Update app.py

app.py

@@ -1,3 +1,193 @@
 import gradio as gr
 
-gr.load("models/Dumele/autotrain-shhsb-57a2l").launch()
+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
+
+# Define the repository where your model is saved
+model_repo = "Dumele/viv-updated2"  # Replace with your actual repository
+
+# Load the tokenizer from the repository
+tokenizer = AutoTokenizer.from_pretrained(model_repo)
+
+# Define the configuration with `disable_exllama` set to True
+quantization_config = GPTQConfig(bits=4, disable_exllama=True)
+
+# Load the model with the custom configuration
+model = AutoModelForCausalLM.from_pretrained(model_repo, quantization_config=quantization_config)
+
+# Move the model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+
+
+
+from transformers import pipeline
+
+# Create a text generation pipeline
+text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
+
+# Define a prompt
+prompt = "###Human: Answer this question: What exactly does Viv do?\n###Assistant:"
+
+# Generate text
+generated_text = text_generator(prompt, max_length=100, num_return_sequences=1)
+
+# Print the generated text
+print(generated_text[0]['generated_text'])
+
+# pip install gradio
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import gradio as gr
+
+# Define the repository where your model is saved
+model_repo = "Dumele/viv-updated2"  # Replace with your actual repository name
+
+# Load the tokenizer from the repository
+tokenizer = AutoTokenizer.from_pretrained(model_repo)
+
+# Define the configuration with `disable_exllama` set to True
+quantization_config = GPTQConfig(bits=4, disable_exllama=True)
+
+# Load the model with the custom configuration
+model = AutoModelForCausalLM.from_pretrained(model_repo, quantization_config=quantization_config)
+
+# Move the model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Create a text generation pipeline
+text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
+
+def generate_response(prompt):
+    generated_text = text_generator(prompt, max_length=100, num_return_sequences=1)
+    return generated_text[0]['generated_text']
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
+    outputs="text",
+    title="Chat with VivBeta",
+    description="Enter a prompt to interact with the fine-tuned model."
+)
+
+iface.launch()
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%bash
+#