update

llama_lora/ui/finetune_ui.py

@@ -360,7 +360,7 @@ Train data (first 10):
 
         Global.should_stop_training = False
 
-        return "Done. " + Global.train_fn(
+        return Global.train_fn(
             get_base_model(),  # base_model
             get_tokenizer(),  # tokenizer
             os.path.join(Global.data_dir, "lora_models",
@@ -535,13 +535,13 @@ def finetune_ui():
                 )
 
                 gradient_accumulation_steps = gr.Slider(
-                    minimum=1, maximum=10, step=1, value=32,
+                    minimum=1, maximum=10, step=1, value=1,
                     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, step=1, value=1,
+                    minimum=1, maximum=100, step=1, value=3,
                     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.")
 

llama_lora/ui/inference_ui.py

@@ -326,122 +326,144 @@ def inference_ui():
     function inference_ui_blocks_js() {
       // Auto load options
       setTimeout(function () {
-        document.getElementById("inference_reload_selections_button").click();
+        document.getElementById('inference_reload_selections_button').click();
 
         // Workaround default value not shown.
-        document.querySelector('#inference_lora_model input').value = "tloen/alpaca-lora-7b";
+        document.querySelector('#inference_lora_model input').value =
+          'tloen/alpaca-lora-7b';
       }, 100);
 
       // Add tooltips
       setTimeout(function () {
-
-        tippy("#inference_lora_model", {
+        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>).',
+          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", {
+        tippy('#inference_prompt_template', {
           placement: 'bottom-start',
           delay: [500, 0],
           animation: 'scale-subtle',
-          content: 'Templates are loaded from the "templates" folder of your data directory. Be sure to select the template that matches your selected LoRA model to get the best results.',
+          content:
+            'Templates are loaded from the "templates" folder of your data directory. Be sure to select the template that matches your selected LoRA model to get the best results.',
         });
 
-        tippy("#inference_reload_selections_button", {
+        tippy('#inference_reload_selections_button', {
           placement: 'bottom-end',
           delay: [500, 0],
           animation: 'scale-subtle',
           content: 'Press to reload LoRA Model and Prompt Template selections.',
         });
 
-        document.querySelector('#inference_preview_prompt_container .label-wrap').addEventListener('click', function () {
-          tippy("#inference_preview_prompt", {
+        document
+          .querySelector('#inference_preview_prompt_container .label-wrap')
+          .addEventListener('click', function () {
+            tippy('#inference_preview_prompt', {
+              placement: 'right',
+              delay: [500, 0],
+              animation: 'scale-subtle',
+              content: 'This is the prompt that will be sent to the language model.',
+            });
+          });
+
+        function setTooltipForOptions() {
+          tippy('#inference_temperature', {
             placement: 'right',
             delay: [500, 0],
             animation: 'scale-subtle',
-            content: 'This is the prompt that will be sent to the language model.',
+            content:
+              'Controls randomness: Lowering results in less random completions. Higher values (e.g., 1.0) make the model generate more diverse and random outputs. As the temperature approaches zero, the model will become deterministic and repetitive.',
           });
-        });
-
-        tippy("#inference_temperature", {
-          placement: 'right',
-          delay: [500, 0],
-          animation: 'scale-subtle',
-          content: 'Controls randomness: Lowering results in less random completions. Higher values (e.g., 1.0) make the model generate more diverse and random outputs. As the temperature approaches zero, the model will become deterministic and repetitive.',
-        });
 
-        tippy("#inference_top_p", {
-          placement: 'right',
-          delay: [500, 0],
-          animation: 'scale-subtle',
-          content: 'Controls diversity via nucleus sampling: only the tokens whose cumulative probability exceeds "top_p" are considered. 0.5 means half of all likelihood-weighted options are considered.',
-        });
-
-        tippy("#inference_top_k", {
-          placement: 'right',
-          delay: [500, 0],
-          animation: 'scale-subtle',
-          content: 'Controls diversity of the generated text by only considering the "top_k" tokens with the highest probabilities. This method can lead to more focused and coherent outputs by reducing the impact of low probability tokens.',
-        });
+          tippy('#inference_top_p', {
+            placement: 'right',
+            delay: [500, 0],
+            animation: 'scale-subtle',
+            content:
+              'Controls diversity via nucleus sampling: only the tokens whose cumulative probability exceeds "top_p" are considered. 0.5 means half of all likelihood-weighted options are considered.',
+          });
 
-        tippy("#inference_beams", {
-          placement: 'right',
-          delay: [500, 0],
-          animation: 'scale-subtle',
-          content: 'Number of candidate sequences explored in parallel during text generation using beam search. A higher value increases the chances of finding high-quality, coherent output, but may slow down the generation process.',
-        });
+          tippy('#inference_top_k', {
+            placement: 'right',
+            delay: [500, 0],
+            animation: 'scale-subtle',
+            content:
+              'Controls diversity of the generated text by only considering the "top_k" tokens with the highest probabilities. This method can lead to more focused and coherent outputs by reducing the impact of low probability tokens.',
+          });
 
-        tippy("#inference_repetition_penalty", {
-          placement: 'right',
-          delay: [500, 0],
-          animation: 'scale-subtle',
-          content: 'Applies a penalty to the probability of tokens that have already been generated, discouraging the model from repeating the same words or phrases. The penalty is applied by dividing the token probability by a factor based on the number of times the token has appeared in the generated text.',
-        });
+          tippy('#inference_beams', {
+            placement: 'right',
+            delay: [500, 0],
+            animation: 'scale-subtle',
+            content:
+              'Number of candidate sequences explored in parallel during text generation using beam search. A higher value increases the chances of finding high-quality, coherent output, but may slow down the generation process.',
+          });
 
-        tippy("#inference_max_new_tokens", {
-          placement: 'right',
-          delay: [500, 0],
-          animation: 'scale-subtle',
-          content: 'Limits the maximum number of tokens generated in a single iteration.',
-        });
+          tippy('#inference_repetition_penalty', {
+            placement: 'right',
+            delay: [500, 0],
+            animation: 'scale-subtle',
+            content:
+              'Applies a penalty to the probability of tokens that have already been generated, discouraging the model from repeating the same words or phrases. The penalty is applied by dividing the token probability by a factor based on the number of times the token has appeared in the generated text.',
+          });
 
-        tippy("#inference_stream_output", {
-          placement: 'right',
-          delay: [500, 0],
-          animation: 'scale-subtle',
-          content: 'When enabled, generated text will be displayed in real-time as it is being produced by the model, allowing you to observe the text generation process as it unfolds.',
-        });
+          tippy('#inference_max_new_tokens', {
+            placement: 'right',
+            delay: [500, 0],
+            animation: 'scale-subtle',
+            content:
+              'Limits the maximum number of tokens generated in a single iteration.',
+          });
 
+          tippy('#inference_stream_output', {
+            placement: 'right',
+            delay: [500, 0],
+            animation: 'scale-subtle',
+            content:
+              'When enabled, generated text will be displayed in real-time as it is being produced by the model, allowing you to observe the text generation process as it unfolds.',
+          });
+        }
+        setTooltipForOptions();
+
+        const inference_options_accordion_toggle = document.querySelector(
+          '#inference_options_accordion .label-wrap'
+        );
+        if (inference_options_accordion_toggle) {
+          inference_options_accordion_toggle.addEventListener('click', function () {
+            setTooltipForOptions();
+          });
+        }
       }, 100);
 
       // Show/hide generate and stop button base on the state.
       setTimeout(function () {
         // Make the '#inference_output > .wrap' element appear
-        document.getElementById("inference_stop_btn").click();
+        document.getElementById('inference_stop_btn').click();
 
         setTimeout(function () {
           const output_wrap_element = document.querySelector(
-            "#inference_output > .wrap"
+            '#inference_output > .wrap'
           );
           function handle_output_wrap_element_class_change() {
-            if (Array.from(output_wrap_element.classList).includes("hide")) {
-              document.getElementById("inference_generate_btn").style.display =
-                "block";
-              document.getElementById("inference_stop_btn").style.display = "none";
+            if (Array.from(output_wrap_element.classList).includes('hide')) {
+              document.getElementById('inference_generate_btn').style.display =
+                'block';
+              document.getElementById('inference_stop_btn').style.display = 'none';
             } else {
-              document.getElementById("inference_generate_btn").style.display =
-                "none";
-              document.getElementById("inference_stop_btn").style.display = "block";
+              document.getElementById('inference_generate_btn').style.display =
+                'none';
+              document.getElementById('inference_stop_btn').style.display = 'block';
             }
           }
           new MutationObserver(function (mutationsList, observer) {
             handle_output_wrap_element_class_change();
           }).observe(output_wrap_element, {
             attributes: true,
-            attributeFilter: ["class"],
+            attributeFilter: ['class'],
           });
           handle_output_wrap_element_class_change();
         }, 500);

llama_lora/ui/main_page.py

@@ -182,6 +182,7 @@ def main_page_custom_css():
 
     #finetune_dataset_from_data_dir {
         border: 0;
+        box-shadow: none;
     }
 
     @media screen and (min-width: 640px) {

templates/alpaca_sample.json

@@ -1,6 +1,7 @@
 {
   "description": "Sample Alpaca-LoRA template which declares the variables used.",
   "variables": ["instruction", "input"],
+  "default": "prompt_with_instruction",
   "prompt_with_instruction_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
   "prompt_with_instruction": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
   "response_split": "### Response:"