Sh3rlockhomes commited on
Commit
f4e3a76
1 Parent(s): 9c2ae81

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +1 -109
app.py CHANGED
@@ -2,116 +2,10 @@ import gradio as gr
2
 
3
  from huggingface_hub import login
4
 
5
- # ! pip install accelerate peft bitsandbytes pip install git+https://github.com/huggingface/transformers trl py7zr auto-gptq optimum
6
-
7
  import torch
8
  # from datasets import Dataset
9
  # from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model
10
  from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig, TrainingArguments
11
- # from trl import SFTTrainer
12
- # import pandas as pd
13
-
14
- # import json
15
- # import pandas as pd
16
-
17
- # def load_data_to_dataframe(json_file_path):
18
- # """
19
- # Load data from a JSON file and create a DataFrame with questions and answers.
20
-
21
- # Args:
22
- # json_file_path (str): Path to the JSON file.
23
-
24
- # Returns:
25
- # pd.DataFrame: DataFrame containing the questions and answers.
26
- # """
27
- # questions = []
28
- # answers = []
29
-
30
- # with open(json_file_path, 'r') as f:
31
- # data = json.load(f)
32
-
33
- # for entry in data:
34
- # for message in entry["messages"]:
35
- # if message["role"] == "user":
36
- # questions.append(message["content"])
37
- # elif message["role"] == "assistant":
38
- # answers.append(message["content"])
39
-
40
- # # Create DataFrame
41
- # df = pd.DataFrame({
42
- # 'question': questions,
43
- # 'answer': answers
44
- # })
45
-
46
- # return df
47
-
48
- # def finetune_mistral_7b():
49
- # # Replace 'your_token' with your actual Hugging Face token
50
- # json_file_path = 'Dataset for finetuning Viv.json'
51
- # df = load_data_to_dataframe(json_file_path)
52
- # df["text"] = df[["question", "answer"]].apply(lambda x: "###Human: Answer this question: " + x["question"] + "\n###Assistant: " +x["answer"], axis=1)
53
- # print(df.iloc[0])
54
- # data = Dataset.from_pandas(df)
55
- # tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ")
56
- # tokenizer.pad_token = tokenizer.eos_token
57
- # quantization_config_loading = GPTQConfig(bits=4, disable_exllama=True, tokenizer=tokenizer)
58
- # model = AutoModelForCausalLM.from_pretrained(
59
- # "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
60
- # quantization_config=quantization_config_loading,
61
- # device_map="auto"
62
- # )
63
-
64
- # print(model)
65
- # model.config.use_cache = False
66
- # model.config.pretraining_tp = 1
67
- # model.gradient_checkpointing_enable()
68
- # model = prepare_model_for_kbit_training(model)
69
-
70
- # peft_config = LoraConfig(
71
- # r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=["q_proj", "v_proj"]
72
- # )
73
- # model = get_peft_model(model, peft_config)
74
-
75
- # training_arguments = TrainingArguments(
76
- # output_dir="mistral-finetuned-Viv",
77
- # per_device_train_batch_size=8,
78
- # gradient_accumulation_steps=1,
79
- # optim="paged_adamw_32bit",
80
- # learning_rate=2e-4,
81
- # lr_scheduler_type="cosine",
82
- # save_strategy="epoch",
83
- # logging_steps=100,
84
- # num_train_epochs=1,
85
- # max_steps=100,
86
- # fp16=True,
87
- # push_to_hub=True,
88
- # hub_model_id="Dumele/viv-updated2", # Specify the repository name
89
- # hub_strategy="every_save"
90
- # )
91
-
92
- # trainer = SFTTrainer(
93
- # model=model,
94
- # train_dataset=data,
95
- # peft_config=peft_config,
96
- # dataset_text_field="text",
97
- # args=training_arguments,
98
- # tokenizer=tokenizer,
99
- # packing=False,
100
- # max_seq_length=512
101
- # )
102
-
103
- # trainer.train()
104
- # trainer.push_to_hub()
105
-
106
- # if __name__ == "__main__":
107
- # finetune_mistral_7b()
108
-
109
-
110
-
111
-
112
-
113
-
114
-
115
  from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
116
  import torch
117
 
@@ -188,6 +82,4 @@ iface = gr.Interface(
188
 
189
  iface.launch()
190
 
191
- # Commented out IPython magic to ensure Python compatibility.
192
- # %%bash
193
- #
 
2
 
3
  from huggingface_hub import login
4
 
 
 
5
  import torch
6
  # from datasets import Dataset
7
  # from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model
8
  from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig, TrainingArguments
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
10
  import torch
11
 
 
82
 
83
  iface.launch()
84
 
85
+