diff --git a/generate/utils.py b/generate/utils.py
index e05ba98..19d3272 100644
--- a/generate/utils.py
+++ b/generate/utils.py
@@ -370,7 +370,6 @@ def clean_output(self, output: str, prompt: str) -> str:
 
 
 class InstructConfig(InferenceConfig):
-
     def __init__(self, prompted : bool = False, instruction_tag : str = "### Instruction", response_tag : str = "### Response"):
         super().__init__(prompted=prompted)
         self.instruction_tag = instruction_tag
@@ -401,6 +400,63 @@ def format_prompt(self, prompt : str) -> str:
     def clean_output(self, output: str, prompt: str) -> str:
         return clean_instruct_output(output, prompt, self.response_tag)
 
+class QwenConfig(InferenceConfig):
+    def __init__(self, prompted : bool = False):
+        super().__init__(prompted=prompted)
+
+    def get_dtype(self):
+        return torch.float16
+
+    def init_padding(self, tokenizer):
+        tokenizer.pad_token_id = tokenizer.eos_token_id  # for batching
+        tokenizer.padding_side = "left"   # for decoder-only models
+
+    def get_pad_token_id(self, tokenizer) -> int:
+        return tokenizer.eos_token_id
+
+    def get_eos_token_id(self, tokenizer) -> int:
+        return None
+    
+    def trust_remote_code(self) -> bool:
+        return False
+
+    def format_prompt(self, prompt : str) -> str:
+        if self.prompted:
+            return f"// filename: solutions/solution_1.cpp\n// here is the correct implementation of the coding exercise\n\n{prompt}"
+        return prompt.strip()
+
+    def clean_output(self, output: str, prompt: str) -> str:
+        return clean_output(output, prompt)
+
+class ChatMLConfig(InferenceConfig):
+    def __init__(self, prompted : bool = False):
+        super().__init__(prompted=prompted)
+
+    def get_dtype(self):
+        return torch.bfloat16
+
+    def init_padding(self, tokenizer):
+        tokenizer.pad_token_id = tokenizer.eos_token_id  # for batching
+        tokenizer.padding_side = "left"   # for decoder-only models
+
+    def get_pad_token_id(self, tokenizer) -> int:
+        return tokenizer.pad_token_id
+
+    def get_eos_token_id(self, tokenizer) -> int:
+        return tokenizer.eos_token_id
+    
+    def trust_remote_code(self) -> bool:
+        return False
+
+    def format_prompt(self, prompt : str) -> str:
+        function_name = get_function_name(prompt, "cuda" if "__global__" in prompt else "serial")
+        prompt = f"Complete the following c++ function.\n```c++{prompt.strip()}```\nWrite only the function {function_name} and no other code. Enclose your solution in ```c++ and ```."
+        prompt = f"<|im_start|>system\nYou are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+        return prompt
+
+    def clean_output(self, output: str, prompt: str) -> str:
+        return clean_instruct_output(output, prompt,"<|im_start|>assistant\n")
+
 def get_inference_config(model_name : str, **kwargs) -> InferenceConfig:
     if model_name == "bigcode/starcoderbase":
         return StarCoderConfig(**kwargs)
@@ -422,6 +478,12 @@ def get_inference_config(model_name : str, **kwargs) -> InferenceConfig:
         return InstructConfig(instruction_tag='Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:', response_tag='### Response:', **kwargs)
     elif model_name.startswith('hpcgroup/rlpf'):
         return InstructConfig(instruction_tag='### Instruction', response_tag='### Response', **kwargs)
+    elif model_name.startswith('Qwen/Qwen2.5') and 'Instruct' in model_name:
+        return ChatMLConfig(**kwargs)
+    elif model_name.startswith('Qwen/Qwen3'):
+        return ChatMLConfig(**kwargs)
+    elif model_name.startswith('Qwen/Qwen2.5'):
+        return QwenConfig(**kwargs)
     else:
         raise ValueError(f"Unknown model name: {model_name}")