update

app.py

@@ -42,7 +42,7 @@ def main(
     with gr.Blocks(title=get_page_title(), css=main_page_custom_css()) as demo:
         main_page()
 
-    demo.queue().launch(server_name=server_name, share=share)
+    demo.queue(concurrency_count=2).launch(server_name=server_name, share=share)
 
 
 if __name__ == "__main__":

llama_lora/lib/finetune.py

@@ -214,10 +214,9 @@ def train(
     if torch.__version__ >= "2" and sys.platform != "win32":
         model = torch.compile(model)
 
-    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+    result = trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
     model.save_pretrained(output_dir)
+    print(f"Model saved to {output_dir}.")
 
-    print(
-        "\n If there's a warning about missing keys above, please disregard :)"
-    )
+    return result

llama_lora/ui/finetune_ui.py

@@ -360,7 +360,7 @@ Train data (first 10):
 
         Global.should_stop_training = False
 
-        return Global.train_fn(
+        return "Done. " + Global.train_fn(
             get_base_model(),  # base_model
             get_tokenizer(),  # tokenizer
             os.path.join(Global.data_dir, "lora_models",
@@ -529,19 +529,19 @@ def finetune_ui():
         with gr.Row():
             with gr.Column():
                 micro_batch_size = gr.Slider(
-                    minimum=1, maximum=100, value=1,
+                    minimum=1, maximum=100, step=1, value=4,
                     label="Micro Batch Size",
                     info="The number of examples in each mini-batch for gradient computation. A smaller micro_batch_size reduces memory usage but may increase training time."
                 )
 
                 gradient_accumulation_steps = gr.Slider(
-                    minimum=1, maximum=10, value=1,
+                    minimum=1, maximum=10, step=1, value=32,
                     label="Gradient Accumulation Steps",
                     info="The number of steps to accumulate gradients before updating model parameters. This can be used to simulate a larger effective batch size without increasing memory usage."
                 )
 
                 epochs = gr.Slider(
-                    minimum=1, maximum=100, value=1,
+                    minimum=1, maximum=100, step=1, value=1,
                     label="Epochs",
                     info="The number of times to iterate over the entire training dataset. A larger number of epochs may improve model performance but also increase the risk of overfitting.")
 
@@ -553,19 +553,19 @@ def finetune_ui():
 
             with gr.Column():
                 lora_r = gr.Slider(
-                    minimum=1, maximum=16, value=8,
+                    minimum=1, maximum=16, step=1, value=8,
                     label="LoRA R",
                     info="The rank parameter for LoRA, which controls the dimensionality of the rank decomposition matrices. A larger lora_r increases the expressiveness and flexibility of LoRA but also increases the number of trainable parameters and memory usage."
                 )
 
                 lora_alpha = gr.Slider(
-                    minimum=1, maximum=128, value=16,
+                    minimum=1, maximum=128, step=1, value=16,
                     label="LoRA Alpha",
                     info="The scaling parameter for LoRA, which controls how much LoRA affects the original pre-trained model weights. A larger lora_alpha amplifies the impact of LoRA but may also distort or override the pre-trained knowledge."
                 )
 
                 lora_dropout = gr.Slider(
-                    minimum=0, maximum=1, value=0.01,
+                    minimum=0, maximum=1, value=0.05,
                     label="LoRA Dropout",
                     info="The dropout probability for LoRA, which controls the fraction of LoRA parameters that are set to zero during training. A larger lora_dropout increases the regularization effect of LoRA but also increases the risk of underfitting."
                 )

llama_lora/ui/inference_ui.py

@@ -335,6 +335,14 @@ def inference_ui():
       // Add tooltips
       setTimeout(function () {
 
+        tippy("#inference_lora_model", {
+          placement: 'bottom-start',
+          delay: [500, 0],
+          animation: 'scale-subtle',
+          content: 'Select a LoRA model form your data directory, or type in a model name on HF (e.g.: <code>tloen/alpaca-lora-7b</code>).',
+          allowHTML: true,
+        });
+
         tippy("#inference_prompt_template", {
           placement: 'bottom-start',
           delay: [500, 0],