Testing

app.py

@@ -1,116 +1,113 @@
-import gradio as gr
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer
-from model import CustomSmolLM, ModelConfig
-import os
-
-from huggingface_hub import hf_hub_download
-
-# Configuration
-DEVICE = "cpu"  # Spaces usually run on CPU unless GPU is requested
-# We will host the heavy model weights in a separate Model Repository
-MODEL_REPO_ID = "AmolDuse/SmolLM2-135M-Disecting-Model"
-MODEL_FILENAME = "final.pt"  # Using the stripped version
-
-print("Downloading model weights from Hub...")
-try:
-    MODEL_PATH = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
-    print(f"✅ Model downloaded to {MODEL_PATH}")
-except Exception as e:
-    print(f"⚠️ Could not download model: {e}")
-    MODEL_PATH = "final.pt"  # Fallback to local
-
-print("Loading model and tokenizer...")
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
-
-config = ModelConfig()
-model = CustomSmolLM(config)
-
-# Load weights
-if os.path.exists(MODEL_PATH):
-    state_dict = torch.load(MODEL_PATH, map_location=DEVICE)
-    model.load_state_dict(state_dict)
-    print("✅ Model weights loaded successfully")
-else:
-    print(f"⚠️ Warning: {MODEL_PATH} not found. Running with random weights.")
-
-model.to(DEVICE)
-model.eval()
-
-
-def generate_text(prompt, max_length=50, temperature=0.8):
-    try:
-        if not prompt:
-            return "Please enter a prompt."
-
-        # Ensure inputs are correct types
-        max_length = int(max_length)
-        temperature = float(temperature)
-
-        print(f"Generating: prompt='{prompt}', max_length={max_length}, temp={temperature}")
-
-        input_ids = tokenizer.encode(prompt, return_tensors='pt').to(DEVICE)
-
-        with torch.no_grad():
-            for i in range(max_length):
-                outputs = model(input_ids)
-                logits = outputs['logits']
-
-                # Get next token logits
-                next_token_logits = logits[:, -1, :] / temperature
-
-                # Sample next token
-                probs = F.softmax(next_token_logits, dim=-1)
-                next_token = torch.multinomial(probs, num_samples=1)
-
-                # Append to sequence
-                input_ids = torch.cat([input_ids, next_token], dim=1)
-
-                # Stop if we hit end of sequence
-                if tokenizer.eos_token_id is not None and next_token.item() == tokenizer.eos_token_id:
-                    break
-
-        return tokenizer.decode(input_ids[0], skip_special_tokens=True)
-
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
-        return f"Error during generation: {str(e)}"
-
-
-# Gradio Interface
-with gr.Blocks(title="SmolLM2-135M Dissecting Demo") as demo:
-    gr.Markdown("# SmolLM2-135M Dissecting Demo")
-    gr.Markdown(
-        "A custom implementation of SmolLM2-135M trained from scratch. Enter a prompt to see what it generates!")
-
-    with gr.Row():
-        with gr.Column():
-            prompt_input = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
-            with gr.Row():
-                max_len_input = gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Max Length")
-                temp_input = gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature")
-            generate_btn = gr.Button("Generate", variant="primary")
-
-        with gr.Column():
-            output_text = gr.Textbox(label="Generated Text", lines=10)
-
-    generate_btn.click(
-        fn=generate_text,
-        inputs=[prompt_input, max_len_input, temp_input],
-        outputs=output_text
-    )
-
-    # Add examples
-    gr.Examples(
-        examples=[
-            ["The quick brown fox"],
-            ["Once upon a time"],
-            ["What is English"]
-        ],
-        inputs=prompt_input
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer
+from model import CustomSmolLM, ModelConfig
+import os
+
+from huggingface_hub import hf_hub_download
+
+# Configuration
+DEVICE = "cpu" # Spaces usually run on CPU unless GPU is requested
+# We will host the heavy model weights in a separate Model Repository
+MODEL_REPO_ID = "AmolDuse/SmolLM2-135M-Disecting-Model"
+MODEL_FILENAME = "model.pt" # Using the stripped version
+
+print("Downloading model weights from Hub...")
+try:
+    MODEL_PATH = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
+    print(f"✅ Model downloaded to {MODEL_PATH}")
+except Exception as e:
+    print(f"⚠️ Could not download model: {e}")
+    MODEL_PATH = "model.pt" # Fallback to local
+
+print("Loading model and tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
+
+config = ModelConfig()
+model = CustomSmolLM(config)
+
+# Load weights
+if os.path.exists(MODEL_PATH):
+    state_dict = torch.load(MODEL_PATH, map_location=DEVICE)
+    model.load_state_dict(state_dict)
+    print("✅ Model weights loaded successfully")
+else:
+    print(f"⚠️ Warning: {MODEL_PATH} not found. Running with random weights.")
+
+model.to(DEVICE)
+model.eval()
+
+def generate_text(prompt, max_length=50, temperature=0.8):
+    try:
+        if not prompt:
+            return "Please enter a prompt."
+        
+        # Ensure inputs are correct types
+        max_length = int(max_length)
+        temperature = float(temperature)
+        
+        print(f"Generating: prompt='{prompt}', max_length={max_length}, temp={temperature}")
+        
+        input_ids = tokenizer.encode(prompt, return_tensors='pt').to(DEVICE)
+        
+        with torch.no_grad():
+            for i in range(max_length):
+                outputs = model(input_ids)
+                logits = outputs['logits']
+                
+                # Get next token logits
+                next_token_logits = logits[:, -1, :] / temperature
+                
+                # Sample next token
+                probs = F.softmax(next_token_logits, dim=-1)
+                next_token = torch.multinomial(probs, num_samples=1)
+                
+                # Append to sequence
+                input_ids = torch.cat([input_ids, next_token], dim=1)
+                
+                # Stop if we hit end of sequence
+                if tokenizer.eos_token_id is not None and next_token.item() == tokenizer.eos_token_id:
+                    break
+        
+        return tokenizer.decode(input_ids[0], skip_special_tokens=True)
+        
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return f"Error during generation: {str(e)}"
+
+# Gradio Interface
+with gr.Blocks(title="SmolLM2-135M Dissecting Demo") as demo:
+    gr.Markdown("# SmolLM2-135M Dissecting Demo")
+    gr.Markdown("A custom implementation of SmolLM2-135M trained from scratch. Enter a prompt to see what it generates!")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
+            with gr.Row():
+                max_len_input = gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Max Length")
+                temp_input = gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature")
+            generate_btn = gr.Button("Generate", variant="primary")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="Generated Text", lines=10)
+            
+    generate_btn.click(
+        fn=generate_text,
+        inputs=[prompt_input, max_len_input, temp_input],
+        outputs=output_text
+    )
+    
+    # Add examples
+    gr.Examples(
+        examples=[
+            ["The quick brown fox"],
+            ["Once upon a time"],
+            ["What is English"]
+        ],
+        inputs=prompt_input
+    )
+
+if __name__ == "__main__":
+    demo.launch()

model.py

@@ -1,212 +1,212 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-from dataclasses import dataclass
-
-@dataclass
-class ModelConfig:
-    """Configuration matching SmolLM2-135M"""
-    vocab_size: int = 49152
-    hidden_size: int = 576
-    num_hidden_layers: int = 30
-    num_attention_heads: int = 9
-    intermediate_size: int = 1536
-    max_position_embeddings: int = 2048
-    layer_norm_eps: float = 1e-5
-    hidden_dropout_prob: float = 0.1
-    attention_dropout_prob: float = 0.1
-    
-    @property
-    def head_dim(self):
-        return self.hidden_size // self.num_attention_heads
-
-
-class RotaryEmbedding(nn.Module):
-    """Rotary Position Embedding (RoPE)"""
-    def __init__(self, dim, max_position_embeddings=2048, base=10000):
-        super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer("inv_freq", inv_freq)
-        self.max_seq_len_cached = max_position_embeddings
-        
-        t = torch.arange(self.max_seq_len_cached, dtype=self.inv_freq.dtype)
-        freqs = torch.outer(t, self.inv_freq)
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos(), persistent=False)
-        self.register_buffer("sin_cached", emb.sin(), persistent=False)
-    
-    def forward(self, x, seq_len):
-        return (
-            self.cos_cached[:seq_len, ...],
-            self.sin_cached[:seq_len, ...],
-        )
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin):
-    """Apply rotary position embedding to query and key tensors."""
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class MultiHeadAttention(nn.Module):
-    """Multi-head attention with RoPE"""
-    def __init__(self, config: ModelConfig):
-        super().__init__()
-        self.num_heads = config.num_attention_heads
-        self.head_dim = config.head_dim
-        self.hidden_size = config.hidden_size
-        
-        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
-        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
-        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
-        self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
-        
-        self.rotary_emb = RotaryEmbedding(self.head_dim, config.max_position_embeddings)
-        self.dropout = nn.Dropout(config.attention_dropout_prob)
-    
-    def forward(self, hidden_states, attention_mask=None):
-        batch_size, seq_len, _ = hidden_states.shape
-        
-        # Project to Q, K, V
-        q = self.q_proj(hidden_states)
-        k = self.k_proj(hidden_states)
-        v = self.v_proj(hidden_states)
-        
-        # Reshape for multi-head attention
-        q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
-        k = k.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
-        v = v.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
-        
-        # Apply rotary embeddings
-        cos, sin = self.rotary_emb(v, seq_len)
-        q, k = apply_rotary_pos_emb(q, k, cos, sin)
-        
-        # Attention scores
-        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
-        
-        if attention_mask is not None:
-            attn_weights = attn_weights + attention_mask
-        
-        attn_weights = F.softmax(attn_weights, dim=-1)
-        attn_weights = self.dropout(attn_weights)
-        
-        # Apply attention to values
-        attn_output = torch.matmul(attn_weights, v)
-        
-        # Reshape and project
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(batch_size, seq_len, self.hidden_size)
-        attn_output = self.o_proj(attn_output)
-        
-        return attn_output
-
-
-class MLP(nn.Module):
-    """Feed-forward network"""
-    def __init__(self, config: ModelConfig):
-        super().__init__()
-        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-    
-    def forward(self, x):
-        # SwiGLU activation
-        gate = F.silu(self.gate_proj(x))
-        up = self.up_proj(x)
-        return self.dropout(self.down_proj(gate * up))
-
-
-class TransformerBlock(nn.Module):
-    """Single transformer block"""
-    def __init__(self, config: ModelConfig):
-        super().__init__()
-        self.attention = MultiHeadAttention(config)
-        self.mlp = MLP(config)
-        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-    
-    def forward(self, hidden_states, attention_mask=None):
-        # Pre-norm architecture
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-        hidden_states = self.attention(hidden_states, attention_mask)
-        hidden_states = residual + hidden_states
-        
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-        
-        return hidden_states
-
-
-class CustomSmolLM(nn.Module):
-    """Custom implementation mimicking SmolLM2-135M"""
-    def __init__(self, config: ModelConfig):
-        super().__init__()
-        self.config = config
-        
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.layers = nn.ModuleList([
-            TransformerBlock(config) for _ in range(config.num_hidden_layers)
-        ])
-        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        
-        # Tie weights
-        self.lm_head.weight = self.embed_tokens.weight
-        
-        self.apply(self._init_weights)
-    
-    def _init_weights(self, module):
-        std = 0.02
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-    
-    def forward(self, input_ids, attention_mask=None, labels=None):
-        batch_size, seq_len = input_ids.shape
-        
-        # Create causal mask
-        if attention_mask is None:
-            causal_mask = torch.triu(
-                torch.full((seq_len, seq_len), float('-inf'), device=input_ids.device),
-                diagonal=1
-            )
-            causal_mask = causal_mask.unsqueeze(0).unsqueeze(0)
-        else:
-            causal_mask = None  # Simplified for this example
-        
-        # Embed tokens
-        hidden_states = self.embed_tokens(input_ids)
-        
-        # Pass through transformer blocks
-        for layer in self.layers:
-            hidden_states = layer(hidden_states, causal_mask)
-        
-        hidden_states = self.norm(hidden_states)
-        logits = self.lm_head(hidden_states)
-        
-        loss = None
-        if labels is not None:
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            loss = F.cross_entropy(
-                shift_logits.view(-1, self.config.vocab_size),
-                shift_labels.view(-1)
-            )
-        
-        return {'loss': loss, 'logits': logits}
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from dataclasses import dataclass
+
+@dataclass
+class ModelConfig:
+    """Configuration matching SmolLM2-135M"""
+    vocab_size: int = 49152
+    hidden_size: int = 576
+    num_hidden_layers: int = 30
+    num_attention_heads: int = 9
+    intermediate_size: int = 1536
+    max_position_embeddings: int = 2048
+    layer_norm_eps: float = 1e-5
+    hidden_dropout_prob: float = 0.1
+    attention_dropout_prob: float = 0.1
+    
+    @property
+    def head_dim(self):
+        return self.hidden_size // self.num_attention_heads
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary Position Embedding (RoPE)"""
+    def __init__(self, dim, max_position_embeddings=2048, base=10000):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.max_seq_len_cached = max_position_embeddings
+        
+        t = torch.arange(self.max_seq_len_cached, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+    
+    def forward(self, x, seq_len):
+        return (
+            self.cos_cached[:seq_len, ...],
+            self.sin_cached[:seq_len, ...],
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin):
+    """Apply rotary position embedding to query and key tensors."""
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MultiHeadAttention(nn.Module):
+    """Multi-head attention with RoPE"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.hidden_size = config.hidden_size
+        
+        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        
+        self.rotary_emb = RotaryEmbedding(self.head_dim, config.max_position_embeddings)
+        self.dropout = nn.Dropout(config.attention_dropout_prob)
+    
+    def forward(self, hidden_states, attention_mask=None):
+        batch_size, seq_len, _ = hidden_states.shape
+        
+        # Project to Q, K, V
+        q = self.q_proj(hidden_states)
+        k = self.k_proj(hidden_states)
+        v = self.v_proj(hidden_states)
+        
+        # Reshape for multi-head attention
+        q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        
+        # Apply rotary embeddings
+        cos, sin = self.rotary_emb(v, seq_len)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+        
+        # Attention scores
+        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+        
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+        
+        # Apply attention to values
+        attn_output = torch.matmul(attn_weights, v)
+        
+        # Reshape and project
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(batch_size, seq_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+        
+        return attn_output
+
+
+class MLP(nn.Module):
+    """Feed-forward network"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+    
+    def forward(self, x):
+        # SwiGLU activation
+        gate = F.silu(self.gate_proj(x))
+        up = self.up_proj(x)
+        return self.dropout(self.down_proj(gate * up))
+
+
+class TransformerBlock(nn.Module):
+    """Single transformer block"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.attention = MultiHeadAttention(config)
+        self.mlp = MLP(config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+    
+    def forward(self, hidden_states, attention_mask=None):
+        # Pre-norm architecture
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.attention(hidden_states, attention_mask)
+        hidden_states = residual + hidden_states
+        
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        
+        return hidden_states
+
+
+class CustomSmolLM(nn.Module):
+    """Custom implementation mimicking SmolLM2-135M"""
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.config = config
+        
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([
+            TransformerBlock(config) for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        
+        # Tie weights
+        self.lm_head.weight = self.embed_tokens.weight
+        
+        self.apply(self._init_weights)
+    
+    def _init_weights(self, module):
+        std = 0.02
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+    
+    def forward(self, input_ids, attention_mask=None, labels=None):
+        batch_size, seq_len = input_ids.shape
+        
+        # Create causal mask
+        if attention_mask is None:
+            causal_mask = torch.triu(
+                torch.full((seq_len, seq_len), float('-inf'), device=input_ids.device),
+                diagonal=1
+            )
+            causal_mask = causal_mask.unsqueeze(0).unsqueeze(0)
+        else:
+            causal_mask = None  # Simplified for this example
+        
+        # Embed tokens
+        hidden_states = self.embed_tokens(input_ids)
+        
+        # Pass through transformer blocks
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, causal_mask)
+        
+        hidden_states = self.norm(hidden_states)
+        logits = self.lm_head(hidden_states)
+        
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss = F.cross_entropy(
+                shift_logits.view(-1, self.config.vocab_size),
+                shift_labels.view(-1)
+            )
+        
+        return {'loss': loss, 'logits': logits}

requirements.txt

@@ -1,4 +1,4 @@
-torch
-transformers
-gradio
-huggingface_hub
+torch
+transformers
+gradio>=4.0.0
+huggingface_hub