update

llama_lora/ui/finetune_ui.py

@@ -334,15 +334,30 @@ Train data (first 10):
             time.sleep(2)
             return message
 
+        log_history = []
+
         class UiTrainerCallback(TrainerCallback):
             def _on_progress(self, args, state, control):
+                nonlocal log_history
+
                 if Global.should_stop_training:
                     control.should_training_stop = True
                 total_steps = (
                     state.max_steps if state.max_steps is not None else state.num_train_epochs * state.steps_per_epoch)
+                log_history = state.log_history
+                last_history = None
+                last_loss = None
+                if len(log_history) > 0:
+                    last_history = log_history[-1]
+                    last_loss = last_history.get('loss', None)
+
+                progress_detail = f"Epoch {math.ceil(state.epoch)}/{epochs}"
+                if last_loss is not None:
+                    progress_detail += f", Loss: {last_loss:.4f}"
+
                 progress(
                     (state.global_step, total_steps),
-                    desc=f"Training... (Epoch {math.ceil(state.epoch)}/{epochs}, Step {state.global_step}/{total_steps})"
+                    desc=f"Training... ({progress_detail})"
                 )
 
             def on_epoch_begin(self, args, state, control, **kwargs):
@@ -382,7 +397,12 @@ Train data (first 10):
             None,  # resume_from_checkpoint
             training_callbacks  # callbacks
         )
-        return "Done: " + str(results)
+
+        logs_str = "\n".join([json.dumps(log) for log in log_history]) or "None"
+
+        result_message = f"Training ended:\n{str(results)}\n\nLogs:\n{logs_str}"
+        print(result_message)
+        return result_message
 
     except Exception as e:
         raise gr.Error(e)
@@ -582,7 +602,7 @@ def finetune_ui():
 
                 with gr.Column():
                     model_name = gr.Textbox(
-                        lines=1, label="LoRA Model Name", value=random_name(),
+                        lines=1, label="LoRA Model Name", value=random_name,
                         elem_id="finetune_model_name",
                     )
 

llama_lora/ui/main_page.py

@@ -109,6 +109,12 @@ def main_page_custom_css():
         font-weight: 100;
     }
 
+    /*
+    .codemirror-wrapper .cm-editor .cm-gutters {
+        background-color: var(--background-fill-secondary);
+    }
+    */
+
     .error-message, .error-message p {
         color: var(--error-text-color) !important;
     }
@@ -161,12 +167,14 @@ def main_page_custom_css():
     }
 
     .inference_options_group {
-        margin-top: -12px;
+        margin-top: -16px;
+        margin-bottom: -16px;
     }
     .inference_options_group > .form {
         border-radius: 0;
         border-left: 0;
         border-right: 0;
+        border-bottom: 0;
         box-shadow: none;
     }
 
@@ -395,10 +403,22 @@ def main_page_custom_css():
         }
     }
 
+    /*
     #tokenizer_encoded_tokens_input_textbox .codemirror-wrapper,
     #tokenizer_decoded_text_input_textbox .codemirror-wrapper {
         margin-bottom: -20px;
     }
+    */
+    #tokenizer_encoded_tokens_input_textbox,
+    #tokenizer_decoded_text_input_textbox {
+        overflow: hidden !important;
+    }
+
+    /* in case if there's too many logs on the previous run and made the box too high */
+    #finetune_training_status:has(.wrap:not(.hide)) {
+        max-height: 160px;
+        height: 160px;
+    }
 
     .foot_stop_timeoutable_btn {
         align-self: flex-end;

llama_lora/ui/tokenizer_ui.py

@@ -66,12 +66,12 @@ def tokenizer_ui():
             things_that_might_timeout.append(decoding)
             things_that_might_timeout.append(encoding)
 
-            stop_timeoutable_btn = gr.Button(
-                "stop not-responding elements",
-                elem_id="inference_stop_timeoutable_btn",
-                elem_classes="foot_stop_timeoutable_btn")
-            stop_timeoutable_btn.click(
-                fn=None, inputs=None, outputs=None, cancels=things_that_might_timeout)
+        stop_timeoutable_btn = gr.Button(
+            "stop not-responding elements",
+            elem_id="inference_stop_timeoutable_btn",
+            elem_classes="foot_stop_timeoutable_btn")
+        stop_timeoutable_btn.click(
+            fn=None, inputs=None, outputs=None, cancels=things_that_might_timeout)
 
     tokenizer_ui_blocks.load(_js="""
     function tokenizer_ui_blocks_js() {

llama_lora/utils/prompter.py

@@ -48,7 +48,8 @@ class Prompter(object):
         elif "variables" in self.template:
             variable_names = self.template.get("variables")
             if type(variables) == dict:
-                variables = [variables.get(name, None) for name in variable_names]
+                variables = [variables.get(name, None)
+                             for name in variable_names]
             if "default" not in self.template:
                 raise ValueError(
                     f"The template {self.template_name} has \"variables\" defined but does not has a default prompt defined. Please do it like: '\"default\": \"prompt_with_instruction\"' to handle cases when a matching prompt can't be found.")
@@ -91,7 +92,9 @@ class Prompter(object):
     def get_response(self, output: str) -> str:
         if self.template_name == "None":
             return output
-        return output.split(self.template["response_split"])[1].strip()
+        return self.template["response_split"].join(
+            output.split(self.template["response_split"])[1:]
+        ).strip()
 
     def get_variable_names(self) -> List[str]:
         if self.template_name == "None":