fixing layer_replication error 1

handler.py

@@ -4,57 +4,60 @@ from peft import PeftModel, PeftConfig
 import torch
 import time
 
-
 class EndpointHandler:
-	def __init__(self, path="samadeniyi/lora_lesson_plan_model"):
-		# Load the model configuration
-		config = PeftConfig.from_pretrained(path)
-		
-		# Define 4-bit quantization configuration
-		bnb_config = BitsAndBytesConfig(
-			load_in_4bit=True,
-			bnb_4bit_use_double_quant=True,
-			bnb_4bit_quant_type="nf4",
-			bnb_4bit_compute_dtype=torch.float16,
-		)
-		
-		# Load the model without extra arguments like `layer_replication`
-		self.model = AutoModelForCausalLM.from_pretrained(
-			config.base_model_name_or_path,
-			return_dict=True,
-			load_in_4bit=True,
-			device_map={"": 0},
-			trust_remote_code=True,
-			quantization_config=bnb_config,
-		)
-		
-		# Load tokenizer
-		self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-		self.tokenizer.pad_token = self.tokenizer.eos_token
-		
-		# Apply PEFT (Parameter-Efficient Fine-Tuning) to the model
-		self.model = PeftModel.from_pretrained(self.model, path)
-	
-	def __call__(self, data: Any) -> Dict[str, Any]:
-		"""
-		Args:
-			data :obj:`dict`:. The object should contain {"instruction": "some text", "input": "some text"}:
-				- "instruction": The instruction describing what to generate.
-				- "input": Context to guide the generation.
-
-		Returns:
-			A :obj:`dict` containing {"generated_text": "the generated lesson plan", "time": "..."}:
-				- "generated_text": The generated text based on the input.
-				- "time": The time taken to generate the output.
-		"""
-		
-		# Parse input data
-		inputs = data.pop("inputs", data)
-		instruction = inputs.get("instruction", "")
-		input_context = inputs.get("input", "")
-		
-		# Create the lesson plan prompt based on your preparation format
-		lesson_prompt = f"""Below is an instruction that describes how to create a lesson plan, paired with an input that provides further context. Write a response that appropriately completes the request.
+    def __init__(self, path="samadeniyi/lora_lesson_plan_model"):
+        # Load the model configuration
+        config = PeftConfig.from_pretrained(path)
+
+        # Filter out unsupported arguments like 'layer_replication'
+        if "layer_replication" in config.__dict__:
+            del config.__dict__["layer_replication"]
+
+        # Define 4-bit quantization configuration (this is necessary for low-memory usage)
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+        )
+
+        # Load the model using 4-bit quantization and optimized settings
+        self.model = AutoModelForCausalLM.from_pretrained(
+            config.base_model_name_or_path,
+            return_dict=True,
+            load_in_4bit=True,
+            device_map={"": 0},  # Map to CUDA device 0
+            trust_remote_code=True,
+            quantization_config=bnb_config,
+        )
+
+        # Load tokenizer and ensure it matches the model
+        self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        # Apply PEFT (Parameter-Efficient Fine-Tuning) to the model
+        self.model = PeftModel.from_pretrained(self.model, path)
+
+    def __call__(self, data: Any) -> Dict[str, Any]:
+        """
+        Args:
+            data :obj:`dict`:. The object should contain {"instruction": "some text", "input": "some text"}:
+                - "instruction": The instruction describing what to generate.
+                - "input": Context to guide the generation.
+
+        Returns:
+            A :obj:`dict` containing {"generated_text": "the generated lesson plan", "time": "..."}:
+                - "generated_text": The generated text based on the input.
+                - "time": The time taken to generate the output.
+        """
+
+        # Parse input data
+        inputs = data.pop("inputs", data)
+        instruction = inputs.get("instruction", "")
+        input_context = inputs.get("input", "")
+
+        # Create the lesson plan prompt based on your preparation format
+        lesson_prompt = f"""Below is an instruction that describes how to create a lesson plan, paired with an input that provides further context. Write a response that appropriately completes the request.
 
 ### Instruction:
 {instruction}
@@ -64,36 +67,36 @@ class EndpointHandler:
 
 ### Response:
 """
-		
-		# Tokenize the prompt
-		batch = self.tokenizer(
-			lesson_prompt,
-			padding=True,
-			truncation=True,
-			return_tensors='pt'
-		)
-		batch = batch.to('cuda:0')
-		
-		# Configure generation settings
-		generation_config = self.model.generation_config
-		generation_config.top_p = 0.7
-		generation_config.temperature = 0.7
-		generation_config.max_new_tokens = 256
-		generation_config.num_return_sequences = 1
-		generation_config.pad_token_id = self.tokenizer.eos_token_id
-		generation_config.eos_token_id = self.tokenizer.eos_token_id
-		
-		# Time the prediction
-		start = time.time()
-		with torch.cuda.amp.autocast():
-			output_tokens = self.model.generate(
-				input_ids=batch.input_ids,
-				generation_config=generation_config,
-			)
-		end = time.time()
-		
-		# Decode generated tokens into text
-		generated_text = self.tokenizer.decode(output_tokens[0], skip_special_tokens=True)
-		
-		# Return the generated text and the time taken
-		return {"generated_text": generated_text, "time": f"{(end - start):.2f} s"}
+
+        # Tokenize the prompt
+        batch = self.tokenizer(
+            lesson_prompt,
+            padding=True,
+            truncation=True,
+            return_tensors='pt'
+        )
+        batch = batch.to('cuda:0')
+
+        # Configure generation settings
+        generation_config = self.model.generation_config
+        generation_config.top_p = 0.7
+        generation_config.temperature = 0.7
+        generation_config.max_new_tokens = 256
+        generation_config.num_return_sequences = 1
+        generation_config.pad_token_id = self.tokenizer.eos_token_id
+        generation_config.eos_token_id = self.tokenizer.eos_token_id
+
+        # Time the prediction
+        start = time.time()
+        with torch.cuda.amp.autocast():
+            output_tokens = self.model.generate(
+                input_ids=batch.input_ids,
+                generation_config=generation_config,
+            )
+        end = time.time()
+
+        # Decode generated tokens into text
+        generated_text = self.tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+        # Return the generated text and the time taken
+        return {"generated_text": generated_text, "time": f"{(end-start):.2f} s"}

requirements.txt

@@ -1,4 +1,10 @@
-torch
-transformers
-optimum
-peft>=0.3.0
+torch>=2.0.0
+transformers>=4.25.0
+unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git
+xformers==0.0.27  # or the appropriate version for your Torch version
+trl
+peft
+accelerate
+bitsandbytes
+triton
+wandb