Upload app.py

app.py

@@ -1,440 +1,456 @@
-import gradio as gr
-from huggingface_hub import HfApi
-from unsloth import FastLanguageModel, is_bfloat16_supported
-from unsloth.chat_templates import get_chat_template, train_on_responses_only
-
-from trl import SFTTrainer
-from transformers import TrainingArguments, TrainerCallback, DataCollatorForSeq2Seq
-import torch
-from datasets import load_dataset
-import time
-import psutil
-import platform
-import os
-
-hf_user = None
-try:
-    hfApi = HfApi()
-    hf_user = hfApi.whoami()["name"]
-except Exception as e:
-    hf_user = "not logged in"
-
-def get_human_readable_size(size, decimal_places=2):
-    for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
-        if size < 1024.0:
-            break
-        size /= 1024.0
-    return f"{size:.{decimal_places}f} {unit}"
-
-
-# get cpu stats
-disk_stats = psutil.disk_usage('.')
-print(get_human_readable_size(disk_stats.total))
-cpu_info = platform.processor()
-print(cpu_info)
-os_info = platform.platform()
-print(os_info)
-
-memory = psutil.virtual_memory()
-
-# Dropdown options
-model_options = [
-    "unsloth/Meta-Llama-3.1-8B-bnb-4bit",
-    "unsloth/Llama-3.2-1B-bnb-4bit",
-    "unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
-    "unsloth/Llama-3.2-3B-bnb-4bit",
-    "unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
-    "unsloth/mistral-7b-v0.3-bnb-4bit",      # New Mistral v3 2x faster!
-    "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
-    "unsloth/llama-3-8b-bnb-4bit",           # Llama-3 15 trillion tokens model 2x faster!
-    "unsloth/llama-3-8b-Instruct-bnb-4bit",
-    "unsloth/llama-3-70b-bnb-4bit",
-    "unsloth/Phi-3-mini-4k-instruct",        # Phi-3 2x faster!
-    "unsloth/Phi-3-medium-4k-instruct",
-    "unsloth/mistral-7b-bnb-4bit",
-    "unsloth/gemma-2-9b-bnb-4bit",
-    "unsloth/gemma-2-9b-bnb-4bit-instruct",
-    "unsloth/gemma-2-27b-bnb-4bit",          # Gemma 2x faster!
-    "unsloth/gemma-2-27b-bnb-4bit-instruct",          # Gemma 2x faster!
-    "unsloth/Qwen2-1.5B-bnb-4bit",         
-    "unsloth/Qwen2-1.5B-bnb-4bit-instruct",         
-    "unsloth/Qwen2-7B-bnb-4bit",          
-    "unsloth/Qwen2-7B-bnb-4bit-instruct",          
-    "unsloth/Qwen2-72B-bnb-4bit",         
-    "unsloth/Qwen2-72B-bnb-4bit-instruct",         
-    "unsloth/yi-6b-bnb-4bit",          
-    "unsloth/yi-34b-bnb-4bit",        
-]
-gpu_stats = torch.cuda.get_device_properties(0)
-start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
-max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
-
-running_on_hf = False
-if os.getenv("SYSTEM", None) == "spaces":
-    running_on_hf = True
-
-system_info = f"""\
-- **System:** {os_info}
-- **CPU:** {cpu_info} **Memory:** {get_human_readable_size(memory.free)} free of {get_human_readable_size(memory.total)}
-- **GPU:** {gpu_stats.name} ({max_memory} GB)
-- **Disk:** {get_human_readable_size(disk_stats.free)} free of {get_human_readable_size(disk_stats.total)}
-- **Hugging Face:** {running_on_hf}
-"""
-
-model=None
-tokenizer = None
-dataset = None
-max_seq_length = 2048
-
-class PrinterCallback(TrainerCallback):
-    step = 0
-    def __init__(self, progress):
-        self.progress = progress
-    def on_log(self, args, state, control, logs=None, **kwargs):
-        _ = logs.pop("total_flos", None)
-        if state.is_local_process_zero:
-            #print(logs)
-            pass
-    def on_step_end(self, args, state, control, **kwargs):
-        if state.is_local_process_zero:
-            self.step = state.global_step
-            self.progress(self.step/60, desc=f"Training {self.step}/60")
-            #print("**Step ", state.global_step)
-
-    
-
-
-def formatting_prompts_func(examples, prompt):
-    global tokenizer
-    instructions = examples["instruction"]
-    inputs       = examples["input"]
-    outputs      = examples["output"]
-    
-    texts = []
-    for instruction, input, output in zip(instructions, inputs, outputs):
-        conversation = [
-            {
-                "role": "system",
-                "content": instruction + tokenizer.eos_token
-            },
-            {
-                "role": "user",
-                "content": input + tokenizer.eos_token
-            },
-            {
-                "role": "assistant",
-                "content": output + tokenizer.eos_token
-            }
-        ]
-        text = tokenizer.apply_chat_template(
-            conversation, tokenize=False, add_generation_prompt=False
-        )
-        
-        texts.append(text)
-    
-    return { "text" : texts }
-
-def load_model(initial_model_name, load_in_4bit, max_sequence_length, hub_token):
-    global model, tokenizer, max_seq_length
-    dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
-    max_seq_length = max_sequence_length
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name = initial_model_name,
-        max_seq_length = max_sequence_length,
-        dtype = dtype,
-        load_in_4bit = load_in_4bit,
-        token = f"{hub_token}", # use one if using gated models like meta-llama/Llama-2-7b-hf
-    )
-    tokenizer = get_chat_template(
-        tokenizer,
-        chat_template="llama-3.1",
-    )
-    return f"Model {initial_model_name} loaded, using {max_sequence_length} as max sequence length.", gr.update(visible=True, interactive=True), gr.update(interactive=True),gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
-
-def load_data(dataset_name, data_template_style, data_template):
-    global dataset
-    dataset = load_dataset(dataset_name, split = "train")
-    dataset = dataset.map(lambda examples: formatting_prompts_func(examples, data_template), batched=True)
-    
-    return f"Data loaded {len(dataset)} records loaded.", gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True)
-
-def inference(prompt, input_text):
-    FastLanguageModel.for_inference(model) # Enable native 2x faster inference
-    inputs = tokenizer(
-    [
-        prompt.format(
-            "Continue the fibonnaci sequence.", # instruction
-            "1, 1, 2, 3, 5, 8", # input
-            "", # output - leave this blank for generation!
-        )
-    ], return_tensors = "pt").to("cuda")
-
-    outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
-    result = tokenizer.batch_decode(outputs)
-    return result[0], gr.update(visible=True, interactive=True)
-
-def save_model(model_name, hub_model_name, hub_token, gguf_16bit, gguf_8bit, gguf_4bit, gguf_custom, gguf_custom_value, merge_16bit, merge_4bit, just_lora, push_to_hub, progress=gr.Progress()):
-    global model, tokenizer
-
-    quants = []
-    current_quant = 0
-
-    if gguf_custom:
-        gguf_custom_value = gguf_custom_value
-        quants.append(gguf_custom_value)
-    else:
-        gguf_custom_value = None
-    
-    if gguf_16bit:
-        quants.append("f16")
-    if gguf_8bit:
-        quants.append("q8_0")
-    if gguf_4bit:
-        quants.append("q4_k_m")
-    
-    if merge_16bit:
-        progress(current_quant/len(quants), desc=f"Pushing model merged 16bit {model_name} to HuggingFace Hub")
-        model.save_pretrained_merged(
-            "model",
-            tokenizer,
-            save_method="merged_16bit",
-        )
-        if push_to_hub:
-            model.push_to_hub_merged(hub_model_name, tokenizer, save_method="merged_16bit", token=hub_token)
-
-    elif merge_4bit:
-        progress(current_quant/len(quants), desc=f"Pushing model merged 4bit {model_name} to HuggingFace Hub")
-        model.save_pretrained_merged(
-            "model",
-            tokenizer,
-            save_method="merged_4bit",
-        )
-        if push_to_hub:
-            model.push_to_hub_merged(hub_model_name, tokenizer, save_method="merged_4bit",  token=hub_token)
-
-    elif just_lora:
-        progress(current_quant/len(quants), desc=f"Pushing model merged lora {model_name} to HuggingFace Hub")
-        model.save_pretrained_merged(
-            "model",
-            tokenizer,
-            save_method="lora",
-        )
-        if push_to_hub:
-            model.push_to_hub_merged(hub_model_name, tokenizer, save_method="lora",  token=hub_token)
-
-    if push_to_hub:
-        current_quant = 0
-        for q in quants:
-            progress(current_quant/len(quants), desc=f"Pushing model {model_name} with {q} to HuggingFace Hub")
-            model.push_to_hub_gguf(hub_model_name, tokenizer, quantization_method=q, token=hub_token)
-            current_quant += 1
-    return "Model saved", gr.update(visible=True, interactive=True)
-
-def username(profile: gr.OAuthProfile | None):
-    hf_user = profile["name"] if profile else "not logged in"
-    return hf_user
-
-# Create the Gradio interface
-with gr.Blocks(title="Unsloth fine-tuning") as demo:
-    if (running_on_hf):
-        gr.LoginButton()
-    # logged_user = gr.Markdown(f"**User:** {hf_user}")
-    #demo.load(username, inputs=None, outputs=logged_user)
-    with gr.Row():
-        with gr.Column(scale=0.5):
-            gr.Image("unsloth.png", width="300px", interactive=False, show_download_button=False, show_label=False, show_share_button=False)
-        with gr.Column(min_width="550px", scale=1):
-            gr.Markdown(system_info) 
-        with gr.Column(min_width="250px", scale=0.3):
-            gr.Markdown(f"**Links:**\n\n* [Unsloth Hub](https://huggingface.co/unsloth)\n\n* [Unsloth Docs](http://docs.unsloth.com/)\n\n* [Unsloth GitHub](https://github.com/unslothai/unsloth)")
-    with gr.Tab("Base Model Parameters"):
-
-        with gr.Row():
-            initial_model_name = gr.Dropdown(choices=model_options, label="Select Base Model", allow_custom_value=True)
-            load_in_4bit = gr.Checkbox(label="Load 4bit model", value=True)
-
-        gr.Markdown("### Target Model Parameters")
-        with gr.Row():
-            max_sequence_length = gr.Slider(minimum=128, value=512, step=64, maximum=128*1024, interactive=True, label="Max Sequence Length")
-        load_btn = gr.Button("Load")
-        output = gr.Textbox(label="Model Load Status", value="Model not loaded", interactive=False)
-        gr.Markdown("---")
-
-    with gr.Tab("Data Preparation"):
-        with gr.Row():
-            dataset_name = gr.Textbox(label="Dataset Name", value="yahma/alpaca-cleaned")
-            data_template_style = gr.Dropdown(label="Template", choices=["alpaca","custom"], value="alpaca",  allow_custom_value=True)
-        with gr.Row():
-            data_template =  gr.TextArea(label="Data Template", value="""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-### Instruction:
-{}
-
-### Input:
-{}
-
-### Response:
-{}""")
-        gr.Markdown("---")
-        output_load_data = gr.Textbox(label="Data Load Status", value="Data not loaded", interactive=False)
-        load_data_btn = gr.Button("Load Dataset", interactive=True)
-        load_data_btn.click(load_data, inputs=[dataset_name, data_template_style, data_template], outputs=[output_load_data, load_data_btn])
-
-    with gr.Tab("Fine-Tuning"):
-        gr.Markdown("""### Fine-Tuned Model Parameters""")
-        with gr.Row():
-            model_name = gr.Textbox(label="Model Name", value=initial_model_name.value, interactive=True)
-
-        gr.Markdown("""### Lora Parameters""")
-
-        with gr.Row():
-            lora_r = gr.Number(label="R", value=16, interactive=True)
-            lora_alpha = gr.Number(label="Lora Alpha", value=16, interactive=True)
-            lora_dropout = gr.Number(label="Lora Dropout", value=0.1, interactive=True)
-
-        gr.Markdown("---")
-        gr.Markdown("""### Training Parameters""")
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    per_device_train_batch_size = gr.Number(label="Per Device Train Batch Size", value=2, interactive=True)
-                    warmup_steps = gr.Number(label="Warmup Steps", value=5, interactive=True)
-                    max_steps = gr.Number(label="Max Steps", value=60, interactive=True)
-                    gradient_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=4, interactive=True)
-                with gr.Row():
-                    logging_steps = gr.Number(label="Logging Steps", value=1, interactive=True)
-                    log_to_tensorboard = gr.Checkbox(label="Log to Tensorboard", value=True, interactive=True)
-
-                with gr.Row():
-                #     optim = gr.Dropdown(choices=["adamw_8bit", "adamw", "sgd"], label="Optimizer", value="adamw_8bit")
-                    learning_rate = gr.Number(label="Learning Rate", value=2e-4, interactive=True)
-
-                # with gr.Row():
-                    weight_decay = gr.Number(label="Weight Decay", value=0.01, interactive=True)
-                    # lr_scheduler_type = gr.Dropdown(choices=["linear", "cosine", "constant"], label="LR Scheduler Type", value="linear")
-        gr.Markdown("---")
-
-        with gr.Row():
-            seed = gr.Number(label="Seed", value=3407, interactive=True)
-            output_dir = gr.Textbox(label="Output Directory", value="outputs", interactive=True)
-        gr.Markdown("---")
-
-        train_output = gr.Textbox(label="Training Status", value="Model not trained", interactive=False)
-        train_btn = gr.Button("Train", visible=True)
-
-        def train_model(model_name: str, lora_r: int, lora_alpha: int, lora_dropout: float, per_device_train_batch_size: int, warmup_steps: int, max_steps: int,
-                gradient_accumulation_steps: int, logging_steps: int, log_to_tensorboard: bool, learning_rate, weight_decay, seed: int, output_dir, progress= gr.Progress()):
-            global model, tokenizer
-            print(f"$$$ Training model {model_name} with {lora_r} R, {lora_alpha} alpha, {lora_dropout} dropout, {per_device_train_batch_size} per device train batch size, {warmup_steps} warmup steps, {max_steps} max steps, {gradient_accumulation_steps} gradient accumulation steps, {logging_steps} logging steps, {log_to_tensorboard} log to tensorboard, {learning_rate} learning rate, {weight_decay} weight decay, {seed} seed, {output_dir} output dir")
-            iseed = seed
-            model = FastLanguageModel.get_peft_model(
-                model,
-                r = lora_r,
-                target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                                "gate_proj", "up_proj", "down_proj",],
-                lora_alpha = lora_alpha,
-                lora_dropout = lora_dropout,
-                bias = "none",
-                use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
-                random_state=iseed,
-                use_rslora = False,  # We support rank stabilized LoRA
-                loftq_config = None, # And LoftQ
-            )
-            progress(0.0, desc="Loading Trainer")
-            time.sleep(1)
-            trainer = SFTTrainer(
-                model = model,
-                tokenizer = tokenizer,
-                train_dataset = dataset,
-                dataset_text_field="text",
-                max_seq_length=max_seq_length,
-                data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer),
-                dataset_num_proc = 2,
-                packing = False, # Can make training 5x faster for short sequences.
-                callbacks = [PrinterCallback(progress)],
-                args = TrainingArguments(
-                    per_device_train_batch_size = per_device_train_batch_size,
-                    gradient_accumulation_steps = gradient_accumulation_steps,
-                    warmup_steps = warmup_steps,
-                    max_steps = 60, # Set num_train_epochs = 1 for full training runs
-                    learning_rate = learning_rate,
-                    fp16 = not is_bfloat16_supported(),
-                    bf16 = is_bfloat16_supported(),
-                    logging_steps = logging_steps,
-                    optim = "adamw_8bit",
-                    weight_decay = weight_decay,
-                    lr_scheduler_type = "linear",
-                    seed = iseed,
-                    report_to="tensorboard" if log_to_tensorboard else None,
-                    output_dir = output_dir
-                ),
-            )
-            trainer = train_on_responses_only(
-                trainer,
-                instruction_part="<|start_header_id|>user<|end_header_id|>\n\n",
-                response_part="<|start_header_id|>assistant<|end_header_id|>\n\n",
-            )
-            trainer.train()
-            progress(1, desc="Training completed")
-            time.sleep(1)
-            return "Model trained 100%",gr.update(visible=True, interactive=False), gr.update(visible=True, interactive=True), gr.update(interactive=True)
-
-
-        train_btn.click(train_model, inputs=[model_name, lora_r, lora_alpha, lora_dropout, per_device_train_batch_size, warmup_steps, max_steps, gradient_accumulation_steps, logging_steps, log_to_tensorboard, learning_rate, weight_decay, seed, output_dir], outputs=[train_output, train_btn])
-
-    with gr.Tab("Save & Push Options"):
-
-        with gr.Row():
-            gr.Markdown("### Merging Options")
-            with gr.Column():
-                merge_16bit = gr.Checkbox(label="Merge to 16bit", value=False, interactive=True)
-                merge_4bit = gr.Checkbox(label="Merge to 4bit", value=False, interactive=True)
-                just_lora = gr.Checkbox(label="Just LoRA Adapter", value=False, interactive=True)
-        gr.Markdown("---")
-
-        with gr.Row():
-            gr.Markdown("### GGUF Options")
-            with gr.Column():
-                gguf_16bit = gr.Checkbox(label="Quantize to f16", value=False, interactive=True)
-                gguf_8bit = gr.Checkbox(label="Quantize to 8bit (Q8_0)", value=False, interactive=True)
-                gguf_4bit = gr.Checkbox(label="Quantize to 4bit (q4_k_m)", value=False, interactive=True)
-            with gr.Column():
-                gguf_custom = gr.Checkbox(label="Custom", value=False, interactive=True)
-                gguf_custom_value = gr.Textbox(label="", value="Q5_K", interactive=True)
-        gr.Markdown("---")
-
-        with gr.Row():
-            gr.Markdown("### Hugging Face Hub Options")
-            push_to_hub = gr.Checkbox(label="Push to Hub", value=False, interactive=True)
-            with gr.Column():
-                hub_model_name = gr.Textbox(label="Hub Model Name", value=f"username/model_name", interactive=True)
-                hub_token = gr.Textbox(label="Hub Token", interactive=True, type="password")
-        gr.Markdown("---")
-            
-        # with gr.Row():
-        #     gr.Markdown("### Ollama options")
-        #     with gr.Column():
-        #         ollama_create_local = gr.Checkbox(label="Create in Ollama (local)", value=False, interactive=True)
-        #         ollama_push_to_hub = gr.Checkbox(label="Push to Ollama", value=False, interactive=True)
-        #     with gr.Column():
-        #         ollama_model_name = gr.Textbox(label="Ollama Model Name", value="user/model_name")
-        #         ollama_pub_key = gr.Button("Ollama Pub Key")    
-        save_output = gr.Markdown("---")
-        save_button = gr.Button("Save Model", visible=True, interactive=True)
-        save_button.click(save_model, inputs=[model_name, hub_model_name, hub_token, gguf_16bit, gguf_8bit, gguf_4bit, gguf_custom, gguf_custom_value, merge_16bit, merge_4bit, just_lora, push_to_hub], outputs=[save_output, save_button])
-
-    with gr.Tab("Inference"):
-        with gr.Row():
-            input_text = gr.Textbox(label="Input Text", lines=4, value="""\
-Continue the fibonnaci sequence.
-# instruction
-1, 1, 2, 3, 5, 8
-# input
-""", interactive=True)
-            output_text = gr.Textbox(label="Output Text", lines=4, value="", interactive=False)
-
-        inference_button = gr.Button("Inference", visible=True, interactive=True)
-        inference_button.click(inference, inputs=[data_template, input_text], outputs=[output_text, inference_button])
-    load_btn.click(load_model, inputs=[initial_model_name, load_in_4bit, max_sequence_length, hub_token], outputs=[output, load_btn, train_btn, initial_model_name, load_in_4bit, max_sequence_length])
-
-demo.launch()
+import gradio as gr
+from huggingface_hub import HfApi
+from unsloth import FastLanguageModel, is_bfloat16_supported
+from unsloth.chat_templates import get_chat_template, train_on_responses_only
+
+from trl import SFTTrainer
+from transformers import TrainingArguments, TrainerCallback, DataCollatorForSeq2Seq
+import torch
+from datasets import load_dataset
+import time
+import psutil
+import platform
+import os
+
+hf_user = None
+try:
+    hfApi = HfApi()
+    hf_user = hfApi.whoami()["name"]
+except Exception as e:
+    hf_user = "not logged in"
+
+def get_human_readable_size(size, decimal_places=2):
+    for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
+        if size < 1024.0:
+            break
+        size /= 1024.0
+    return f"{size:.{decimal_places}f} {unit}"
+
+
+# get cpu stats
+disk_stats = psutil.disk_usage('.')
+print(get_human_readable_size(disk_stats.total))
+cpu_info = platform.processor()
+print(cpu_info)
+os_info = platform.platform()
+print(os_info)
+
+memory = psutil.virtual_memory()
+
+# Dropdown options
+model_options = [
+    "unsloth/Meta-Llama-3.1-8B-bnb-4bit",
+    "unsloth/Llama-3.2-1B-bnb-4bit",
+    "unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
+    "unsloth/Llama-3.2-3B-bnb-4bit",
+    "unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
+    "unsloth/mistral-7b-v0.3-bnb-4bit",      # New Mistral v3 2x faster!
+    "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
+    "unsloth/llama-3-8b-bnb-4bit",           # Llama-3 15 trillion tokens model 2x faster!
+    "unsloth/llama-3-8b-Instruct-bnb-4bit",
+    "unsloth/llama-3-70b-bnb-4bit",
+    "unsloth/Phi-3-mini-4k-instruct",        # Phi-3 2x faster!
+    "unsloth/Phi-3-medium-4k-instruct",
+    "unsloth/mistral-7b-bnb-4bit",
+    "unsloth/gemma-2-9b-bnb-4bit",
+    "unsloth/gemma-2-9b-bnb-4bit-instruct",
+    "unsloth/gemma-2-27b-bnb-4bit",          # Gemma 2x faster!
+    "unsloth/gemma-2-27b-bnb-4bit-instruct",          # Gemma 2x faster!
+    "unsloth/Qwen2-1.5B-bnb-4bit",         
+    "unsloth/Qwen2-1.5B-bnb-4bit-instruct",         
+    "unsloth/Qwen2-7B-bnb-4bit",          
+    "unsloth/Qwen2-7B-bnb-4bit-instruct",          
+    "unsloth/Qwen2-72B-bnb-4bit",         
+    "unsloth/Qwen2-72B-bnb-4bit-instruct",         
+    "unsloth/yi-6b-bnb-4bit",          
+    "unsloth/yi-34b-bnb-4bit",        
+]
+gpu_stats = torch.cuda.get_device_properties(0)
+start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
+
+running_on_hf = False
+if os.getenv("SYSTEM", None) == "spaces":
+    running_on_hf = True
+
+system_info = f"""\
+- **System:** {os_info}
+- **CPU:** {cpu_info} **Memory:** {get_human_readable_size(memory.free)} free of {get_human_readable_size(memory.total)}
+- **GPU:** {gpu_stats.name} ({max_memory} GB)
+- **Disk:** {get_human_readable_size(disk_stats.free)} free of {get_human_readable_size(disk_stats.total)}
+- **Hugging Face:** {running_on_hf}
+"""
+
+model=None
+tokenizer = None
+dataset = None
+max_seq_length = 2048
+
+class PrinterCallback(TrainerCallback):
+    step = 0
+    def __init__(self, progress):
+        self.progress = progress
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        _ = logs.pop("total_flos", None)
+        if state.is_local_process_zero:
+            #print(logs)
+            pass
+    def on_step_end(self, args, state, control, **kwargs):
+        if state.is_local_process_zero:
+            self.step = state.global_step
+            self.progress(self.step/60, desc=f"Training {self.step}/60")
+            #print("**Step ", state.global_step)
+
+    
+
+
+def formatting_prompts_func(examples, prompt):
+    global tokenizer
+    instructions = examples["instruction"]
+    inputs       = examples["input"]
+    outputs      = examples["output"]
+    
+    texts = []
+    for instruction, input, output in zip(instructions, inputs, outputs):
+        conversation = [
+            {
+                "role": "system",
+                "content": instruction + tokenizer.eos_token
+            },
+            {
+                "role": "user",
+                "content": input + tokenizer.eos_token
+            },
+            {
+                "role": "assistant",
+                "content": output + tokenizer.eos_token
+            }
+        ]
+        text = tokenizer.apply_chat_template(
+            conversation, tokenize=False, add_generation_prompt=False
+        )
+        
+        texts.append(text)
+    
+    return { "text" : texts }
+
+def load_model(initial_model_name, load_in_4bit, max_sequence_length, hub_token):
+    global model, tokenizer, max_seq_length
+    dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+    max_seq_length = max_sequence_length
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = initial_model_name,
+        max_seq_length = max_sequence_length,
+        dtype = dtype,
+        load_in_4bit = load_in_4bit,
+        token = f"{hub_token}", # use one if using gated models like meta-llama/Llama-2-7b-hf
+    )
+    tokenizer = get_chat_template(
+        tokenizer,
+        chat_template="llama-3.1",
+    )
+    return f"Model {initial_model_name} loaded, using {max_sequence_length} as max sequence length.", gr.update(visible=True, interactive=True), gr.update(interactive=True),gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
+
+def load_data(dataset_name, data_template_style, data_template):
+    global dataset
+    dataset = load_dataset(dataset_name, split = "train")
+    dataset = dataset.map(lambda examples: formatting_prompts_func(examples, data_template), batched=True)
+    
+    return f"Data loaded {len(dataset)} records loaded.", gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True)
+
+def inference(prompt, input_text):
+    FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+    inputs = tokenizer(
+    [
+        prompt.format(
+            "Continue the fibonnaci sequence.", # instruction
+            "1, 1, 2, 3, 5, 8", # input
+            "", # output - leave this blank for generation!
+        )
+    ], return_tensors = "pt").to("cuda")
+
+    outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+    result = tokenizer.batch_decode(outputs)
+    return result[0], gr.update(visible=True, interactive=True)
+
+def save_model(model_name, hub_model_name, hub_token, gguf_16bit, gguf_8bit, gguf_4bit, gguf_custom, gguf_custom_value, merge_16bit, merge_4bit, just_lora, push_to_hub, progress=gr.Progress()):
+    global model, tokenizer
+
+    print("Starting save_model function")
+    print(f"Model name: {model_name}")
+    print(f"Hub model name: {hub_model_name}")
+    print(f"GGUF 16bit: {gguf_16bit}, GGUF 8bit: {gguf_8bit}, GGUF 4bit: {gguf_4bit}")
+    print(f"Merge 16bit: {merge_16bit}, Merge 4bit: {merge_4bit}, Just LoRA: {just_lora}")
+    print(f"Push to hub: {push_to_hub}")
+    
+    quants = []
+    current_quant = 0
+
+    if gguf_custom:
+        gguf_custom_value = gguf_custom_value
+        quants.append(gguf_custom_value)
+        print(f"Custom GGUF value: {gguf_custom_value}")
+    else:
+        gguf_custom_value = None
+    
+    if gguf_16bit:
+        quants.append("f16")
+    if gguf_8bit:
+        quants.append("q8_0")
+    if gguf_4bit:
+        quants.append("q4_k_m")
+    
+    if merge_16bit:
+        print("Merging model to 16bit")
+        progress(current_quant/len(quants), desc=f"Pushing model merged 16bit {model_name} to HuggingFace Hub")
+        model.save_pretrained_merged(
+            "model",
+            tokenizer,
+            save_method="merged_16bit",
+        )
+        if push_to_hub:
+            print("Pushing merged 16bit model to HuggingFace Hub")
+            model.push_to_hub_merged(hub_model_name, tokenizer, save_method="merged_16bit", token=hub_token)
+
+    elif merge_4bit:
+        print("Merging model to 4bit")
+        progress(current_quant/len(quants), desc=f"Pushing model merged 4bit {model_name} to HuggingFace Hub")
+        model.save_pretrained_merged(
+            "model",
+            tokenizer,
+            save_method="merged_4bit_forced",
+        )
+        if push_to_hub:
+            print("Pushing merged 4bit model to HuggingFace Hub")
+            model.push_to_hub_merged(hub_model_name, tokenizer, save_method="merged_4bit_forced",  token=hub_token)
+
+    elif just_lora:
+        print("Saving just LoRA")
+        progress(current_quant/len(quants), desc=f"Pushing model merged lora {model_name} to HuggingFace Hub")
+        model.save_pretrained_merged(
+            "model",
+            tokenizer,
+            save_method="lora",
+        )
+        if push_to_hub:
+            print("Pushing LoRA model to HuggingFace Hub")
+            model.push_to_hub_merged(hub_model_name, tokenizer, save_method="lora",  token=hub_token)
+
+    if push_to_hub:
+        current_quant = 0
+        for q in quants:
+            print(f"Pushing model with quantization {q} to HuggingFace Hub")
+            progress(current_quant/len(quants), desc=f"Pushing model {model_name} with {q} to HuggingFace Hub")
+            model.push_to_hub_gguf(hub_model_name, tokenizer, quantization_method=q, token=hub_token)
+            current_quant += 1
+    print("Model saved successfully")
+    return "Model saved", gr.update(visible=True, interactive=True)
+
+def username(profile: gr.OAuthProfile | None):
+    hf_user = profile["name"] if profile else "not logged in"
+    return hf_user
+
+# Create the Gradio interface
+with gr.Blocks(title="Unsloth fine-tuning") as demo:
+    if (running_on_hf):
+        gr.LoginButton()
+    # logged_user = gr.Markdown(f"**User:** {hf_user}")
+    #demo.load(username, inputs=None, outputs=logged_user)
+    with gr.Row():
+        with gr.Column(scale=0.5):
+            gr.Image("unsloth.png", width="300px", interactive=False, show_download_button=False, show_label=False, show_share_button=False)
+        with gr.Column(min_width="550px", scale=1):
+            gr.Markdown(system_info) 
+        with gr.Column(min_width="250px", scale=0.3):
+            gr.Markdown(f"**Links:**\n\n* [Unsloth Hub](https://huggingface.co/unsloth)\n\n* [Unsloth Docs](http://docs.unsloth.com/)\n\n* [Unsloth GitHub](https://github.com/unslothai/unsloth)")
+    with gr.Tab("Base Model Parameters"):
+
+        with gr.Row():
+            initial_model_name = gr.Dropdown(choices=model_options, label="Select Base Model", allow_custom_value=True)
+            load_in_4bit = gr.Checkbox(label="Load 4bit model", value=True)
+
+        gr.Markdown("### Target Model Parameters")
+        with gr.Row():
+            max_sequence_length = gr.Slider(minimum=128, value=512, step=64, maximum=128*1024, interactive=True, label="Max Sequence Length")
+        load_btn = gr.Button("Load")
+        output = gr.Textbox(label="Model Load Status", value="Model not loaded", interactive=False)
+        gr.Markdown("---")
+
+    with gr.Tab("Data Preparation"):
+        with gr.Row():
+            dataset_name = gr.Textbox(label="Dataset Name", value="yahma/alpaca-cleaned")
+            data_template_style = gr.Dropdown(label="Template", choices=["alpaca","custom"], value="alpaca",  allow_custom_value=True)
+        with gr.Row():
+            data_template =  gr.TextArea(label="Data Template", value="""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}""")
+        gr.Markdown("---")
+        output_load_data = gr.Textbox(label="Data Load Status", value="Data not loaded", interactive=False)
+        load_data_btn = gr.Button("Load Dataset", interactive=True)
+        load_data_btn.click(load_data, inputs=[dataset_name, data_template_style, data_template], outputs=[output_load_data, load_data_btn])
+
+    with gr.Tab("Fine-Tuning"):
+        gr.Markdown("""### Fine-Tuned Model Parameters""")
+        with gr.Row():
+            model_name = gr.Textbox(label="Model Name", value=initial_model_name.value, interactive=True)
+
+        gr.Markdown("""### Lora Parameters""")
+
+        with gr.Row():
+            lora_r = gr.Number(label="R", value=16, interactive=True)
+            lora_alpha = gr.Number(label="Lora Alpha", value=16, interactive=True)
+            lora_dropout = gr.Number(label="Lora Dropout", value=0.1, interactive=True)
+
+        gr.Markdown("---")
+        gr.Markdown("""### Training Parameters""")
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    per_device_train_batch_size = gr.Number(label="Per Device Train Batch Size", value=2, interactive=True)
+                    warmup_steps = gr.Number(label="Warmup Steps", value=5, interactive=True)
+                    max_steps = gr.Number(label="Max Steps", value=60, interactive=True)
+                    gradient_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=4, interactive=True)
+                with gr.Row():
+                    logging_steps = gr.Number(label="Logging Steps", value=1, interactive=True)
+                    log_to_tensorboard = gr.Checkbox(label="Log to Tensorboard", value=True, interactive=True)
+
+                with gr.Row():
+                #     optim = gr.Dropdown(choices=["adamw_8bit", "adamw", "sgd"], label="Optimizer", value="adamw_8bit")
+                    learning_rate = gr.Number(label="Learning Rate", value=2e-4, interactive=True)
+
+                # with gr.Row():
+                    weight_decay = gr.Number(label="Weight Decay", value=0.01, interactive=True)
+                    # lr_scheduler_type = gr.Dropdown(choices=["linear", "cosine", "constant"], label="LR Scheduler Type", value="linear")
+        gr.Markdown("---")
+
+        with gr.Row():
+            seed = gr.Number(label="Seed", value=3407, interactive=True)
+            output_dir = gr.Textbox(label="Output Directory", value="outputs", interactive=True)
+        gr.Markdown("---")
+
+        train_output = gr.Textbox(label="Training Status", value="Model not trained", interactive=False)
+        train_btn = gr.Button("Train", visible=True)
+
+        def train_model(model_name: str, lora_r: int, lora_alpha: int, lora_dropout: float, per_device_train_batch_size: int, warmup_steps: int, max_steps: int,
+                gradient_accumulation_steps: int, logging_steps: int, log_to_tensorboard: bool, learning_rate, weight_decay, seed: int, output_dir, progress= gr.Progress()):
+            global model, tokenizer
+            print(f"$$$ Training model {model_name} with {lora_r} R, {lora_alpha} alpha, {lora_dropout} dropout, {per_device_train_batch_size} per device train batch size, {warmup_steps} warmup steps, {max_steps} max steps, {gradient_accumulation_steps} gradient accumulation steps, {logging_steps} logging steps, {log_to_tensorboard} log to tensorboard, {learning_rate} learning rate, {weight_decay} weight decay, {seed} seed, {output_dir} output dir")
+            iseed = seed
+            model = FastLanguageModel.get_peft_model(
+                model,
+                r = lora_r,
+                target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                                "gate_proj", "up_proj", "down_proj",],
+                lora_alpha = lora_alpha,
+                lora_dropout = lora_dropout,
+                bias = "none",
+                use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+                random_state=iseed,
+                use_rslora = False,  # We support rank stabilized LoRA
+                loftq_config = None, # And LoftQ
+            )
+            progress(0.0, desc="Loading Trainer")
+            time.sleep(1)
+            trainer = SFTTrainer(
+                model = model,
+                tokenizer = tokenizer,
+                train_dataset = dataset,
+                dataset_text_field="text",
+                max_seq_length=max_seq_length,
+                data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer),
+                dataset_num_proc = 2,
+                packing = False, # Can make training 5x faster for short sequences.
+                callbacks = [PrinterCallback(progress)],
+                args = TrainingArguments(
+                    per_device_train_batch_size = per_device_train_batch_size,
+                    gradient_accumulation_steps = gradient_accumulation_steps,
+                    warmup_steps = warmup_steps,
+                    max_steps = 60, # Set num_train_epochs = 1 for full training runs
+                    learning_rate = learning_rate,
+                    fp16 = not is_bfloat16_supported(),
+                    bf16 = is_bfloat16_supported(),
+                    logging_steps = logging_steps,
+                    optim = "adamw_8bit",
+                    weight_decay = weight_decay,
+                    lr_scheduler_type = "linear",
+                    seed = iseed,
+                    report_to="tensorboard" if log_to_tensorboard else None,
+                    output_dir = output_dir
+                ),
+            )
+            trainer = train_on_responses_only(
+                trainer,
+                instruction_part="<|start_header_id|>user<|end_header_id|>\n\n",
+                response_part="<|start_header_id|>assistant<|end_header_id|>\n\n",
+            )
+            trainer.train()
+            progress(1, desc="Training completed")
+            time.sleep(1)
+            return "Model trained 100%",gr.update(visible=True, interactive=False), gr.update(visible=True, interactive=True), gr.update(interactive=True)
+
+
+        train_btn.click(train_model, inputs=[model_name, lora_r, lora_alpha, lora_dropout, per_device_train_batch_size, warmup_steps, max_steps, gradient_accumulation_steps, logging_steps, log_to_tensorboard, learning_rate, weight_decay, seed, output_dir], outputs=[train_output, train_btn])
+
+    with gr.Tab("Save & Push Options"):
+
+        with gr.Row():
+            gr.Markdown("### Merging Options")
+            with gr.Column():
+                merge_16bit = gr.Checkbox(label="Merge to 16bit", value=False, interactive=True)
+                merge_4bit = gr.Checkbox(label="Merge to 4bit", value=False, interactive=True)
+                just_lora = gr.Checkbox(label="Just LoRA Adapter", value=False, interactive=True)
+        gr.Markdown("---")
+
+        with gr.Row():
+            gr.Markdown("### GGUF Options")
+            with gr.Column():
+                gguf_16bit = gr.Checkbox(label="Quantize to f16", value=False, interactive=True)
+                gguf_8bit = gr.Checkbox(label="Quantize to 8bit (Q8_0)", value=False, interactive=True)
+                gguf_4bit = gr.Checkbox(label="Quantize to 4bit (q4_k_m)", value=False, interactive=True)
+            with gr.Column():
+                gguf_custom = gr.Checkbox(label="Custom", value=False, interactive=True)
+                gguf_custom_value = gr.Textbox(label="", value="Q5_K", interactive=True)
+        gr.Markdown("---")
+
+        with gr.Row():
+            gr.Markdown("### Hugging Face Hub Options")
+            push_to_hub = gr.Checkbox(label="Push to Hub", value=False, interactive=True)
+            with gr.Column():
+                hub_model_name = gr.Textbox(label="Hub Model Name", value=f"username/model_name", interactive=True)
+                hub_token = gr.Textbox(label="Hub Token", interactive=True, type="password")
+        gr.Markdown("---")
+            
+        # with gr.Row():
+        #     gr.Markdown("### Ollama options")
+        #     with gr.Column():
+        #         ollama_create_local = gr.Checkbox(label="Create in Ollama (local)", value=False, interactive=True)
+        #         ollama_push_to_hub = gr.Checkbox(label="Push to Ollama", value=False, interactive=True)
+        #     with gr.Column():
+        #         ollama_model_name = gr.Textbox(label="Ollama Model Name", value="user/model_name")
+        #         ollama_pub_key = gr.Button("Ollama Pub Key")    
+        save_output = gr.Markdown("---")
+        save_button = gr.Button("Save Model", visible=True, interactive=True)
+        save_button.click(save_model, inputs=[model_name, hub_model_name, hub_token, gguf_16bit, gguf_8bit, gguf_4bit, gguf_custom, gguf_custom_value, merge_16bit, merge_4bit, just_lora, push_to_hub], outputs=[save_output, save_button])
+
+    with gr.Tab("Inference"):
+        with gr.Row():
+            input_text = gr.Textbox(label="Input Text", lines=4, value="""\
+Continue the fibonnaci sequence.
+# instruction
+1, 1, 2, 3, 5, 8
+# input
+""", interactive=True)
+            output_text = gr.Textbox(label="Output Text", lines=4, value="", interactive=False)
+
+        inference_button = gr.Button("Inference", visible=True, interactive=True)
+        inference_button.click(inference, inputs=[data_template, input_text], outputs=[output_text, inference_button])
+    load_btn.click(load_model, inputs=[initial_model_name, load_in_4bit, max_sequence_length, hub_token], outputs=[output, load_btn, train_btn, initial_model_name, load_in_4bit, max_sequence_length])
+
+demo.launch()