diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py
index a0adf4d0b8c03..3b1ed7617c9b4 100644
--- a/vllm/entrypoints/openai/api_server.py
+++ b/vllm/entrypoints/openai/api_server.py
@@ -199,9 +199,10 @@ async def create_chat_completion(request: ChatCompletionRequest,
         return error_check_ret
 
     if request.logit_bias is not None and len(request.logit_bias) > 0:
+        pass
         # TODO: support logit_bias in vLLM engine.
-        return create_error_response(HTTPStatus.BAD_REQUEST,
-                                     "logit_bias is not currently supported")
+        #return create_error_response(HTTPStatus.BAD_REQUEST,
+        #                             "logit_bias is not currently supported")
 
     prompt = await get_gen_prompt(request)
     token_ids, error_check_ret = await check_length(request, prompt=prompt)
@@ -228,6 +229,7 @@ async def create_chat_completion(request: ChatCompletionRequest,
             use_beam_search=request.use_beam_search,
             skip_special_tokens=request.skip_special_tokens,
             spaces_between_special_tokens=spaces_between_special_tokens,
+            logit_bias=request.logit_bias
         )
     except ValueError as e:
         return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
diff --git a/vllm/model_executor/layers/sampler.py b/vllm/model_executor/layers/sampler.py
index 6a29f1afd368b..688826fd8be64 100644
--- a/vllm/model_executor/layers/sampler.py
+++ b/vllm/model_executor/layers/sampler.py
@@ -44,7 +44,7 @@ def forward(
         hidden_states = _prune_hidden_states(hidden_states, input_metadata)
 
         # Get the logits for the next tokens.
-        logits = _get_logits(hidden_states, embedding, embedding_bias,
+        logits = _get_logits(hidden_states, embedding, embedding_bias if embedding_bias is not None else _get_logit_bias(input_metadata),
                              self.vocab_size)
 
         # Apply presence and frequency penalties.
@@ -217,6 +217,11 @@ def _apply_penalties(
     logits -= presence_penalties.unsqueeze(dim=1) * mask
     return logits
 
+def _get_logit_bias(input_metadata: InputMetadata) -> any:
+    logit_biases: any = []
+    for seq_group in input_metadata.seq_groups:
+        set_ids, sampling_params = seq_group
+        logit_biases += [sampling_params.logit_bias]
 
 def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
     # Collect the temperatures for the logits.
diff --git a/vllm/sampling_params.py b/vllm/sampling_params.py
index 00a9135a5ca7f..7fe4aed20f8f6 100644
--- a/vllm/sampling_params.py
+++ b/vllm/sampling_params.py
@@ -62,6 +62,7 @@ class SamplingParams:
             the stop tokens are sepcial tokens.
         ignore_eos: Whether to ignore the EOS token and continue generating
             tokens after the EOS token is generated.
+        logit_bias: Bias adjustment for different logits
         max_tokens: Maximum number of tokens to generate per output sequence.
         logprobs: Number of log probabilities to return per output token.
             Note that the implementation follows the OpenAI API: The return
@@ -92,6 +93,7 @@ def __init__(
         stop_token_ids: Optional[List[int]] = None,
         ignore_eos: bool = False,
         max_tokens: int = 16,
+        logit_bias: float = [],
         logprobs: Optional[int] = None,
         prompt_logprobs: Optional[int] = None,
         skip_special_tokens: bool = True,
@@ -122,6 +124,7 @@ def __init__(
         self.max_tokens = max_tokens
         self.logprobs = logprobs
         self.prompt_logprobs = prompt_logprobs
+        self.logit_bias = logit_bias
         self.skip_special_tokens = skip_special_tokens
         self.spaces_between_special_tokens = spaces_between_special_tokens