Update app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer
+import re
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM
+from transformers import BitsAndBytesConfig
+import torch
+
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM
+# The model that you want to train from the Hugging Face hub
+import os
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    HfArgumentParser,
+    TrainingArguments,
+    pipeline,
+    logging,
+    Trainer
+)
+from peft import LoraConfig, PeftModel
+from trl import SFTTrainer
+model_name ="bigscience/bloomz-7b1"
+
+# The instruction dataset to use
+
+# Fine-tuned model name
+new_model = "bigscience/bloomz-7b1"
+# LoRA attention dimension
+lora_r = 16
+lora_alpha = 16
+
+# Dropout probability for LoRA layers
+lora_dropout = 0.05
+use_4bit = True
+
+# Compute dtype for 4-bit base models
+bnb_4bit_compute_dtype = "float16"
+
+# Quantization type (fp4 or nf4)
+bnb_4bit_quant_type = "nf4"
+
+use_nested_quant = False
+
+output_dir = "./results"
+
+# Number of training epochs
+num_train_epochs = 1
+fp16 = False
+bf16 = False
+
+per_device_train_batch_size =1
+
+per_device_eval_batch_size = 4
+
+gradient_accumulation_steps = 8
+
+gradient_checkpointing = True
+
+max_grad_norm = 0.3
+
+learning_rate = 5e-5
+
+weight_decay = 0.001
+
+optim = "paged_adamw_8bit"
+
+lr_scheduler_type = "constant"
+
+max_steps = -1
+warmup_ratio = 0.03
+group_by_length = True
+
+save_steps = 100
+
+logging_steps = 25
+
+max_seq_length = False
+packing = False
+#device_map = {"": 0}
+compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=use_4bit,
+    bnb_4bit_quant_type=bnb_4bit_quant_type,
+    bnb_4bit_compute_dtype=compute_dtype,
+    bnb_4bit_use_double_quant=use_nested_quant,
+)
+
+# Check GPU compatibility with bfloat16
+if compute_dtype == torch.float16 and use_4bit:
+    major, _ = torch.cuda.get_device_capability()
+    if major >= 8:
+        print("=" * 80)
+        print("Your GPU supports bfloat16: accelerate training with bf16=True")
+        print("=" * 80)
+
+# Load base model
+config = PeftConfig.from_pretrained("mohamedemam/essay_checker")
+model = AutoModelForCausalLM.from_pretrained("nfaheem/Marcoroni-7b-DPO-Merge",    quantization_config=bnb_config)
+model = PeftModel.from_pretrained(model, "mohamedemam/essay_checker")
+model.eval()
+def chat_Format(context,quetion,answer):
+   return "Instruction:\n check answer is true or false of next quetion using context below:\n"+"context: "+context+"\nquetion:"+quetion+ f".\n#student answer: "+answer+".\n#response:"
+# Create a Wikipedia API instance
+
+
+# Function to generate questions and answers with configurable parameters
+def generate_qa(context,quetion,answer,max_new_token):
+    input_text = chat_Format(context,quetion,answer)
+    a = tokenizer(text=input_text, return_tensors='pt')
+    
+    # Generate with configurable parameters
+    output = model.generate(input_ids=a['input_ids'],attention_mask=w['attention_mask'],
+        max_new_tokens=max_new_token
+    )
+    #
+    generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+    formatted_output = "\n\n".join(set(generated_text))
+    return formatted_output
+iface = gr.Interface(
+    fn=generate_qa,
+    inputs=[    "text", "text2", "text3",
+
+        gr.inputs.Slider(minimum=1, maximum=100, default=3, step=1, label="max token"),
+
+
+    ],theme="red-black",
+    outputs=gr.outputs.Textbox(label="Generated Output"),
+    title="check answers",
+    description="put you context ",
+)
+# Launch the interface
+iface.launch()