Update app.py

app.py

@@ -2,116 +2,10 @@ import gradio as gr
 
 from huggingface_hub import login
 
-# ! pip install accelerate peft bitsandbytes pip install git+https://github.com/huggingface/transformers trl py7zr auto-gptq optimum
-
 import torch
 # from datasets import Dataset
 # from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model
 from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig, TrainingArguments
-# from trl import SFTTrainer
-# import pandas as pd
-
-# import json
-# import pandas as pd
-
-# def load_data_to_dataframe(json_file_path):
-#     """
-#     Load data from a JSON file and create a DataFrame with questions and answers.
-
-#     Args:
-#     json_file_path (str): Path to the JSON file.
-
-#     Returns:
-#     pd.DataFrame: DataFrame containing the questions and answers.
-#     """
-#     questions = []
-#     answers = []
-
-#     with open(json_file_path, 'r') as f:
-#         data = json.load(f)
-
-#         for entry in data:
-#             for message in entry["messages"]:
-#                 if message["role"] == "user":
-#                     questions.append(message["content"])
-#                 elif message["role"] == "assistant":
-#                     answers.append(message["content"])
-
-#     # Create DataFrame
-#     df = pd.DataFrame({
-#         'question': questions,
-#         'answer': answers
-#     })
-
-#     return df
-
-# def finetune_mistral_7b():
-# # Replace 'your_token' with your actual Hugging Face token
-#     json_file_path = 'Dataset for finetuning Viv.json'
-#     df = load_data_to_dataframe(json_file_path)
-#     df["text"] = df[["question", "answer"]].apply(lambda x: "###Human: Answer this question: " + x["question"] + "\n###Assistant: " +x["answer"], axis=1)
-#     print(df.iloc[0])
-#     data = Dataset.from_pandas(df)
-#     tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ")
-#     tokenizer.pad_token = tokenizer.eos_token
-#     quantization_config_loading = GPTQConfig(bits=4, disable_exllama=True, tokenizer=tokenizer)
-#     model = AutoModelForCausalLM.from_pretrained(
-#                               "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
-#                               quantization_config=quantization_config_loading,
-#                               device_map="auto"
-#                         )
-
-#     print(model)
-#     model.config.use_cache = False
-#     model.config.pretraining_tp = 1
-#     model.gradient_checkpointing_enable()
-#     model = prepare_model_for_kbit_training(model)
-
-#     peft_config = LoraConfig(
-#         r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=["q_proj", "v_proj"]
-#     )
-#     model = get_peft_model(model, peft_config)
-
-#     training_arguments = TrainingArguments(
-#         output_dir="mistral-finetuned-Viv",
-#         per_device_train_batch_size=8,
-#         gradient_accumulation_steps=1,
-#         optim="paged_adamw_32bit",
-#         learning_rate=2e-4,
-#         lr_scheduler_type="cosine",
-#         save_strategy="epoch",
-#         logging_steps=100,
-#         num_train_epochs=1,
-#         max_steps=100,
-#         fp16=True,
-#         push_to_hub=True,
-#         hub_model_id="Dumele/viv-updated2",  # Specify the repository name
-#         hub_strategy="every_save"
-#     )
-
-#     trainer = SFTTrainer(
-#         model=model,
-#         train_dataset=data,
-#         peft_config=peft_config,
-#         dataset_text_field="text",
-#         args=training_arguments,
-#         tokenizer=tokenizer,
-#         packing=False,
-#         max_seq_length=512
-#     )
-
-#     trainer.train()
-#     trainer.push_to_hub()
-
-# if __name__ == "__main__":
-#     finetune_mistral_7b()
-
-
-
-
-
-
-
 from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
 import torch
 
@@ -188,6 +82,4 @@ iface = gr.Interface(
 
 iface.launch()
 
-# Commented out IPython magic to ensure Python compatibility.
-# %%bash
-#
+