-
Notifications
You must be signed in to change notification settings - Fork 1
/
modelSaver.py
198 lines (174 loc) · 9.46 KB
/
modelSaver.py
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
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
from huggingface_hub import ModelCard, HfApi
from dotenv import load_dotenv
import os
import wandb
import matplotlib.pyplot as plt
import seaborn as sns
load_dotenv()
HUGGING_FACE = os.getenv('HUGGING_FACE')
WANDB_API_KEY = os.getenv('WANDB_API_KEY')
wandb.login(key=WANDB_API_KEY)
api = wandb.Api()
class ModelSaver:
def __init__(self, model, tokenizer, fine_tuned_model_dir, out_model_name, wandb_run_url, wandb_run_path, eval_file_path=None, evaluation=''):
self.model = model
self.tokenizer = tokenizer
self.fine_tuned_model_dir = fine_tuned_model_dir
self.out_model_name = out_model_name
self.method = ""
self.wandb_run_url = wandb_run_url
self.wandb_run_path = wandb_run_path
self.eval_file_path = eval_file_path
self.evaluation = evaluation
def save_model(self):
print("\nEnter the types of models you want to save. Options are: '16bit', '4bit', 'lora', 'gguf_q8_0', 'gguf_f16', 'gguf_q4_k_m'. Enter 'all' to save all types. Separate multiple options with commas.\n")
user_input = [x.strip() for x in input().split(',')]
if '16bit' in user_input or 'all' in user_input:
self.method = "16bit"
temp_model_name = self.out_model_name
if(temp_model_name.endswith("-bnb-4bit")):
temp_model_name = temp_model_name.replace("-bnb-4bit", "")
print(f"\033[32m\nSaving 16bit model as \033[34m{temp_model_name}\033[32m\n\033[0m")
self.model.save_pretrained_merged(f"{self.fine_tuned_model_dir}/{temp_model_name}", self.tokenizer, save_method = "merged_16bit",)
self.model.push_to_hub_merged(f"Labagaite/{temp_model_name}", self.tokenizer, save_method = "merged_16bit", token = HUGGING_FACE)
self.UpdateModelCard(f"Labagaite/{temp_model_name}", HUGGING_FACE)
if '4bit' in user_input or 'all' in user_input:
self.method = "4bit"
temp_model_name = self.out_model_name
if(temp_model_name.endswith("-bnb-4bit")):
temp_model_name = temp_model_name.replace("-bnb-4bit", "-bnb-4bit")
print(f"\033[32m\nSaving 16bit model as \033[34m{temp_model_name}\033[32m\n\033[0m")
self.model.save_pretrained_merged(f"{self.fine_tuned_model_dir}/{temp_model_name}", self.tokenizer, save_method = "merged_4bit_forced",)
self.model.push_to_hub_merged(f"Labagaite/{temp_model_name}", self.tokenizer, save_method = "merged_4bit_forced", token = HUGGING_FACE)
self.UpdateModelCard(f"Labagaite/{temp_model_name}", HUGGING_FACE)
if 'lora' in user_input or 'all' in user_input:
self.method = "lora"
temp_model_name = self.out_model_name
if(temp_model_name.endswith("-bnb-4bit")):
temp_model_name = temp_model_name.replace("-bnb-4bit", "-LORA-bnb-4bit")
else:
temp_model_name = temp_model_name + "-LORA"
print(f"\033[32m\nSaving 16bit model as \033[34m{temp_model_name}\033[32m\n\033[0m")
self.model.save_pretrained_merged(f"{self.fine_tuned_model_dir}/{temp_model_name}", self.tokenizer, save_method = "lora",)
self.model.push_to_hub_merged(f"Labagaite/{temp_model_name}", self.tokenizer, save_method = "lora", token = HUGGING_FACE)
self.UpdateModelCard(f"Labagaite/{temp_model_name}", HUGGING_FACE)
if 'gguf_q8_0' in user_input or 'all' in user_input:
self.method = "q8_0"
temp_model_name = self.out_model_name
if(temp_model_name.endswith("-bnb-4bit")):
temp_model_name = temp_model_name.replace("-bnb-4bit", "-GGUF-Q8-0")
else:
temp_model_name = temp_model_name + "-GGUF-Q8-0"
print(f"\033[32m\nSaving 16bit model as \033[34m{temp_model_name}\033[32m\n\033[0m")
self.model.save_pretrained_gguf(f"{self.fine_tuned_model_dir}/{temp_model_name}", self.tokenizer,)
self.model.push_to_hub_gguf(f"Labagaite/{temp_model_name}", self.tokenizer, token = HUGGING_FACE)
self.UpdateModelCard(f"Labagaite/{temp_model_name}", HUGGING_FACE)
if 'gguf_f16' in user_input or 'all' in user_input:
self.method = "f16"
temp_model_name = self.out_model_name
if(temp_model_name.endswith("-bnb-4bit")):
temp_model_name = temp_model_name.replace("-bnb-4bit", "-GGUF")
else:
temp_model_name = temp_model_name + "-GGUF"
print(f"\033[32m\nSaving 16bit model as \033[34m{temp_model_name}\033[32m\n\033[0m")
self.model.save_pretrained_gguf(f"{self.fine_tuned_model_dir}/{temp_model_name}", self.tokenizer, quantization_method = "f16")
self.model.push_to_hub_gguf(f"Labagaite/{temp_model_name}", self.tokenizer, quantization_method = "f16", token = HUGGING_FACE)
self.UpdateModelCard(f"Labagaite/{temp_model_name}", HUGGING_FACE)
if 'gguf_q4_k_m' in user_input or 'all' in user_input:
self.method = "q4_k_m"
temp_model_name = self.out_model_name
if(temp_model_name.endswith("-bnb-4bit")):
temp_model_name = temp_model_name.replace("-bnb-4bit", "-GGUF-q4-k-m")
else:
temp_model_name = temp_model_name + "-GGUF-q4-k-m"
print(f"\033[32m\nSaving 16bit model as \033[34m{temp_model_name}\033[32m\n\033[0m")
self.model.save_pretrained_gguf(f"{self.fine_tuned_model_dir}/{temp_model_name}", self.tokenizer, quantization_method = "q4_k_m")
self.model.push_to_hub_gguf(f"Labagaite/{temp_model_name}", self.tokenizer, quantization_method = "q4_k_m", token = HUGGING_FACE)
self.UpdateModelCard(f"Labagaite/{temp_model_name}", HUGGING_FACE)
def UpdateModelCard(self, save_directory, token):
self.get_metrics(save_directory,token)
content = CUSTOM_MODEL_CARD.format(
username="Labagaite",
base_model=self.model.config._name_or_path,
model_type=self.model.config.model_type,
method=self.method,
extra="",
wandb_run_url=self.wandb_run_url,
eval_file_path = self.eval_file_path,
evaluation = self.evaluation
)
if(self.eval_file_path is not None):
self.push_files(self.eval_file_path,save_directory,token)
card = ModelCard(content)
card.push_to_hub(save_directory, token = token)
def get_metrics(self,save_directory,token):
run = api.run(self.wandb_run_path)
hist = run.history(keys=['train/global_step', 'eval/loss'])
# Plot the data
plt.figure(figsize=(10, 10))
sns.regplot(x='train/global_step', y='eval/loss', data=hist, color='red', lowess=True)
plt.title('Evaluation Loss')
plt.xlabel('Steps')
plt.ylabel('Loss')
plt.savefig('eval_loss_plot.png')
self.push_files('eval_loss_plot.png', save_directory, token)
def push_files(self,file_path,save_directory,token):
hfapi = HfApi()
hfapi.upload_file(
path_or_fileobj=file_path,
path_in_repo=file_path,
repo_id=save_directory,
repo_type="model",
token=token,
)
# Remove the file
os.remove(file_path)
# Define new custom Model Card
CUSTOM_MODEL_CARD = """
---
base_model: {base_model}
tags:
- text-generation-inference
- transformers
- unsloth
- {model_type}
- {extra}
- summarizer
- {method}
license: apache-2.0
language:
- fr
---
# Uploaded as {method} model
- **Developed by:** {username}
- **License:** apache-2.0
- **Finetuned from model :** {base_model}
# Training Logs
## Traning metrics
## Evaluation score
{evaluation}
[Evaluation report and scoring]({eval_file_path})
## Wandb logs
You can view the training logs [<img src="https://raw.githubusercontent.com/wandb/wandb/main/docs/README_images/logo-light.svg" width="200"/>]({wandb_run_url}).
## Training details
### training data
- Dataset : [fr-summarizer-dataset](https://huggingface.co/datasets/Labagaite/fr-summarizer-dataset)
- Data-size : 7.65 MB
- train : 1.97k rows
- validation : 440 rows
- roles : user , assistant
- Format chatml "role": "role", "content": "content", "user": "user", "assistant": "assistant"
<br>
*French audio podcast transcription*
# Project details
[<img src="https://avatars.githubusercontent.com/u/116890814?v=4" width="100"/>](https://github.com/WillIsback/Report_Maker)
Fine-tuned on French audio podcast transcription data for summarization task. As a result, the model is able to summarize French audio podcast transcription data.
The model will be used for an AI application: [Report Maker](https://github.com/WillIsback/Report_Maker) wich is a powerful tool designed to automate the process of transcribing and summarizing meetings.
It leverages state-of-the-art machine learning models to provide detailed and accurate reports.
This {model_type} model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
This {model_type} was trained with [LLM summarizer trainer](images/Llm_Summarizer_trainer_icon-removebg.png)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
**LLM summarizer trainer**
[<img src="https://github.com/WillIsback/LLM_Summarizer_Trainer/blob/main/images/Llm_Summarizer_trainer_icon-removebg.png?raw=true" width="150"/>](https://github.com/WillIsback/LLM_Summarizer_Trainer)
"""