diff --git a/OAI/types/model.py b/OAI/types/model.py
index 4f636e4..15cb760 100644
--- a/OAI/types/model.py
+++ b/OAI/types/model.py
@@ -1,6 +1,7 @@
 from pydantic import BaseModel, Field
 from time import time
 from typing import List, Optional
+from gen_logging import LogConfig
 
 class ModelCardParameters(BaseModel):
     max_seq_len: Optional[int] = 4096
@@ -14,6 +15,7 @@ class ModelCard(BaseModel):
     object: str = "model"
     created: int = Field(default_factory=lambda: int(time()))
     owned_by: str = "tabbyAPI"
+    logging: Optional[LogConfig] = None
     parameters: Optional[ModelCardParameters] = None
 
 class ModelList(BaseModel):
diff --git a/config_sample.yml b/config_sample.yml
index f69db6e..0e4ddd4 100644
--- a/config_sample.yml
+++ b/config_sample.yml
@@ -13,6 +13,14 @@ network:
   # The port to host on (default: 5000)
   port: 5000
 
+# Options for logging
+logging:
+  # Enable prompt logging (default: False)
+  prompt: False
+
+  # Enable generation parameter logging (default: False)
+  generation_params: False
+
 # Options for model overrides and loading
 model:
   # Overrides the directory to look for models (default: models)
diff --git a/gen_logging.py b/gen_logging.py
new file mode 100644
index 0000000..ff18ced
--- /dev/null
+++ b/gen_logging.py
@@ -0,0 +1,47 @@
+from typing import Dict
+from pydantic import BaseModel
+
+# Logging preference config
+class LogConfig(BaseModel):
+    prompt: bool = False
+    generation_params: bool = False
+
+# Global reference to logging preferences
+config = LogConfig()
+
+# Wrapper to set the logging config for generations
+def update_from_dict(options_dict: Dict[str, bool]):
+    global config
+
+    # Force bools on the dict
+    for value in options_dict.values():
+        if value is None:
+            value = False
+
+    config = LogConfig.parse_obj(options_dict)
+
+def broadcast_status():
+    enabled = []
+    if config.prompt:
+        enabled.append("prompts")
+
+    if config.generation_params:
+        enabled.append("generation params")
+
+    if len(enabled) > 0:
+        print("Generation logging is enabled for: " + ", ".join(enabled))
+    else:
+        print("Generation logging is disabled")
+
+# Logs generation parameters to console
+def log_generation_params(**kwargs):
+    if config.generation_params:
+        print(f"Generation options: {kwargs}\n")
+
+def log_prompt(prompt: str):
+    if config.prompt:
+        print(f"Prompt: {prompt if prompt else 'Empty'}\n")
+
+def log_response(response: str):
+    if config.prompt:
+        print(f"Response: {response if response else 'Empty'}\n")
diff --git a/main.py b/main.py
index 22455c1..9f4871d 100644
--- a/main.py
+++ b/main.py
@@ -1,6 +1,7 @@
 import uvicorn
 import yaml
 import pathlib
+import gen_logging
 from asyncio import CancelledError
 from auth import check_admin_key, check_api_key, load_auth_keys
 from fastapi import FastAPI, Request, HTTPException, Depends
@@ -81,7 +82,8 @@ async def get_current_model():
             rope_alpha = model_container.config.scale_alpha_value,
             max_seq_len = model_container.config.max_seq_len,
             prompt_template = unwrap(model_container.prompt_template, "auto")
-        )
+        ),
+        logging = gen_logging.config
     )
 
     if model_container.draft_config:
@@ -370,6 +372,13 @@ if __name__ == "__main__":
         )
         config = {}
 
+    # Override the generation log options if given
+    log_config = unwrap(config.get("logging"), {})
+    if log_config:
+        gen_logging.update_from_dict(log_config)
+
+    gen_logging.broadcast_status()
+
     # If an initial model name is specified, create a container and load the model
     model_config = unwrap(config.get("model"), {})
     if "model_name" in model_config:
diff --git a/model.py b/model.py
index d281dba..f34501d 100644
--- a/model.py
+++ b/model.py
@@ -14,6 +14,7 @@ from exllamav2.generator import(
 )
 from typing import List, Optional, Union
 from utils import coalesce, unwrap
+from gen_logging import log_generation_params, log_prompt, log_response
 
 # Bytes to reserve on first device when loading with auto split
 auto_split_reserve_bytes = 96 * 1024**2
@@ -351,13 +352,6 @@ class ModelContainer:
         stop_conditions: List[Union[str, int]] = unwrap(kwargs.get("stop"), [])
         ban_eos_token = unwrap(kwargs.get("ban_eos_token"), False)
 
-
-        # Ban the EOS token if specified. If not, append to stop conditions as well.
-        if ban_eos_token:
-            gen_settings.disallow_tokens(self.tokenizer, [self.tokenizer.eos_token_id])
-        else:
-            stop_conditions.append(self.tokenizer.eos_token_id)
-
         # Override sampler settings for temp = 0
         if gen_settings.temperature == 0:
             gen_settings.temperature = 1.0
@@ -365,7 +359,25 @@ class ModelContainer:
             gen_settings.top_p = 0
             gen_settings.typical = 0
 
-        # Stop conditions           
+        # Log generation options to console
+        log_generation_params(
+            **vars(gen_settings),
+            token_healing = token_healing,
+            max_tokens = max_tokens,
+            stop_conditions = stop_conditions
+        )
+
+        # Log prompt to console
+        log_prompt(prompt)
+
+        # Ban the EOS token if specified. If not, append to stop conditions as well.
+        # Set this below logging to avoid polluting the stop strings array
+        if ban_eos_token:
+            gen_settings.disallow_tokens(self.tokenizer, [self.tokenizer.eos_token_id])
+        else:
+            stop_conditions.append(self.tokenizer.eos_token_id)
+
+        # Stop conditions
         self.generator.set_stop_conditions(stop_conditions)
 
         # Tokenized context
@@ -430,9 +442,12 @@ class ModelContainer:
 
             if eos or generated_tokens == max_tokens: break
 
+        # Print response
+        log_response(full_response)
+
         elapsed_time = last_chunk_time - start_time
 
-        initial_response = f"Response: {generated_tokens} tokens generated in {round(elapsed_time, 2)} seconds"
+        initial_response = f"Metrics: {generated_tokens} tokens generated in {round(elapsed_time, 2)} seconds"
         itemization = []
         extra_parts = []