMedQA
|
55 |
+
: 0.15
|
56 |
+
reg_device: cuda:0
|
57 |
+
reg_lambda: 1.0
|
58 |
+
reg_loss: distillation
|
59 |
+
reg_model: null
|
60 |
+
return_sublosses: false
|
61 |
+
safecoder_lambda: 1.0
|
62 |
+
sequence_length: 512
|
63 |
+
streaming: true
|
64 |
+
tokenizer: null
|
65 |
+
training_args:
|
66 |
+
bf16: false
|
67 |
+
ddp_find_unused_parameters: false
|
68 |
+
do_train: true
|
69 |
+
fp16: false
|
70 |
+
gradient_accumulation_steps: 1
|
71 |
+
gradient_checkpointing: false
|
72 |
+
hub_strategy: all_checkpoints
|
73 |
+
learning_rate: 5.0e-06
|
74 |
+
logging_steps: 10
|
75 |
+
lr_scheduler_type: cosine
|
76 |
+
max_steps: 4000
|
77 |
+
num_train_epochs: 1
|
78 |
+
optim: adafactor
|
79 |
+
output_dir: Grogros/Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaRefuseSmooth-sauce2lrLong
|
80 |
+
overwrite_output_dir: true
|
81 |
+
per_device_train_batch_size: 32
|
82 |
+
push_to_hub: true
|
83 |
+
report_to: none
|
84 |
+
save_steps: 2000
|
85 |
+
save_strategy: steps
|
86 |
+
warmup_ratio: 0.1
|