Upload app.py with huggingface_hub

app.py

@@ -1,331 +1,691 @@
-import gradio as gr
-import torch
-import os
+#!/usr/bin/env python3
+"""
+OpenLLM Real Models App - Final working version with correct attribute naming
+"""
+
 import json
-import time
+import logging
+import math
 from pathlib import Path
-import subprocess
-import sys
-
-# Add the core module to path
-sys.path.append('../core/src')
-
-try:
-    from train_model_improved import ImprovedModelTrainer
-    from model import GPTConfig, GPTModel
-    from data_loader import TextDataset
-except ImportError as e:
-    print(f"Import error: {e}")
-    # Fallback for when core modules aren't available
-    pass
-
-class LiveTrainingInterface:
+from typing import Any, Dict, Optional
+
+import gradio as gr
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub import snapshot_download
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class GPTConfig:
+    """GPT model configuration"""
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        n_layer=6,
+        n_head=8,
+        n_embd=512,
+        block_size=1024,
+        dropout=0.1,
+        bias=False,
+        **kwargs,
+    ):
+        # Accept any additional kwargs to handle extra config fields
+        self.vocab_size = vocab_size
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_embd = n_embd
+        self.block_size = block_size
+        self.dropout = dropout
+        self.bias = bias
+
+
+class GPT(nn.Module):
+    """GPT-style transformer model - EXACT architecture matching the saved model"""
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.vocab_size is not None
+        assert config.block_size is not None
+        self.config = config
+
+        # Create the transformer module with the exact naming convention
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=nn.Embedding(config.vocab_size, config.n_embd),
+                wpe=nn.Embedding(config.block_size, config.n_embd),
+                drop=nn.Dropout(config.dropout),
+                h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+                ln_f=nn.LayerNorm(config.n_embd),
+            )
+        )
+
+        # Language model head - Use bias=False to match saved models
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # Initialize weights
+        self.apply(self._init_weights)
+        for pn, p in self.named_parameters():
+            if pn.endswith("c_proj.weight"):
+                torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        device = idx.device
+        b, t = idx.size()
+        assert (
+            t <= self.config.block_size
+        ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)
+        tok_emb = self.transformer.wte(idx)
+        pos_emb = self.transformer.wpe(pos)
+        x = self.transformer.drop(tok_emb + pos_emb)
+
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+
+        if targets is not None:
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(
+                logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1
+            )
+        else:
+            logits = self.lm_head(x[:, [-1], :])
+            loss = None
+
+        return logits, loss
+
+    def generate(
+        self, idx, max_new_tokens, temperature=1.0, top_k=None, top_p=None, do_sample=True
+    ):
+        for _ in range(max_new_tokens):
+            idx_cond = (
+                idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size :]
+            )
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float("Inf")
+
+            if top_p is not None:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+                sorted_indices_to_remove[..., 0] = 0
+                indices_to_remove = sorted_indices_to_remove.scatter(
+                    1, sorted_indices, sorted_indices_to_remove
+                )
+                logits[indices_to_remove] = -float("Inf")
+
+            probs = F.softmax(logits, dim=-1)
+            if do_sample:
+                idx_next = torch.multinomial(probs, num_samples=1)
+            else:
+                _, idx_next = torch.topk(probs, k=1, dim=-1)
+
+            idx = torch.cat((idx, idx_next), dim=1)
+
+        return idx
+
+
+class Block(nn.Module):
+    """Transformer block with self-attention and feed-forward layers"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class CausalSelfAttention(nn.Module):
+    """Multi-head self-attention with causal masking - FINAL WORKING VERSION"""
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        self.use_bias = config.bias  # Use different name for the boolean flag
+
+        # REGISTER THE ATTENTION BIAS as a buffer (not parameter) to match saved model
+        # This is actually an attention mask, not a learnable bias
+        if config.bias:
+            # Create a causal attention mask buffer
+            mask = torch.tril(torch.ones(config.block_size, config.block_size))
+            mask = mask.view(1, 1, config.block_size, config.block_size)
+            self.register_buffer("bias", mask)  # This matches the saved model's 'bias' key
+        else:
+            self.register_buffer("bias", None)
+
+    def forward(self, x):
+        B, T, C = x.size()
+
+        # Calculate query, key, values for all heads
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+
+        # Causal self-attention using the bias mask
+        if self.bias is not None:
+            # Use the causal mask
+            attn_mask = self.bias[:, :, :T, :T]
+            y = F.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                attn_mask=attn_mask,
+                dropout_p=self.dropout if self.training else 0,
+                is_causal=False,
+            )
+        else:
+            # Use built-in causal attention
+            y = F.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                attn_mask=None,
+                dropout_p=self.dropout if self.training else 0,
+                is_causal=True,
+            )
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+
+        # Output projection
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+
+class MLP(nn.Module):
+    """Multi-layer perceptron"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.gelu = nn.GELU()
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+class RealOpenLLMInference:
+    """Real OpenLLM inference engine using actual trained models"""
+
     def __init__(self):
-        self.base_model = "lemms/openllm-small-extended-9k"
-        self.training_configs = self.load_training_options()
-        self.current_training = None
-        self.training_logs = []
-        
-    def load_training_options(self):
-        """Load available training configuration options"""
-        return {
-            "learning_rate": [1e-4, 3e-4, 5e-4, 1e-3],
-            "batch_size": [4, 8, 16, 32],
-            "training_steps": [1000, 2000, 5000, 10000],
-            "gradient_accumulation": [1, 2, 4, 8],
-            "optimizer": ["AdamW", "Adam", "SGD"],
-            "scheduler": ["Cosine", "Linear", "Constant"],
-            "weight_decay": [0.01, 0.1, 0.0],
-            "gradient_clipping": [0.5, 1.0, 2.0],
-            "warmup_steps": [100, 500, 1000]
+        self.models = {}
+        self.tokenizers = {}
+        self.current_model = None
+
+        # Real model configurations from Hugging Face
+        self.model_configs = {
+            "openllm-small-extended-4k": {
+                "name": "OpenLLM Small (4k steps)",
+                "description": "Real model trained for 4,000 steps - Early training stage",
+                "hf_repo": "lemms/openllm-small-extended-4k",
+                "training_steps": 4000,
+                "parameters": "35.8M",
+            },
+            "openllm-small-extended-6k": {
+                "name": "OpenLLM Small (6k steps)",
+                "description": "Real model trained for 6,000 steps - Improved coherence (Perplexity: 816.040)",
+                "hf_repo": "lemms/openllm-small-extended-6k",
+                "training_steps": 6000,
+                "parameters": "35.8M",
+            },
+            "openllm-small-extended-7k": {
+                "name": "OpenLLM Small (7k steps)",
+                "description": "Real model trained for 7,000 steps - Enhanced quality (Loss: 2.100, Perplexity: 8.200)",
+                "hf_repo": "lemms/openllm-small-extended-7k",
+                "training_steps": 7000,
+                "parameters": "35.8M",
+            },
+            "openllm-small-extended-8k": {
+                "name": "OpenLLM Small (8k steps)",
+                "description": "Real model trained for 8,000 steps - Sophisticated understanding",
+                "hf_repo": "lemms/openllm-small-extended-8k",
+                "training_steps": 8000,
+                "parameters": "35.8M",
+            },
+            "openllm-small-extended-9k": {
+                "name": "OpenLLM Small (9k steps)",
+                "description": "Real model trained for 9,000 steps - Best performing model",
+                "hf_repo": "lemms/openllm-small-extended-9k",
+                "training_steps": 9000,
+                "parameters": "35.8M",
+            },
+            "openllm-small-extended-10k": {
+                "name": "OpenLLM Small (10k steps)",
+                "description": "Real model trained for 10,000 steps - Latest extended training",
+                "hf_repo": "lemms/openllm-small-extended-10k",
+                "training_steps": 10000,
+                "parameters": "35.8M",
+            },
+            "openllm-small-extended-10k-improved": {
+                "name": "OpenLLM Small (10k steps - Improved)",
+                "description": "Real model trained for 10,000 steps with improved training process - Proper checkpoint format",
+                "hf_repo": "lemms/openllm-small-extended-10k-improved",
+                "training_steps": 10000,
+                "parameters": "35.8M",
+            },
         }
-    
-    def start_training(self, config):
-        """Start a training session with the given configuration"""
+
+        logger.info("🚀 Real OpenLLM Inference Engine initialized")
+
+    def load_model_from_hf(self, model_id: str) -> bool:
+        """Load a real model from Hugging Face"""
         try:
-            # Validate configuration
-            if not self.validate_config(config):
-                return "❌ Invalid configuration. Please check your settings."
-            
-            # Create training configuration
-            training_config = {
-                "base_model": self.base_model,
-                "learning_rate": float(config["learning_rate"]),
-                "batch_size": int(config["batch_size"]),
-                "training_steps": int(config["training_steps"]),
-                "gradient_accumulation": int(config["gradient_accumulation"]),
-                "optimizer": config["optimizer"],
-                "scheduler": config["scheduler"],
-                "weight_decay": float(config["weight_decay"]),
-                "gradient_clipping": float(config["gradient_clipping"]),
-                "warmup_steps": int(config["warmup_steps"]),
-                "output_dir": f"models/training-{int(time.time())}",
-                "save_steps": 500,
-                "eval_steps": 1000,
-                "logging_steps": 100
-            }
-            
-            # Start training in background
-            self.current_training = training_config
-            self.training_logs = []
-            
-            return f"🚀 Training started with configuration:\n{json.dumps(training_config, indent=2)}"
-            
+            config = self.model_configs.get(model_id)
+            if not config:
+                logger.error(f"❌ Unknown model ID: {model_id}")
+                return False
+
+            logger.info(f"📥 Loading real model from HF: {config['hf_repo']}")
+
+            # Download model from Hugging Face
+            local_dir = snapshot_download(
+                repo_id=config["hf_repo"],
+                repo_type="model",
+                local_dir=f"temp_{model_id}",
+                allow_patterns=["*.pt", "*.json", "*.model", "*.bin"],
+            )
+
+            logger.info(f"✅ Downloaded model to: {local_dir}")
+
+            # Load model and tokenizer
+            success = self._load_model_and_tokenizer(local_dir, model_id)
+            if success:
+                self.current_model = model_id
+                logger.info(f"✅ Successfully loaded real model: {model_id}")
+                return True
+            else:
+                return False
+
         except Exception as e:
-            return f"❌ Error starting training: {str(e)}"
-    
-    def validate_config(self, config):
-        """Validate training configuration"""
+            logger.error(f"❌ Failed to load real model from HF {model_id}: {e}")
+            return False
+
+    def _load_model_and_tokenizer(self, model_dir: str, model_id: str) -> bool:
+        """Load model and tokenizer from local directory"""
         try:
-            required_fields = ["learning_rate", "batch_size", "training_steps"]
-            for field in required_fields:
-                if field not in config or not config[field]:
-                    return False
+            model_path = Path(model_dir)
+
+            # Load model configuration
+            config_file = model_path / "config.json"
+            if config_file.exists():
+                with open(config_file, "r") as f:
+                    config_data = json.load(f)
+
+                logger.info(f"📋 Config data keys: {list(config_data.keys())}")
+
+                # Handle different config structures
+                if "model_config" in config_data:
+                    # Extract model_config section
+                    model_config_data = config_data["model_config"]
+                else:
+                    # Use the entire config as model config
+                    model_config_data = config_data
+
+                # Create GPTConfig with only the expected parameters
+                expected_params = {
+                    "vocab_size",
+                    "n_layer",
+                    "n_head",
+                    "n_embd",
+                    "block_size",
+                    "dropout",
+                    "bias",
+                }
+
+                config_kwargs = {}
+                for key, value in model_config_data.items():
+                    if key in expected_params:
+                        config_kwargs[key] = value
+
+                logger.info(f"🔧 Using config parameters: {config_kwargs}")
+                model_config = GPTConfig(**config_kwargs)
+            else:
+                # Default configuration for OpenLLM small models
+                model_config = GPTConfig(
+                    vocab_size=32000,
+                    n_layer=6,
+                    n_head=8,
+                    n_embd=512,
+                    block_size=1024,
+                    dropout=0.1,
+                    bias=False,
+                )
+
+            # Load model weights
+            model_file = model_path / "best_model.pt"
+            if not model_file.exists():
+                model_file = model_path / "model.pt"
+            if not model_file.exists():
+                model_file = model_path / "pytorch_model.bin"
+
+            if model_file.exists():
+                logger.info(f"📦 Loading model from: {model_file}")
+                model = GPT(model_config)
+                checkpoint = torch.load(model_file, map_location="cpu")
+
+                # Handle different checkpoint formats
+                if isinstance(checkpoint, dict):
+                    if "model_state_dict" in checkpoint:
+                        # Extract the actual model weights
+                        state_dict = checkpoint["model_state_dict"]
+                        logger.info(f"📋 Loading from model_state_dict with {len(state_dict)} keys")
+                    elif "model" in checkpoint:
+                        state_dict = checkpoint["model"]
+                        logger.info(f"📋 Loading from model with {len(state_dict)} keys")
+                    else:
+                        # Try to load directly as state dict
+                        state_dict = checkpoint
+                        logger.info(f"📋 Loading direct state dict with {len(state_dict)} keys")
+                else:
+                    # Direct state dict
+                    state_dict = checkpoint
+                    logger.info(f"📋 Loading direct state dict with {len(state_dict)} keys")
+
+                # Load the state dict
+                model.load_state_dict(state_dict)
+                model.eval()
+                self.models[model_id] = model
+                logger.info(f"✅ Model loaded successfully")
+            else:
+                logger.error(f"❌ Model file not found in {model_dir}")
+                logger.error(f"   Available files: {list(model_path.glob('*'))}")
+                return False
+
+            # Load tokenizer
+            tokenizer_file = model_path / "tokenizer.model"
+            if tokenizer_file.exists():
+                tokenizer = spm.SentencePieceProcessor()
+                tokenizer.load(str(tokenizer_file))
+                self.tokenizers[model_id] = tokenizer
+                logger.info(f"✅ Tokenizer loaded successfully")
+            else:
+                logger.error(f"❌ Tokenizer file not found in {model_dir}")
+                return False
+
             return True
-        except:
+
+        except Exception as e:
+            logger.error(f"❌ Failed to load model and tokenizer: {e}")
+            import traceback
+
+            logger.error(f"📋 Full traceback: {traceback.format_exc()}")
             return False
-    
-    def get_training_status(self):
-        """Get current training status"""
-        if self.current_training is None:
-            return "📊 No active training session"
-        
-        # Simulate training progress
-        progress = {
-            "status": "Training in progress...",
-            "current_step": 500,
-            "total_steps": self.current_training["training_steps"],
-            "loss": 5.8,
-            "learning_rate": self.current_training["learning_rate"]
-        }
-        
-        return f"📊 Training Status:\n{json.dumps(progress, indent=2)}"
-    
-    def stop_training(self):
-        """Stop current training session"""
-        if self.current_training is None:
-            return "❌ No active training session to stop"
+
+    def generate_text(
+        self,
+        prompt: str,
+        max_length: int = 100,
+        temperature: float = 0.7,
+        top_k: int = 50,
+        top_p: float = 0.9,
+    ) -> str:
+        """Generate text using the loaded real model"""
+        if not self.current_model or self.current_model not in self.models:
+            return "❌ No model loaded. Please select a model first."
+
+        try:
+            model = self.models[self.current_model]
+            tokenizer = self.tokenizers[self.current_model]
+
+            # Tokenize input
+            input_ids = tokenizer.encode(prompt)
+            input_tensor = torch.tensor([input_ids], dtype=torch.long)
+
+            logger.info(f"🎯 Generating text with prompt: '{prompt[:50]}...'")
+            logger.info(
+                f"📊 Parameters: max_length={max_length}, temperature={temperature}, top_k={top_k}, top_p={top_p}"
+            )
+
+            # Generate text
+            with torch.no_grad():
+                output_ids = model.generate(
+                    input_tensor,
+                    max_new_tokens=max_length,
+                    temperature=temperature,
+                    top_k=top_k,
+                    top_p=top_p,
+                    do_sample=True,
+                )
+
+            # Decode output
+            generated_text = tokenizer.decode(output_ids[0].tolist())
+
+            # Remove the input prompt from the output
+            if generated_text.startswith(prompt):
+                generated_text = generated_text[len(prompt) :].strip()
+
+            logger.info(f"✅ Generated text: '{generated_text[:100]}...'")
+            return generated_text
+
+        except Exception as e:
+            error_msg = f"❌ Generation failed: {str(e)}"
+            logger.error(error_msg)
+            import traceback
+
+            logger.error(f"📋 Full traceback: {traceback.format_exc()}")
+            return error_msg
+
+
+# Initialize the real inference engine
+inference_engine = RealOpenLLMInference()
+
+
+def load_model_info(model_id: str) -> str:
+    """Get information about a specific model"""
+    config = inference_engine.model_configs.get(model_id)
+    if config:
+        return f"**{config['name']}**\n\n{config['description']}\n\n**Parameters:** {config['parameters']}\n**Training Steps:** {config['training_steps']:,}"
+    return "❌ Model not found"
+
+
+def generate_text_interface(
+    model_id: str, prompt: str, max_length: int, temperature: float, top_k: int, top_p: float
+) -> str:
+    """Gradio interface function for text generation"""
+    try:
+        # Load model if not already loaded
+        if model_id not in inference_engine.models:
+            logger.info(f"🔄 Loading real model: {model_id}")
+            success = inference_engine.load_model_from_hf(model_id)
+            if not success:
+                return f"❌ Failed to load real model: {model_id}"
+
+        # Generate text
+        result = inference_engine.generate_text(
+            prompt=prompt, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p
+        )
+
+        return result
+
+    except Exception as e:
+        error_msg = f"❌ Error in generation interface: {str(e)}"
+        logger.error(error_msg)
+        return error_msg
+
+
+# Create Gradio interface
+def create_interface():
+    """Create the Gradio interface"""
+
+    with gr.Blocks(title="🚀 OpenLLM Real Models Space", theme=gr.themes.Soft()) as interface:
+        # Header
+        gr.Markdown(
+            """
+        # 🚀 OpenLLM Real Models Space
         
-        self.current_training = None
-        return "⏹️ Training stopped"
-    
-    def download_model(self):
-        """Download the trained model"""
-        if self.current_training is None:
-            return "❌ No trained model available"
+        Welcome to the OpenLLM Real Models Space! This interface uses **actual trained models** from Hugging Face.
         
-        # This would implement actual model download
-        return "📥 Model download started (this is a demo)"
-
-def create_training_interface():
-    """Create the Gradio interface for live training"""
-    
-    trainer = LiveTrainingInterface()
-    
-    with gr.Blocks(title="OpenLLM Live Training Space", theme=gr.themes.Soft()) as interface:
-        gr.Markdown("""
-        # 🚀 OpenLLM Live Training Space
+        ## 🎯 Real Trained Models
         
-        Welcome to the **OpenLLM Live Training Space**! This is where you can train new language models interactively.
+        We provide **5 different real models** with varying training steps:
         
-        ## 🎯 What You Can Do
-        - **Start training** from the latest model checkpoint (9k model)
-        - **Configure training parameters** in real-time
-        - **Monitor training progress** with live metrics
-        - **Download or deploy** newly trained models
+        | Model | Training Steps | Parameters | Performance |
+        |-------|---------------|------------|-------------|
+        | **4k Model** | 4,000 | 35.8M | Early training stage |
+        | **6k Model** | 6,000 | 35.8M | Improved coherence (Perplexity: 816.040) |
+        | **7k Model** | 7,000 | 35.8M | Enhanced quality (Loss: 2.100, Perplexity: 8.200) |
+        | **8k Model** | 8,000 | 35.8M | Sophisticated understanding |
+        | **9k Model** | 9,000 | 35.8M | Best performing model |
+        | **10k Model** | 10,000 | 35.8M | Latest extended training |
         
-        ## 📋 Training Configuration
-        """)
+        **These are real GPT-style transformer models trained on Wikipedia passages from the SQuAD dataset.**
         
+        ---
+        """
+        )
+
         with gr.Row():
             with gr.Column(scale=1):
-                gr.Markdown("### ⚙️ Training Parameters")
-                
-                learning_rate = gr.Dropdown(
-                    choices=trainer.training_configs["learning_rate"],
-                    value=3e-4,
-                    label="Learning Rate",
-                    info="How fast the model learns"
-                )
-                
-                batch_size = gr.Dropdown(
-                    choices=trainer.training_configs["batch_size"],
-                    value=8,
-                    label="Batch Size",
-                    info="Number of samples per training step"
-                )
-                
-                training_steps = gr.Dropdown(
-                    choices=trainer.training_configs["training_steps"],
-                    value=2000,
-                    label="Training Steps",
-                    info="How long to train"
-                )
-                
-                gradient_accumulation = gr.Dropdown(
-                    choices=trainer.training_configs["gradient_accumulation"],
-                    value=2,
-                    label="Gradient Accumulation",
-                    info="Memory optimization technique"
+                # Model selection
+                model_dropdown = gr.Dropdown(
+                    choices=list(inference_engine.model_configs.keys()),
+                    value="openllm-small-extended-10k",
+                    label="🎯 Select Model",
+                    info="Choose the real trained model to use",
                 )
-                
-                optimizer = gr.Dropdown(
-                    choices=trainer.training_configs["optimizer"],
-                    value="AdamW",
-                    label="Optimizer",
-                    info="Optimization algorithm"
-                )
-                
-                scheduler = gr.Dropdown(
-                    choices=trainer.training_configs["scheduler"],
-                    value="Cosine",
-                    label="Scheduler",
-                    info="Learning rate schedule"
-                )
-                
-                weight_decay = gr.Dropdown(
-                    choices=trainer.training_configs["weight_decay"],
-                    value=0.01,
-                    label="Weight Decay",
-                    info="Regularization strength"
-                )
-                
-                gradient_clipping = gr.Dropdown(
-                    choices=trainer.training_configs["gradient_clipping"],
-                    value=1.0,
-                    label="Gradient Clipping",
-                    info="Gradient stability"
-                )
-                
-                warmup_steps = gr.Dropdown(
-                    choices=trainer.training_configs["warmup_steps"],
-                    value=500,
-                    label="Warmup Steps",
-                    info="Learning rate warmup"
+
+                # Model information display
+                model_info = gr.Markdown(
+                    value=load_model_info("openllm-small-extended-10k"), label="📋 Model Information"
                 )
-            
-            with gr.Column(scale=1):
-                gr.Markdown("### 🎮 Training Controls")
-                
-                start_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
-                stop_btn = gr.Button("⏹️ Stop Training", variant="stop", size="lg")
-                status_btn = gr.Button("📊 Check Status", size="lg")
-                download_btn = gr.Button("📥 Download Model", size="lg")
-                
-                gr.Markdown("### 📊 Training Status")
-                status_output = gr.Textbox(
-                    label="Status",
-                    value="Ready to start training",
-                    lines=10,
-                    interactive=False
+
+                # Update model info when selection changes
+                model_dropdown.change(
+                    fn=load_model_info, inputs=[model_dropdown], outputs=[model_info]
                 )
-                
-                gr.Markdown("### 📝 Training Logs")
-                logs_output = gr.Textbox(
-                    label="Logs",
-                    value="No logs yet",
-                    lines=8,
-                    interactive=False
+
+            with gr.Column(scale=2):
+                # Input prompt
+                prompt_input = gr.Textbox(
+                    lines=5,
+                    label="📝 Input Prompt",
+                    placeholder="Enter your text prompt here...",
+                    info="The text that will be used as input for generation",
                 )
-        
-        # Training scenarios section
-        gr.Markdown("""
-        ## 🎯 Training Scenarios
-        
-        ### Quick Experiments (1000 steps)
-        - **Duration**: 10-30 minutes
-        - **Purpose**: Test different learning rates and configurations
-        - **Use case**: Hyperparameter exploration and rapid prototyping
-        
-        ### Medium Training (5000 steps)
-        - **Duration**: 1-3 hours
-        - **Purpose**: Significant model improvement and fine-tuning
-        - **Use case**: Model optimization and performance enhancement
-        
-        ### Extended Training (10000 steps)
-        - **Duration**: 3-8 hours
-        - **Purpose**: Maximum performance improvement
-        - **Use case**: Production model development and research
-        """)
-        
-        # Event handlers
-        def start_training_handler(lr, bs, steps, ga, opt, sched, wd, gc, warmup):
-            config = {
-                "learning_rate": lr,
-                "batch_size": bs,
-                "training_steps": steps,
-                "gradient_accumulation": ga,
-                "optimizer": opt,
-                "scheduler": sched,
-                "weight_decay": wd,
-                "gradient_clipping": gc,
-                "warmup_steps": warmup
-            }
-            return trainer.start_training(config)
-        
-        def stop_training_handler():
-            return trainer.stop_training()
-        
-        def status_handler():
-            return trainer.get_training_status()
-        
-        def download_handler():
-            return trainer.download_model()
-        
-        # Connect event handlers
-        start_btn.click(
-            fn=start_training_handler,
-            inputs=[learning_rate, batch_size, training_steps, gradient_accumulation, 
-                   optimizer, scheduler, weight_decay, gradient_clipping, warmup_steps],
-            outputs=status_output
-        )
-        
-        stop_btn.click(
-            fn=stop_training_handler,
-            outputs=status_output
-        )
-        
-        status_btn.click(
-            fn=status_handler,
-            outputs=status_output
+
+                # Generation parameters
+                with gr.Row():
+                    max_length = gr.Slider(
+                        minimum=10,
+                        maximum=500,
+                        value=100,
+                        step=10,
+                        label="📏 Max Length",
+                        info="Maximum number of tokens to generate",
+                    )
+
+                    temperature = gr.Slider(
+                        minimum=0.1,
+                        maximum=2.0,
+                        value=0.7,
+                        step=0.1,
+                        label="🌡️ Temperature",
+                        info="Controls randomness (higher = more random)",
+                    )
+
+                with gr.Row():
+                    top_k = gr.Slider(
+                        minimum=1,
+                        maximum=100,
+                        value=50,
+                        step=1,
+                        label="🔝 Top-K",
+                        info="Number of highest probability tokens to consider",
+                    )
+
+                    top_p = gr.Slider(
+                        minimum=0.1,
+                        maximum=1.0,
+                        value=0.9,
+                        step=0.1,
+                        label="📊 Top-P",
+                        info="Nucleus sampling parameter",
+                    )
+
+                # Generate button
+                generate_btn = gr.Button("🚀 Generate Text", variant="primary", size="lg")
+
+        # Output
+        output_text = gr.Textbox(
+            lines=10, label="🎯 Generated Text", info="The generated text will appear here"
         )
-        
-        download_btn.click(
-            fn=download_handler,
-            outputs=status_output
+
+        # Connect the generate button
+        generate_btn.click(
+            fn=generate_text_interface,
+            inputs=[model_dropdown, prompt_input, max_length, temperature, top_k, top_p],
+            outputs=[output_text],
         )
-        
+
         # Footer
-        gr.Markdown("""
+        gr.Markdown(
+            """
         ---
         
-        ## 📚 Educational Value
+        ## 🔧 Technical Details
         
-        This space provides hands-on experience with:
-        - **Understanding hyperparameters** and their effects on model performance
-        - **Real-time observation** of training dynamics and convergence
-        - **Learning best practices** for language model training
-        - **Experimenting with different configurations** without local setup
+        - **Architecture**: GPT-style transformer decoder
+        - **Model Size**: Small (6 layers, 8 heads, 512 embedding dim)
+        - **Vocabulary**: 32k tokens (SentencePiece BPE)
+        - **Training Data**: Wikipedia passages from SQuAD dataset
+        - **Framework**: PyTorch with real trained models
+        - **Gradio Version**: 4.44.1 (latest)
         
-        ## 🔗 Related Resources
+        **These models generate actual text based on their training on Wikipedia content.**
         
-        - **[Model Demo Space](https://huggingface.co/spaces/lemms/llm)** - Test trained models
-        - **[GitHub Repository](https://github.com/louischua/osllm)** - Source code and documentation
-        - **[Training Documentation](../docs/TRAINING_IMPROVEMENTS.md)** - Detailed training guide
-        
-        ---
-        
-        *This is a demonstration of the OpenLLM training capabilities. For production training, please refer to the full documentation.*
-        """)
-    
+        **Model Sources:**
+        - [4k Model](https://huggingface.co/lemms/openllm-small-extended-4k)
+        - [6k Model](https://huggingface.co/lemms/openllm-small-extended-6k)
+        - [7k Model](https://huggingface.co/lemms/openllm-small-extended-7k)
+        - [8k Model](https://huggingface.co/lemms/openllm-small-extended-8k)
+        - [9k Model](https://huggingface.co/lemms/openllm-small-extended-9k)
+        - [10k Model](https://huggingface.co/lemms/openllm-small-extended-10k)
+        """
+        )
+
     return interface
 
+
 # Create and launch the interface
 if __name__ == "__main__":
-    interface = create_training_interface()
-    interface.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        debug=True
-    )
+    interface = create_interface()
+    interface.launch(server_name="0.0.0.0", server_port=7860, share=False, debug=True)