diff --git a/src/abstractions/model.py b/src/abstractions/model.py
index d223274..41d565b 100644
--- a/src/abstractions/model.py
+++ b/src/abstractions/model.py
@@ -21,6 +21,7 @@
 from src.abstractions.configs.templates_configs import *
 import multiprocessing
 from src.download_models import download_model
+from functools import partial
 
 from src.abstractions.backends import (
     start_inference_backend,
@@ -41,6 +42,7 @@ def inference_standalone(
     prompt_field_name: str,
     query_field_name: str,
     temperature: float,
+    max_tokens: int,
     backend_type: Literal["sglang", "vllm"],
     purpose: Literal["responses", "logprobs"],
     conn: multiprocessing.connection.Connection,
@@ -58,7 +60,7 @@ def inference_standalone(
         prompt_field_name=prompt_field_name, query_field_name=query_field_name
     )
     result_data = data.transform(
-        transformation=process_batch,
+        transformation=partial(process_batch, temperature=temperature, max_tokens=max_tokens),
         result_data_name=result_data_name,
         forced_rewrite=(
             Model.always_force_rewrite
@@ -666,7 +668,8 @@ def inference(
         result_data_name: str,
         backend: Literal["sglang", "vllm", "deepspeed", "serial"] = "sglang",
         batch_size_multiplier_log2: int = 0,
-        temperature=0.25,
+        temperature: float = 0.25,
+        max_tokens: int = 8192,
         purpose: Literal["responses", "logprobs"] = "responses",
     ) -> Union[Data, List[Dict[str, str]]]:
         """Performance inference on a dataset (currently only instruction datasets are tested, with the same format as SFT datasets),
@@ -684,9 +687,12 @@ def inference(
         :param batch_size_multiplier_log2: The log base 2 of the batch size multiplier
         :type batch_size_multiplier_log2: int = 0
 
-        :param temperature: The temperature parameter
+        :param temperature: The temperature parameter.
         :type temperature: float = 0.25
         
+        :param max_tokens: The maximum number of tokens to generate. Ignored if purpose is "logprobs".
+        :type max_tokens: int = 8192
+        
         :param purpose: The purpose of the inference. It can be "responses" or "logprobs". If "logprobs", the log probability of the prompt itself (and the assistant response supplied in the `predict` field, if exists) is returned in the `logprob` field of the resulting dataset, without doing any completion. If "responses", the completion text is saved in the `predict` field of the resulting dataset.
         :type purpose: Literal["responses", "logprobs"] = "responses"
 
@@ -753,7 +759,7 @@ def inference(
 
             result = (
                 self.__inference_parallel_segregated(
-                    data, result_data_name, temperature, backend, purpose
+                    data, result_data_name, temperature, max_tokens, backend, purpose
                 )
                 if backend in ["vllm", "sglang"]
                 else self.__inference_parallel_deepspeed(
@@ -786,7 +792,7 @@ def inference(
         return result
 
     def __inference_parallel_segregated(
-        self, data: Data, result_data_name: str, temperature: float, backend_type: str, purpose: str
+        self, data: Data, result_data_name: str, temperature: float, max_tokens: int, backend_type: str, purpose: str
     ) -> Data:
         """sglang/vllm implementation for `inference()`, but performed in a separate process to free up GPU memory. This is the recommended implementation, due to its superior speed and robustness."""
         data_path = data.data_path
@@ -814,6 +820,7 @@ def __inference_parallel_segregated(
                 prompt_field_name,
                 query_field_name,
                 temperature,
+                max_tokens,
                 backend_type,
                 purpose,
                 child_conn,