diff --git a/docs/tutorials/generator/speculative_decoding.md b/docs/tutorials/generator/speculative_decoding.md
index d416b39..4100eea 100644
--- a/docs/tutorials/generator/speculative_decoding.md
+++ b/docs/tutorials/generator/speculative_decoding.md
@@ -33,6 +33,21 @@ generator_model_platform_args:
     assistant_model: meta-llama/Llama-3.2-1B
 ```
 
+### Universal Assisted Decoding
+You may also use assistant model with different tokeniers from the target model. All you need to do is to explicitly specify the assistant tokenizer:
+
+```yaml
+generator_model_name: google/gemma-2-9b
+generator_model_platform: huggingface
+generator_model_platform_args:
+  hf_generate_params:
+    assistant_model: double7/vicuna-68m
+    assistant_tokenizer: double7/vicuna-68m
+```
+
+Note:  Transformers `v4.46.0` or above is required to support universal assisted decoding.
+
+
 ## Speculative Decoding with vLLM Models
 
 Speculative decoding with vLLM is also straightforward. Here is an example configuration that sets up vLLM in offline mode to use speculative decoding with a draft model, speculating 5 tokens at a time:
diff --git a/src/agrag/modules/generator/generators/hf_generator.py b/src/agrag/modules/generator/generators/hf_generator.py
index 2108ead..290dfa7 100644
--- a/src/agrag/modules/generator/generators/hf_generator.py
+++ b/src/agrag/modules/generator/generators/hf_generator.py
@@ -80,10 +80,17 @@ def __init__(
 
         self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, **self.hf_tokenizer_init_params)
         self.assistant_model_name = self.hf_generate_params.get("assistant_model", None)
+        self.assistant_tokenizer_name = self.hf_generate_params.get("assistant_tokenizer", None)
         logger.info(f"Using Huggingface Model {self.assistant_model_name} as the Assistant Model")
         if self.assistant_model_name:
             assistant_model = AutoModelForCausalLM.from_pretrained(self.assistant_model_name).to(self.device)
             self.hf_generate_params["assistant_model"] = assistant_model
+            # To support Universal Assisted Decoding
+            # See https://huggingface.co/docs/transformers/main/en/generation_strategies#universal-assisted-decoding
+            if self.assistant_tokenizer_name:
+                assistant_tokenizer = AutoTokenizer.from_pretrained(self.assistant_tokenizer_name)
+                self.hf_generate_params["assistant_tokenizer"] = assistant_tokenizer
+                self.hf_generate_params["tokenizer"] = self.tokenizer
         self.model = AutoModelForCausalLM.from_pretrained(self.model_name, **self.hf_model_params).to(self.device)
 
         if self.num_gpus > 1: