diff --git a/comps/cores/proto/api_protocol.py b/comps/cores/proto/api_protocol.py
index 2b2481067..54a99cfdb 100644
--- a/comps/cores/proto/api_protocol.py
+++ b/comps/cores/proto/api_protocol.py
@@ -797,3 +797,45 @@ class FileObject(BaseModel):
 
     Supported values are assistants, assistants_output, batch, batch_output, fine-tune, fine-tune-results and vision.
     """
+
+
+class Metrics(BaseModel):
+    full_valid_loss: Optional[float] = None
+
+    full_valid_mean_token_accuracy: Optional[float] = None
+
+    step: Optional[float] = None
+
+    train_loss: Optional[float] = None
+
+    train_mean_token_accuracy: Optional[float] = None
+
+    valid_loss: Optional[float] = None
+
+    valid_mean_token_accuracy: Optional[float] = None
+
+
+class FineTuningJobCheckpoint(BaseModel):
+    id: str
+    """The checkpoint identifier, which can be referenced in the API endpoints."""
+
+    created_at: int
+    """The Unix timestamp (in seconds) for when the checkpoint was created."""
+
+    fine_tuned_model_checkpoint: str
+    """The name of the fine-tuned checkpoint model that is created."""
+
+    fine_tuning_job_id: str
+    """The name of the fine-tuning job that this checkpoint was created from."""
+
+    fine_tuning_job_id: str
+    """The name of the fine-tuning job that this checkpoint was created from."""
+
+    metrics: Optional[Metrics] = None
+    """Metrics at the step number during the fine-tuning job."""
+
+    object: Literal["fine_tuning.job.checkpoint"]
+    """The object type, which is always "fine_tuning.job.checkpoint"."""
+
+    step_number: Optional[int] = None
+    """The step number that the checkpoint was created at."""
diff --git a/comps/finetuning/finetuning_service.py b/comps/finetuning/finetuning_service.py
index 958b08acb..64097c720 100644
--- a/comps/finetuning/finetuning_service.py
+++ b/comps/finetuning/finetuning_service.py
@@ -60,7 +60,7 @@ async def upload_training_files(request: UploadFileRequest = Depends(upload_file
 )
 def list_checkpoints(request: FineTuningJobIDRequest):
     checkpoints = handle_list_finetuning_checkpoints(request)
-    return {"status": 200, "checkpoints": str(checkpoints)}
+    return checkpoints
 
 
 if __name__ == "__main__":
diff --git a/comps/finetuning/handlers.py b/comps/finetuning/handlers.py
index b97231485..ddae0726f 100644
--- a/comps/finetuning/handlers.py
+++ b/comps/finetuning/handlers.py
@@ -17,6 +17,7 @@
 from comps.cores.proto.api_protocol import (
     FileObject,
     FineTuningJob,
+    FineTuningJobCheckpoint,
     FineTuningJobIDRequest,
     FineTuningJobList,
     FineTuningJobsRequest,
@@ -38,6 +39,9 @@
     os.mkdir(OUTPUT_DIR)
 
 FineTuningJobID = str
+CheckpointID = str
+CheckpointPath = str
+
 CHECK_JOB_STATUS_INTERVAL = 5  # Check every 5 secs
 
 global ray_client
@@ -45,6 +49,7 @@
 
 running_finetuning_jobs: Dict[FineTuningJobID, FineTuningJob] = {}
 finetuning_job_to_ray_job: Dict[FineTuningJobID, str] = {}
+checkpoint_id_to_checkpoint_path: Dict[CheckpointID, CheckpointPath] = {}
 
 
 # Add a background task to periodicly update job status
@@ -117,8 +122,6 @@ def handle_create_finetuning_jobs(request: FineTuningParams, background_tasks: B
     ray_job_id = ray_client.submit_job(
         # Entrypoint shell command to execute
         entrypoint=f"python finetune_runner.py --config_file {finetune_config_file}",
-        # Path to the local directory that contains the script.py file
-        runtime_env={"working_dir": "./", "excludes": [f"{OUTPUT_DIR}"]},
     )
 
     logger.info(f"Submitted Ray job: {ray_job_id} ...")
@@ -183,10 +186,21 @@ def handle_list_finetuning_checkpoints(request: FineTuningJobIDRequest):
     job = running_finetuning_jobs.get(fine_tuning_job_id)
     if job is None:
         raise HTTPException(status_code=404, detail=f"Fine-tuning job '{fine_tuning_job_id}' not found!")
-    output_dir = os.path.join(JOBS_PATH, job.id)
+    output_dir = os.path.join(OUTPUT_DIR, job.id)
     checkpoints = []
     if os.path.exists(output_dir):
-        checkpoints = os.listdir(output_dir)
+        # Iterate over the contents of the directory and add an entry for each
+        for _ in os.listdir(output_dir):  # Loop over directory contents
+            checkpointsResponse = FineTuningJobCheckpoint(
+                id=f"ftckpt-{uuid.uuid4()}",  # Generate a unique ID
+                created_at=int(time.time()),  # Use the current timestamp
+                fine_tuned_model_checkpoint=output_dir,  # Directory path itself
+                fine_tuning_job_id=fine_tuning_job_id,
+                object="fine_tuning.job.checkpoint",
+            )
+            checkpoints.append(checkpointsResponse)
+            checkpoint_id_to_checkpoint_path[checkpointsResponse.id] = checkpointsResponse.fine_tuned_model_checkpoint
+
     return checkpoints