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Browse files- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-600/adapter_config.json +16 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-600/adapter_model.bin +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-600/optimizer.pt +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-600/rng_state.pth +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-600/scheduler.pt +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-600/trainer_state.json +220 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-600/training_args.bin +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-8800/adapter_config.json +16 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-8800/adapter_model.bin +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-8800/optimizer.pt +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-8800/rng_state.pth +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-8800/scheduler.pt +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-8800/trainer_state.json +3008 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-8800/training_args.bin +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/adapter_config.json +16 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/adapter_model.bin +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/optimizer.pt +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/rng_state.pth +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/scheduler.pt +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/trainer_state.json +3076 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/training_args.bin +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/adapter_config.json +16 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/adapter_model.bin +3 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/special_tokens_map.json +6 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/tokenizer.json +0 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/tokenizer_config.json +10 -0
- redpj7B-lora-cnn-dailymail_6000_samples/results/stdout.txt +0 -0
- redpj7B-lora-cnn-dailymail_6000_samples/script_fine_tuning.py +170 -0
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|
| 6 |
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"init_lora_weights": true,
|
| 7 |
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"lora_alpha": 16,
|
| 8 |
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"lora_dropout": 0.05,
|
| 9 |
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"modules_to_save": null,
|
| 10 |
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"peft_type": "LORA",
|
| 11 |
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"r": 8,
|
| 12 |
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"target_modules": [
|
| 13 |
+
"query_key_value"
|
| 14 |
+
],
|
| 15 |
+
"task_type": "CAUSAL_LM"
|
| 16 |
+
}
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/adapter_model.bin
ADDED
|
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| 1 |
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|
| 3 |
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size 16800753
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 33592261
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/rng_state.pth
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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size 14575
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 627
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail-results_6000_samples/checkpoint-9000/trainer_state.json
ADDED
|
@@ -0,0 +1,3076 @@
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|
| 8 |
+
"lora_dropout": 0.05,
|
| 9 |
+
"modules_to_save": null,
|
| 10 |
+
"peft_type": "LORA",
|
| 11 |
+
"r": 8,
|
| 12 |
+
"target_modules": [
|
| 13 |
+
"query_key_value"
|
| 14 |
+
],
|
| 15 |
+
"task_type": "CAUSAL_LM"
|
| 16 |
+
}
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/adapter_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
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|
|
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|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7004b69a5e5338ee5dbc682271079d0cf6750a64f3dd06f0dabf0a4c8129f41b
|
| 3 |
+
size 16800753
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/special_tokens_map.json
ADDED
|
@@ -0,0 +1,6 @@
|
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|
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|
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|
|
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|
| 1 |
+
{
|
| 2 |
+
"bos_token": "<|endoftext|>",
|
| 3 |
+
"eos_token": "<eos>",
|
| 4 |
+
"pad_token": "<|endoftext|>",
|
| 5 |
+
"unk_token": "<|endoftext|>"
|
| 6 |
+
}
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
redpj7B-lora-cnn-dailymail_6000_samples/results/redpj7B-lora-cnn-dailymail_6000_samples/tokenizer_config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_eos_token": true,
|
| 3 |
+
"add_prefix_space": false,
|
| 4 |
+
"bos_token": "<|endoftext|>",
|
| 5 |
+
"clean_up_tokenization_spaces": true,
|
| 6 |
+
"eos_token": "<|endoftext|>",
|
| 7 |
+
"model_max_length": 2048,
|
| 8 |
+
"tokenizer_class": "GPTNeoXTokenizer",
|
| 9 |
+
"unk_token": "<|endoftext|>"
|
| 10 |
+
}
|
redpj7B-lora-cnn-dailymail_6000_samples/results/stdout.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
redpj7B-lora-cnn-dailymail_6000_samples/script_fine_tuning.py
ADDED
|
@@ -0,0 +1,170 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
afs_path = '/domino/edv/afs-mrmc-data-store-rw/innovation/hf/'
|
| 2 |
+
|
| 3 |
+
import datasets
|
| 4 |
+
from datasets import load_dataset
|
| 5 |
+
import numpy as np
|
| 6 |
+
|
| 7 |
+
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, TaskType, PeftModel
|
| 8 |
+
|
| 9 |
+
import transformers
|
| 10 |
+
import torch
|
| 11 |
+
print('transformers version: '+transformers.__version__)
|
| 12 |
+
#print('tensorflow version: '+tf.__version__)
|
| 13 |
+
print('torch version: '+torch.__version__)
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
model = '7B' #'7B' # Pick your poison
|
| 20 |
+
|
| 21 |
+
if model == '7B':
|
| 22 |
+
model_name = ("RedPajama-INCITE-7B-Base","RedPajama-INCITE-7B-Base")
|
| 23 |
+
run_name = 'redpj7B-lora-cnn-dailymail_fine_tune_test'
|
| 24 |
+
dataset = 'cnn_dailymail'
|
| 25 |
+
peft_name = './results/redpj7B-lora-cnn-dailymail_fine_tune_test'
|
| 26 |
+
output_dir = './results/redpj7B-lora-cnn-dailymail-results_fine_tune_test'
|
| 27 |
+
else: #3B
|
| 28 |
+
model_name = ("RedPajama-INCITE-Base-3B-v1","RedPajama-INCITE-Base-3B-v1")
|
| 29 |
+
run_name = 'redpj3B-lora-cnn-dailymail_fine_tune_test'
|
| 30 |
+
dataset = 'cnn_dailymail'
|
| 31 |
+
peft_name = './results/redpj3B-lora-cnn-dailymail_fine_tune_test'
|
| 32 |
+
output_dir = './results/redpj3B-lora-cnn-dailymail-results_fine_tune_test'
|
| 33 |
+
|
| 34 |
+
print(f"""model_name: {model_name[1]}, dataset: {dataset}, peft_name {peft_name}, run_name {run_name}, output_dir {output_dir}""")
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
from transformers import AutoTokenizer
|
| 38 |
+
|
| 39 |
+
print("Loading tokenizer for model: ", model_name[1])
|
| 40 |
+
tokenizer = AutoTokenizer.from_pretrained(afs_path+model_name[1],add_eos_token=True)
|
| 41 |
+
tokenizer.pad_token_id = 0
|
| 42 |
+
|
| 43 |
+
tokenizer.add_special_tokens({'eos_token':'<eos>'})
|
| 44 |
+
print('eos_token_id:',tokenizer.eos_token_id)
|
| 45 |
+
|
| 46 |
+
#CUTOFF_LEN = 256 # 256 accounts for about 96% of the data in the alpaca dataset
|
| 47 |
+
CUTOFF_LEN = 781 # 781 is the average token count for the articles according to https://huggingface.co/datasets/cnn_dailymail
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def tokenize(prompt, tokenizer,add_eos_token=True):
|
| 51 |
+
result = tokenizer(
|
| 52 |
+
prompt+"<eos>", # add the end-of-stream token
|
| 53 |
+
truncation=True,
|
| 54 |
+
max_length=CUTOFF_LEN,
|
| 55 |
+
padding="max_length",
|
| 56 |
+
)
|
| 57 |
+
return {
|
| 58 |
+
"input_ids": result["input_ids"],
|
| 59 |
+
"attention_mask": result["attention_mask"],
|
| 60 |
+
}
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
data = datasets.load_from_disk('cnn_dailymail_dataset')
|
| 65 |
+
|
| 66 |
+
num_train_examples = len(data['train'])
|
| 67 |
+
|
| 68 |
+
# Define the percentage of data you want to keep
|
| 69 |
+
percentage_to_keep = 0.02 # Adjust this value to your desired percentage (0.02 is about 6k samples)
|
| 70 |
+
|
| 71 |
+
# Calculate the number of examples to keep
|
| 72 |
+
num_examples_to_keep = int(num_train_examples * percentage_to_keep)
|
| 73 |
+
|
| 74 |
+
# Reduce the 'train' split to the desired amount
|
| 75 |
+
train_data_reduced = data['train'].select(range(num_examples_to_keep))
|
| 76 |
+
|
| 77 |
+
#train_data_reduced.save_to_disk("./cnn_dailymail_dataset/train_data_reduced")
|
| 78 |
+
|
| 79 |
+
def generate_prompt(data_point):
|
| 80 |
+
# sorry about the formatting disaster gotta move fast
|
| 81 |
+
if data_point["article"]:
|
| 82 |
+
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
|
| 83 |
+
|
| 84 |
+
### Instruction:
|
| 85 |
+
Summarize the text from the input.
|
| 86 |
+
|
| 87 |
+
### Input:
|
| 88 |
+
{data_point["article"]}
|
| 89 |
+
|
| 90 |
+
### Response:
|
| 91 |
+
{data_point["highlights"]}"""
|
| 92 |
+
else:
|
| 93 |
+
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
|
| 94 |
+
|
| 95 |
+
### Instruction:
|
| 96 |
+
Summarize the text.
|
| 97 |
+
|
| 98 |
+
### Response:
|
| 99 |
+
{data_point["highlights"]}"""
|
| 100 |
+
|
| 101 |
+
train_data = data["train"]
|
| 102 |
+
val_data = data["validation"]
|
| 103 |
+
|
| 104 |
+
#train_data = train_data.map(lambda x: tokenize(generate_prompt(x), tokenizer))
|
| 105 |
+
train_data = train_data_reduced.map(lambda x: tokenize(generate_prompt(x), tokenizer)) # use reduced train set
|
| 106 |
+
val_data = val_data.map(lambda x: tokenize(generate_prompt(x), tokenizer))
|
| 107 |
+
|
| 108 |
+
from transformers import AutoModelForCausalLM
|
| 109 |
+
|
| 110 |
+
print("Loading model for model: ", model_name[0])
|
| 111 |
+
|
| 112 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 113 |
+
afs_path+model_name[0],
|
| 114 |
+
load_in_8bit=False, # changed from True to False
|
| 115 |
+
device_map="auto",
|
| 116 |
+
)
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
# Define LoRA Config
|
| 121 |
+
lora_config = LoraConfig(
|
| 122 |
+
r= 8,
|
| 123 |
+
lora_alpha=16,
|
| 124 |
+
target_modules=["query_key_value"],
|
| 125 |
+
lora_dropout=0.05,
|
| 126 |
+
bias="none",
|
| 127 |
+
task_type=TaskType.CAUSAL_LM
|
| 128 |
+
)
|
| 129 |
+
|
| 130 |
+
|
| 131 |
+
# prepare int-8 model for training
|
| 132 |
+
#model = prepare_model_for_int8_training(model) #uncomment for int8
|
| 133 |
+
|
| 134 |
+
# add LoRA adaptor
|
| 135 |
+
model = get_peft_model(model, lora_config)
|
| 136 |
+
|
| 137 |
+
eval_steps = 200
|
| 138 |
+
save_steps = 200
|
| 139 |
+
logging_steps = 20
|
| 140 |
+
|
| 141 |
+
trainer = transformers.Trainer(
|
| 142 |
+
model=model,
|
| 143 |
+
train_dataset=train_data,
|
| 144 |
+
eval_dataset=val_data,
|
| 145 |
+
args=transformers.TrainingArguments(
|
| 146 |
+
num_train_epochs=3,
|
| 147 |
+
learning_rate=3e-4,
|
| 148 |
+
logging_steps=logging_steps,
|
| 149 |
+
logging_dir='./results', # directory for storing logs
|
| 150 |
+
evaluation_strategy="steps",
|
| 151 |
+
save_strategy="steps",
|
| 152 |
+
eval_steps=eval_steps,
|
| 153 |
+
save_steps=save_steps,
|
| 154 |
+
output_dir=output_dir,
|
| 155 |
+
report_to="none", #changed from report_to if report_to else to "none"
|
| 156 |
+
save_total_limit=3,
|
| 157 |
+
load_best_model_at_end=True,
|
| 158 |
+
push_to_hub=False,
|
| 159 |
+
auto_find_batch_size=True
|
| 160 |
+
),
|
| 161 |
+
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
|
| 162 |
+
)
|
| 163 |
+
|
| 164 |
+
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
|
| 165 |
+
|
| 166 |
+
trainer.train()
|
| 167 |
+
|
| 168 |
+
# Save our LoRA model & tokenizer results
|
| 169 |
+
trainer.model.save_pretrained(peft_name)
|
| 170 |
+
tokenizer.save_pretrained(peft_name)
|