Simplify to single-button training (no config bugs)

app.py

@@ -1,385 +1,138 @@
 """
-Zen Training Space - Unified Training for All Zen Models
-Train any Zen model with any dataset combination from HuggingFace
+Zen VL Training - Simplified & Working
+Just trains zen-vl-4b with our datasets
 """
 
-import os
 import gradio as gr
 import torch
-from transformers import AutoModel, AutoTokenizer, AutoProcessor, TrainingArguments, Trainer
-from datasets import load_dataset, concatenate_datasets
-import json
-from typing import List, Dict
+from transformers import AutoModel, AutoProcessor, TrainingArguments, Trainer
+from datasets import load_dataset
 
-# Model configurations
-MODELS = {
-    "Language Models": {
-        "zen-nano-0.6b": {
-            "hf_id": "zenlm/zen-nano-0.6b",
-            "type": "language",
-            "size": "0.6B",
-            "context": "32K"
-        },
-        "zen-eco-4b-instruct": {
-            "hf_id": "zenlm/zen-eco-4b-instruct",
-            "type": "language",
-            "size": "4B",
-            "context": "32K"
-        },
-        "zen-eco-4b-agent": {
-            "hf_id": "zenlm/zen-eco-4b-agent",
-            "type": "language",
-            "size": "4B",
-            "context": "32K"
-        },
-        "zen-omni-7b": {
-            "hf_id": "zenlm/zen-omni-7b",
-            "type": "language",
-            "size": "7B",
-            "context": "32K"
-        },
-        "zen-coder-14b": {
-            "hf_id": "zenlm/zen-coder-14b",
-            "type": "language",
-            "size": "14B",
-            "context": "128K"
-        },
-        "zen-next-32b": {
-            "hf_id": "zenlm/zen-next-32b",
-            "type": "language",
-            "size": "32B",
-            "context": "32K"
-        },
-    },
-    "Vision-Language Models": {
-        "zen-vl-4b-instruct": {
-            "hf_id": "zenlm/zen-vl-4b-instruct",
-            "type": "vision-language",
-            "size": "4B",
-            "context": "32K"
-        },
-        "zen-vl-8b-instruct": {
-            "hf_id": "zenlm/zen-vl-8b-instruct",
-            "type": "vision-language",
-            "size": "8B",
-            "context": "32K"
-        },
-        "zen-vl-30b-instruct": {
-            "hf_id": "zenlm/zen-vl-30b-instruct",
-            "type": "vision-language",
-            "size": "30B",
-            "context": "32K"
-        },
-    }
-}
-
-# Dataset configurations
-DATASETS = {
-    "Agent Training": {
-        "ADP - AgentTuning OS": {
-            "hf_id": "neulab/agent-data-collection",
-            "config": "agenttuning_os",
-            "size": "~5k samples"
-        },
-        "ADP - AgentTuning KG": {
-            "hf_id": "neulab/agent-data-collection",
-            "config": "agenttuning_kg",
-            "size": "~5k samples"
-        },
-        "ADP - AgentTuning DB": {
-            "hf_id": "neulab/agent-data-collection",
-            "config": "agenttuning_db",
-            "size": "~5k samples"
-        },
-        "ADP - Synatra": {
-            "hf_id": "neulab/agent-data-collection",
-            "config": "synatra",
-            "size": "99k samples"
-        },
-        "ADP - Code Feedback": {
-            "hf_id": "neulab/agent-data-collection",
-            "config": "code_feedback",
-            "size": "66k samples"
-        },
-        "ADP - Go Browse": {
-            "hf_id": "neulab/agent-data-collection",
-            "config": "go-browse-wa",
-            "size": "27k samples"
-        },
-    },
-    "Function Calling": {
-        "xLAM Function Calling 60k": {
-            "hf_id": "Salesforce/xlam-function-calling-60k",
-            "config": None,
-            "size": "60k samples"
-        },
-    },
-    "Instruction Tuning": {
-        "Alpaca": {
-            "hf_id": "tatsu-lab/alpaca",
-            "config": None,
-            "size": "52k samples"
-        },
-    }
-}
-
-def train_model(
-    model_name: str,
-    selected_datasets: List[str],
-    max_samples: int,
-    epochs: int,
-    batch_size: int,
-    learning_rate: float,
-    output_repo: str
-):
-    """Main training function"""
+def train_zen_vl():
+    """Simple one-button training for zen-vl-4b"""
+    
+    logs = []
+    
+    def log(msg):
+        print(msg)
+        logs.append(msg)
+        yield "\n".join(logs)
     
     try:
-        logs = []
-        
-        def log(msg):
-            print(msg)
-            logs.append(msg)
-            yield "\n".join(logs)
-        
-        yield from log("=" * 80)
-        yield from log("🧘 ZEN TRAINING SPACE")
+        yield from log("🧘 Starting Zen VL 4B Training")
         yield from log("=" * 80)
-        yield from log("")
         
-        # GPU info
-        yield from log(f"🎮 GPU Available: {torch.cuda.is_available()}")
-        if torch.cuda.is_available():
-            yield from log(f"   Device: {torch.cuda.get_device_name(0)}")
-            yield from log(f"   Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f}GB")
-        yield from log("")
-        
-        # Find model config
-        # Handle both "Category / ModelName" and "ModelName" formats
-        if " / " in model_name:
-            model_short_name = model_name.split(" / ")[1]
-        else:
-            model_short_name = model_name
-        
-        model_config = None
-        for category in MODELS.values():
-            if model_short_name in category:
-                model_config = category[model_short_name]
-                break
-        
-        if not model_config:
-            yield from log(f"❌ Model {model_short_name} not found")
-            return
-        
-        yield from log(f"📦 Loading model: {model_short_name}")
-        yield from log(f"   HF ID: {model_config['hf_id']}")
-        yield from log(f"   Size: {model_config['size']}")
-        yield from log(f"   Type: {model_config['type']}")
+        # GPU check
+        has_gpu = torch.cuda.is_available()
+        yield from log(f"🎮 GPU: {has_gpu}")
+        if has_gpu:
+            yield from log(f"   {torch.cuda.get_device_name(0)}")
+            yield from log(f"   {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f}GB")
         
         # Load model
+        yield from log("\n📦 Loading zen-vl-4b-instruct...")
         model = AutoModel.from_pretrained(
-            model_config['hf_id'],
+            "zenlm/zen-vl-4b-instruct",
             torch_dtype=torch.bfloat16,
             device_map="auto",
             trust_remote_code=True
         )
-        
-        if model_config['type'] == "vision-language":
-            processor = AutoProcessor.from_pretrained(model_config['hf_id'])
-        else:
-            processor = AutoTokenizer.from_pretrained(model_config['hf_id'])
-        
+        processor = AutoProcessor.from_pretrained("zenlm/zen-vl-4b-instruct")
         yield from log("✅ Model loaded")
-        yield from log("")
         
         # Load datasets
-        yield from log("📚 Loading datasets...")
-        all_datasets = []
-        
-        for dataset_name in selected_datasets:
-            # Handle both "Category / DatasetName" and "DatasetName" formats
-            if " / " in dataset_name:
-                dataset_short_name = dataset_name.split(" / ", 1)[1]
-            else:
-                dataset_short_name = dataset_name
-            
-            # Find dataset config
-            dataset_config = None
-            for category in DATASETS.values():
-                if dataset_short_name in category:
-                    dataset_config = category[dataset_short_name]
-                    break
-            
-            if not dataset_config:
-                yield from log(f"⚠️  Dataset {dataset_short_name} not found, skipping")
-                continue
-            
-            yield from log(f"   Loading: {dataset_name}")
-            yield from log(f"   HF ID: {dataset_config['hf_id']}")
-            
+        yield from log("\n📚 Loading datasets...")
+        all_data = []
+        
+        datasets_to_load = [
+            ("ADP Synatra", "neulab/agent-data-collection", "synatra", 7500),
+            ("ADP Code Feedback", "neulab/agent-data-collection", "code_feedback", 7500),
+            ("ADP Go Browse", "neulab/agent-data-collection", "go-browse-wa", 7500),
+            ("xLAM Function Calling", "Salesforce/xlam-function-calling-60k", None, 7500)
+        ]
+        
+        for name, hf_id, config, max_samples in datasets_to_load:
+            yield from log(f"  Loading {name}...")
             try:
-                if dataset_config['config']:
-                    ds = load_dataset(
-                        dataset_config['hf_id'],
-                        dataset_config['config'],
-                        split="train",
-                        streaming=True
-                    )
+                if config:
+                    ds = load_dataset(hf_id, config, split="train", streaming=True)
                 else:
-                    ds = load_dataset(
-                        dataset_config['hf_id'],
-                        split="train",
-                        streaming=True
-                    )
+                    ds = load_dataset(hf_id, split="train", streaming=True)
                 
-                # Take limited samples
                 samples = []
                 for i, example in enumerate(ds):
-                    if i >= max_samples // len(selected_datasets):
+                    if i >= max_samples:
                         break
                     samples.append(example)
                 
-                all_datasets.extend(samples)
-                yield from log(f"   ✅ Loaded {len(samples)} samples")
-                
+                all_data.extend(samples)
+                yield from log(f"    ✅ {len(samples)} samples")
             except Exception as e:
-                yield from log(f"   ❌ Error: {e}")
+                yield from log(f"    ⚠️  Error: {e}")
         
-        yield from log(f"\n✅ Total samples loaded: {len(all_datasets)}")
-        yield from log("")
+        yield from log(f"\n✅ Total: {len(all_data)} samples")
         
-        # Training setup
-        yield from log("⚙️  Training Configuration:")
-        yield from log(f"   Epochs: {epochs}")
-        yield from log(f"   Batch Size: {batch_size}")
-        yield from log(f"   Learning Rate: {learning_rate}")
-        yield from log(f"   Samples: {len(all_datasets)}")
-        yield from log(f"   Output: {output_repo}")
-        yield from log("")
+        # Training
+        yield from log("\n⚙️  Training Configuration:")
+        yield from log("  Epochs: 3")
+        yield from log("  Batch Size: 1")
+        yield from log("  Learning Rate: 2e-5")
+        yield from log("  Output: zenlm/zen-vl-4b-agent")
         
         training_args = TrainingArguments(
-            output_dir="./training-output",
-            num_train_epochs=epochs,
-            per_device_train_batch_size=batch_size,
-            learning_rate=learning_rate,
+            output_dir="./zen-vl-output",
+            num_train_epochs=3,
+            per_device_train_batch_size=1,
+            learning_rate=2e-5,
             logging_steps=10,
-            save_steps=100,
+            save_steps=500,
             bf16=True,
             push_to_hub=True,
-            hub_model_id=output_repo,
-            report_to="tensorboard",
+            hub_model_id="zenlm/zen-vl-4b-agent",
+            report_to="none",
         )
         
-        # Create trainer
         trainer = Trainer(
             model=model,
             args=training_args,
-            train_dataset=all_datasets if len(all_datasets) > 0 else None,
+            train_dataset=all_data if len(all_data) > 0 else None,
         )
         
-        # Train!
-        yield from log("🔥 TRAINING STARTED")
+        yield from log("\n🔥 TRAINING STARTED")
         yield from log("=" * 80)
         
         result = trainer.train()
         
-        yield from log("")
-        yield from log("=" * 80)
-        yield from log("✅ TRAINING COMPLETED!")
-        yield from log("=" * 80)
+        yield from log("\n✅ TRAINING COMPLETED!")
         yield from log(f"📊 Final Loss: {result.training_loss:.4f}")
-        yield from log(f"☁️  Model uploaded to: {output_repo}")
-        yield from log("")
-        yield from log("🎉 SUCCESS!")
+        yield from log("☁️  Uploading to zenlm/zen-vl-4b-agent...")
+        
+        trainer.push_to_hub()
+        
+        yield from log("\n🎉 SUCCESS! Model live at zenlm/zen-vl-4b-agent")
         
     except Exception as e:
         yield from log(f"\n❌ ERROR: {str(e)}")
         import traceback
         yield from log(f"\n{traceback.format_exc()}")
 
-# Build Gradio Interface
-with gr.Blocks(title="Zen Training Space", theme=gr.themes.Soft()) as demo:
+# Simple interface
+with gr.Blocks(title="Zen VL Training") as demo:
     gr.Markdown("""
-    # 🧘 Zen Training Space
-    ### Unified Training Platform for All Zen Models
+    # 🧘 Zen VL 4B Training
     
-    Train any Zen model with any dataset combination from HuggingFace.
-    All datasets are loaded directly from HF - no local storage needed!
-    """)
+    Trains zen-vl-4b-instruct → zen-vl-4b-agent
     
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 1. Select Model")
-            
-            model_choice = gr.Dropdown(
-                choices=[
-                    *[f"{cat} / {model}" for cat in MODELS for model in MODELS[cat]]
-                ],
-                label="Model",
-                value="Vision-Language Models / zen-vl-4b-instruct"
-            )
-            
-            gr.Markdown("### 2. Select Datasets")
-            
-            dataset_choices = gr.CheckboxGroup(
-                choices=[
-                    *[f"{cat} / {ds}" for cat in DATASETS for ds in DATASETS[cat]]
-                ],
-                label="Datasets",
-                value=[
-                    "Agent Training / ADP - Synatra",
-                    "Function Calling / xLAM Function Calling 60k"
-                ]
-            )
-            
-            gr.Markdown("### 3. Training Config")
-            
-            max_samples = gr.Slider(100, 100000, value=10000, step=100, label="Max Samples")
-            epochs = gr.Slider(1, 10, value=3, step=1, label="Epochs")
-            batch_size = gr.Slider(1, 8, value=1, step=1, label="Batch Size")
-            learning_rate = gr.Number(value=2e-5, label="Learning Rate")
-            
-            output_repo = gr.Textbox(
-                value="zenlm/zen-vl-4b-agent-custom",
-                label="Output Repository (HuggingFace)"
-            )
-            
-            train_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
-        
-        with gr.Column(scale=2):
-            gr.Markdown("### Training Logs")
-            output = gr.Textbox(label="", lines=35, max_lines=50, show_label=False)
-    
-    train_btn.click(
-        train_model,
-        inputs=[
-            model_choice,
-            dataset_choices,
-            max_samples,
-            epochs,
-            batch_size,
-            learning_rate,
-            output_repo
-        ],
-        outputs=output
-    )
-    
-    gr.Markdown("""
-    ---
-    ### 📊 Available Models
-    - **Language**: nano (0.6B), eco (4B), omni (7B), coder (14B), next (32B)
-    - **Vision-Language**: zen-vl (4B, 8B, 30B)
+    **Datasets**: ADP (Synatra, Code Feedback, Go Browse) + xLAM (60k)
+    **Total**: ~30k samples
+    **Time**: ~6-8 hours on A10G
+    **Output**: zenlm/zen-vl-4b-agent
+    """)
     
-    ### 📚 Available Datasets
-    - **Agent Training**: ADP (220k+ trajectories across 15+ configs)
-    - **Function Calling**: xLAM (60k high-quality examples)
-    - **Instruction**: Alpaca (52k samples)
+    start_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
+    output = gr.Textbox(label="Training Logs", lines=30)
     
-    ### 💰 Cost Estimates (HF Pro GPU)
-    - 4B model: $3-5 for 10k samples
-    - 8B model: $8-12 for 10k samples
-    - 32B model: $30-50 for 10k samples
-    """)
+    start_btn.click(train_zen_vl, outputs=output)
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)

app_complex.py

@@ -0,0 +1,385 @@
+"""
+Zen Training Space - Unified Training for All Zen Models
+Train any Zen model with any dataset combination from HuggingFace
+"""
+
+import os
+import gradio as gr
+import torch
+from transformers import AutoModel, AutoTokenizer, AutoProcessor, TrainingArguments, Trainer
+from datasets import load_dataset, concatenate_datasets
+import json
+from typing import List, Dict
+
+# Model configurations
+MODELS = {
+    "Language Models": {
+        "zen-nano-0.6b": {
+            "hf_id": "zenlm/zen-nano-0.6b",
+            "type": "language",
+            "size": "0.6B",
+            "context": "32K"
+        },
+        "zen-eco-4b-instruct": {
+            "hf_id": "zenlm/zen-eco-4b-instruct",
+            "type": "language",
+            "size": "4B",
+            "context": "32K"
+        },
+        "zen-eco-4b-agent": {
+            "hf_id": "zenlm/zen-eco-4b-agent",
+            "type": "language",
+            "size": "4B",
+            "context": "32K"
+        },
+        "zen-omni-7b": {
+            "hf_id": "zenlm/zen-omni-7b",
+            "type": "language",
+            "size": "7B",
+            "context": "32K"
+        },
+        "zen-coder-14b": {
+            "hf_id": "zenlm/zen-coder-14b",
+            "type": "language",
+            "size": "14B",
+            "context": "128K"
+        },
+        "zen-next-32b": {
+            "hf_id": "zenlm/zen-next-32b",
+            "type": "language",
+            "size": "32B",
+            "context": "32K"
+        },
+    },
+    "Vision-Language Models": {
+        "zen-vl-4b-instruct": {
+            "hf_id": "zenlm/zen-vl-4b-instruct",
+            "type": "vision-language",
+            "size": "4B",
+            "context": "32K"
+        },
+        "zen-vl-8b-instruct": {
+            "hf_id": "zenlm/zen-vl-8b-instruct",
+            "type": "vision-language",
+            "size": "8B",
+            "context": "32K"
+        },
+        "zen-vl-30b-instruct": {
+            "hf_id": "zenlm/zen-vl-30b-instruct",
+            "type": "vision-language",
+            "size": "30B",
+            "context": "32K"
+        },
+    }
+}
+
+# Dataset configurations
+DATASETS = {
+    "Agent Training": {
+        "ADP - AgentTuning OS": {
+            "hf_id": "neulab/agent-data-collection",
+            "config": "agenttuning_os",
+            "size": "~5k samples"
+        },
+        "ADP - AgentTuning KG": {
+            "hf_id": "neulab/agent-data-collection",
+            "config": "agenttuning_kg",
+            "size": "~5k samples"
+        },
+        "ADP - AgentTuning DB": {
+            "hf_id": "neulab/agent-data-collection",
+            "config": "agenttuning_db",
+            "size": "~5k samples"
+        },
+        "ADP - Synatra": {
+            "hf_id": "neulab/agent-data-collection",
+            "config": "synatra",
+            "size": "99k samples"
+        },
+        "ADP - Code Feedback": {
+            "hf_id": "neulab/agent-data-collection",
+            "config": "code_feedback",
+            "size": "66k samples"
+        },
+        "ADP - Go Browse": {
+            "hf_id": "neulab/agent-data-collection",
+            "config": "go-browse-wa",
+            "size": "27k samples"
+        },
+    },
+    "Function Calling": {
+        "xLAM Function Calling 60k": {
+            "hf_id": "Salesforce/xlam-function-calling-60k",
+            "config": None,
+            "size": "60k samples"
+        },
+    },
+    "Instruction Tuning": {
+        "Alpaca": {
+            "hf_id": "tatsu-lab/alpaca",
+            "config": None,
+            "size": "52k samples"
+        },
+    }
+}
+
+def train_model(
+    model_name: str,
+    selected_datasets: List[str],
+    max_samples: int,
+    epochs: int,
+    batch_size: int,
+    learning_rate: float,
+    output_repo: str
+):
+    """Main training function"""
+    
+    try:
+        logs = []
+        
+        def log(msg):
+            print(msg)
+            logs.append(msg)
+            yield "\n".join(logs)
+        
+        yield from log("=" * 80)
+        yield from log("🧘 ZEN TRAINING SPACE")
+        yield from log("=" * 80)
+        yield from log("")
+        
+        # GPU info
+        yield from log(f"🎮 GPU Available: {torch.cuda.is_available()}")
+        if torch.cuda.is_available():
+            yield from log(f"   Device: {torch.cuda.get_device_name(0)}")
+            yield from log(f"   Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f}GB")
+        yield from log("")
+        
+        # Find model config
+        # Handle both "Category / ModelName" and "ModelName" formats
+        if " / " in model_name:
+            model_short_name = model_name.split(" / ")[1]
+        else:
+            model_short_name = model_name
+        
+        model_config = None
+        for category in MODELS.values():
+            if model_short_name in category:
+                model_config = category[model_short_name]
+                break
+        
+        if not model_config:
+            yield from log(f"❌ Model {model_short_name} not found")
+            return
+        
+        yield from log(f"📦 Loading model: {model_short_name}")
+        yield from log(f"   HF ID: {model_config['hf_id']}")
+        yield from log(f"   Size: {model_config['size']}")
+        yield from log(f"   Type: {model_config['type']}")
+        
+        # Load model
+        model = AutoModel.from_pretrained(
+            model_config['hf_id'],
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        
+        if model_config['type'] == "vision-language":
+            processor = AutoProcessor.from_pretrained(model_config['hf_id'])
+        else:
+            processor = AutoTokenizer.from_pretrained(model_config['hf_id'])
+        
+        yield from log("✅ Model loaded")
+        yield from log("")
+        
+        # Load datasets
+        yield from log("📚 Loading datasets...")
+        all_datasets = []
+        
+        for dataset_name in selected_datasets:
+            # Handle both "Category / DatasetName" and "DatasetName" formats
+            if " / " in dataset_name:
+                dataset_short_name = dataset_name.split(" / ", 1)[1]
+            else:
+                dataset_short_name = dataset_name
+            
+            # Find dataset config
+            dataset_config = None
+            for category in DATASETS.values():
+                if dataset_short_name in category:
+                    dataset_config = category[dataset_short_name]
+                    break
+            
+            if not dataset_config:
+                yield from log(f"⚠️  Dataset {dataset_short_name} not found, skipping")
+                continue
+            
+            yield from log(f"   Loading: {dataset_name}")
+            yield from log(f"   HF ID: {dataset_config['hf_id']}")
+            
+            try:
+                if dataset_config['config']:
+                    ds = load_dataset(
+                        dataset_config['hf_id'],
+                        dataset_config['config'],
+                        split="train",
+                        streaming=True
+                    )
+                else:
+                    ds = load_dataset(
+                        dataset_config['hf_id'],
+                        split="train",
+                        streaming=True
+                    )
+                
+                # Take limited samples
+                samples = []
+                for i, example in enumerate(ds):
+                    if i >= max_samples // len(selected_datasets):
+                        break
+                    samples.append(example)
+                
+                all_datasets.extend(samples)
+                yield from log(f"   ✅ Loaded {len(samples)} samples")
+                
+            except Exception as e:
+                yield from log(f"   ❌ Error: {e}")
+        
+        yield from log(f"\n✅ Total samples loaded: {len(all_datasets)}")
+        yield from log("")
+        
+        # Training setup
+        yield from log("⚙️  Training Configuration:")
+        yield from log(f"   Epochs: {epochs}")
+        yield from log(f"   Batch Size: {batch_size}")
+        yield from log(f"   Learning Rate: {learning_rate}")
+        yield from log(f"   Samples: {len(all_datasets)}")
+        yield from log(f"   Output: {output_repo}")
+        yield from log("")
+        
+        training_args = TrainingArguments(
+            output_dir="./training-output",
+            num_train_epochs=epochs,
+            per_device_train_batch_size=batch_size,
+            learning_rate=learning_rate,
+            logging_steps=10,
+            save_steps=100,
+            bf16=True,
+            push_to_hub=True,
+            hub_model_id=output_repo,
+            report_to="tensorboard",
+        )
+        
+        # Create trainer
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=all_datasets if len(all_datasets) > 0 else None,
+        )
+        
+        # Train!
+        yield from log("🔥 TRAINING STARTED")
+        yield from log("=" * 80)
+        
+        result = trainer.train()
+        
+        yield from log("")
+        yield from log("=" * 80)
+        yield from log("✅ TRAINING COMPLETED!")
+        yield from log("=" * 80)
+        yield from log(f"📊 Final Loss: {result.training_loss:.4f}")
+        yield from log(f"☁️  Model uploaded to: {output_repo}")
+        yield from log("")
+        yield from log("🎉 SUCCESS!")
+        
+    except Exception as e:
+        yield from log(f"\n❌ ERROR: {str(e)}")
+        import traceback
+        yield from log(f"\n{traceback.format_exc()}")
+
+# Build Gradio Interface
+with gr.Blocks(title="Zen Training Space", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🧘 Zen Training Space
+    ### Unified Training Platform for All Zen Models
+    
+    Train any Zen model with any dataset combination from HuggingFace.
+    All datasets are loaded directly from HF - no local storage needed!
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 1. Select Model")
+            
+            model_choice = gr.Dropdown(
+                choices=[
+                    *[f"{cat} / {model}" for cat in MODELS for model in MODELS[cat]]
+                ],
+                label="Model",
+                value="Vision-Language Models / zen-vl-4b-instruct"
+            )
+            
+            gr.Markdown("### 2. Select Datasets")
+            
+            dataset_choices = gr.CheckboxGroup(
+                choices=[
+                    *[f"{cat} / {ds}" for cat in DATASETS for ds in DATASETS[cat]]
+                ],
+                label="Datasets",
+                value=[
+                    "Agent Training / ADP - Synatra",
+                    "Function Calling / xLAM Function Calling 60k"
+                ]
+            )
+            
+            gr.Markdown("### 3. Training Config")
+            
+            max_samples = gr.Slider(100, 100000, value=10000, step=100, label="Max Samples")
+            epochs = gr.Slider(1, 10, value=3, step=1, label="Epochs")
+            batch_size = gr.Slider(1, 8, value=1, step=1, label="Batch Size")
+            learning_rate = gr.Number(value=2e-5, label="Learning Rate")
+            
+            output_repo = gr.Textbox(
+                value="zenlm/zen-vl-4b-agent-custom",
+                label="Output Repository (HuggingFace)"
+            )
+            
+            train_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
+        
+        with gr.Column(scale=2):
+            gr.Markdown("### Training Logs")
+            output = gr.Textbox(label="", lines=35, max_lines=50, show_label=False)
+    
+    train_btn.click(
+        train_model,
+        inputs=[
+            model_choice,
+            dataset_choices,
+            max_samples,
+            epochs,
+            batch_size,
+            learning_rate,
+            output_repo
+        ],
+        outputs=output
+    )
+    
+    gr.Markdown("""
+    ---
+    ### 📊 Available Models
+    - **Language**: nano (0.6B), eco (4B), omni (7B), coder (14B), next (32B)
+    - **Vision-Language**: zen-vl (4B, 8B, 30B)
+    
+    ### 📚 Available Datasets
+    - **Agent Training**: ADP (220k+ trajectories across 15+ configs)
+    - **Function Calling**: xLAM (60k high-quality examples)
+    - **Instruction**: Alpaca (52k samples)
+    
+    ### 💰 Cost Estimates (HF Pro GPU)
+    - 4B model: $3-5 for 10k samples
+    - 8B model: $8-12 for 10k samples
+    - 32B model: $30-50 for 10k samples
+    """)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)