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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoConfig
+from model import SmolLM2
+import os
+
+# 1. Setup and Loading
+device = "cuda" if torch.cuda.is_available() else "cpu"
+checkpoint_path = "checkpoint_5050.pt"
+tokenizer_path = "./custom_tokenizer"
+
+print(f"Using device: {device}")
+
+# Load Tokenizer
+if os.path.exists(tokenizer_path):
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+else:
+    # Fallback
+    tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Load Model
+config = AutoConfig.from_pretrained("HuggingFaceTB/SmolLM2-135M")
+config.vocab_size = len(tokenizer) # Sync vocab size
+model = SmolLM2(config).to(device)
+
+# Load Checkpoint
+if os.path.exists(checkpoint_path):
+    print(f"Loading checkpoint from {checkpoint_path}")
+    state_dict = torch.load(checkpoint_path, map_location=device, weights_only=True)
+    model.load_state_dict(state_dict)
+else:
+    print("Checkpoint not found! Using random weights.")
+
+model.eval()
+
+# 2. Generation Function
+def generate(prompt, max_new_tokens, temperature, top_k, top_p, repetition_penalty):
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    input_ids = inputs.input_ids
+    
+    # Generation settings
+    gen_kwargs = {
+        "max_new_tokens": int(max_new_tokens),
+        "temperature": float(temperature),
+        "top_k": int(top_k),
+        "top_p": float(top_p),
+        "repetition_penalty": float(repetition_penalty),
+        "do_sample": True,
+        "pad_token_id": tokenizer.pad_token_id,
+        "eos_token_id": tokenizer.eos_token_id
+    }
+
+    # Generate
+    with torch.no_grad():
+        generated_ids = input_ids
+        for _ in range(int(max_new_tokens)):
+            outputs = model(generated_ids)
+            next_token_logits = outputs[:, -1, :]
+            
+            # Repetition Penalty
+            if repetition_penalty != 1.0:
+                for i in range(generated_ids.shape[0]):
+                    for previous_token in set(generated_ids[i].tolist()):
+                        if next_token_logits[i, previous_token] < 0:
+                            next_token_logits[i, previous_token] *= repetition_penalty
+                        else:
+                            next_token_logits[i, previous_token] /= repetition_penalty
+
+            # Temperature
+            next_token_logits = next_token_logits / temperature
+            
+            # Top-K
+            if top_k > 0:
+                indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
+                next_token_logits[indices_to_remove] = float('-inf')
+                
+            # Top-P (Nucleus Sampling)
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                # Shift the indices to the right to keep also the first token above the threshold
+                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)
+                next_token_logits[indices_to_remove] = float('-inf')
+
+            probs = torch.nn.functional.softmax(next_token_logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1)
+            generated_ids = torch.cat([generated_ids, next_token], dim=1)
+            
+            if next_token.item() == tokenizer.eos_token_id:
+                break
+                
+    return tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+# 3. Gradio UI - Redesigned
+with gr.Blocks(theme=gr.themes.Ocean()) as demo:
+    gr.Markdown(
+        """
+        # 🌌 SmolLM2-135M Playground
+        ### A custom 135M parameter model trained from scratch.
+        """
+    )
+    
+    with gr.Row():
+        # Sidebar for Settings
+        with gr.Column(scale=1, variant="panel"):
+            gr.Markdown("### ⚙️ Generation Settings")
+            gr.Markdown("Adjust these parameters to control the creativity and length of the generated text.")
+            
+            max_new_tokens = gr.Slider(minimum=10, maximum=1024, value=150, step=10, label="Max New Tokens", info="Maximum number of tokens to generate.")
+            temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature", info="Higher values mean more random/creative output.")
+            top_k = gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-K", info="Limit to top K tokens.")
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-P", info="Nucleus sampling probability.")
+            repetition_penalty = gr.Slider(minimum=1.0, maximum=2.0, value=1.2, step=0.1, label="Repetition Penalty", info="Penalize repeated tokens.")
+            
+        # Main Content Area
+        with gr.Column(scale=3):
+            prompt = gr.Textbox(
+                label="Input Prompt", 
+                placeholder="Type your prompt here (e.g., 'First Citizen:')...", 
+                lines=5,
+                show_copy_button=True
+            )
+            
+            gr.Examples(
+                examples=[
+                    ["First Citizen:"],
+                    ["The meaning of life is"],
+                    ["Once upon a time"],
+                    ["To be or not to be"],
+                    ["The quick brown fox"]
+                ],
+                inputs=prompt,
+                label="Click on an example to load it:"
+            )
+            
+            generate_btn = gr.Button("✨ Generate Text", variant="primary", size="lg")
+            
+            output = gr.Textbox(
+                label="Generated Output", 
+                lines=12, 
+                show_copy_button=True,
+                interactive=False
+            )
+            
+    # Footer / Info
+    with gr.Accordion("ℹ️ Model Information", open=False):
+        gr.Markdown(
+            """
+            * **Architecture**: SmolLM2 (Transformer with Grouped Query Attention)
+            * **Parameters**: 135M
+            * **Training Data**: Wikitext / Custom Dataset
+            * **Tokenizer**: Custom BPE
+            """
+        )
+
+    generate_btn.click(
+        fn=generate,
+        inputs=[prompt, max_new_tokens, temperature, top_k, top_p, repetition_penalty],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()

checkpoint_5050.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7bc6ed81d4f7cedb6b00bb4dc1cc5ed7178f5380921d19919e26f57327834e8
+size 447967800

custom_tokenizer/added_tokens.json

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+{
+  "<|endoftext|>": 5000
+}

custom_tokenizer/merges.txt

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+#version: 0.2
+Ġ t
+h e
+Ġ a
+o u
+Ġ s
+Ġ m
+i n
+Ġ w
+r e
+h a
+n d
+Ġt he
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+o r
+Ġ f
+e r
+l l
+i t
+o n
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+Ġ c
+e s
+e n
+Ġ n
+Ġ l
+Ġ y
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+a r
+Ġ h
+Ġ o
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+Ġy ou
+Ġ p
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+Ġ I
+Ġ he
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+o t
+s t
+Ġa nd
+o w
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+a n
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+Ġ g
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+Ġb e
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+Ġ in
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+Ġ ha
+l e
+a y
+l d
+i r
+e t
+e d
+u t
+Ġm e
+i m
+it h
+' s
+Ġn ot
+c h
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+Ġ is
+g h
+A nd
+Ġf or
+k e
+Ġ u
+ou r
+Ġw e
+o o
+i ll
+Ġ e
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+r i
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+E N
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+Ġ re
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+I N
+l y
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+Ġl i
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+u r
+Ġth is
+a l
+I O
+Ġs o
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+Ġ on
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+A R
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model.py

@@ -0,0 +1,136 @@
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        mean_square = (x.pow(2).mean(-1, keepdim=True))
+        x = x * torch.rsqrt(mean_square + self.eps)
+        return self.weight * x
+
+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):
+    # q, k: [bsz, heads, seq_len, head_dim]
+    # cos, sin: [seq_len, head_dim] -> unsqueeze to [1, 1, seq_len, head_dim]
+    cos = cos.unsqueeze(0).unsqueeze(0)
+    sin = sin.unsqueeze(0).unsqueeze(0)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        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.act_fn = nn.SiLU()
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+class Attention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+
+        self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=False)
+
+    def forward(self, x, cos, sin, mask=None):
+        bsz, seq_len, _ = x.shape
+        q = self.q_proj(x).view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(x).view(bsz, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(x).view(bsz, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+
+        k = k.repeat_interleave(self.num_key_value_groups, dim=1)
+        v = v.repeat_interleave(self.num_key_value_groups, dim=1)
+
+        attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(self.head_dim)
+        if mask is not None:
+            attn_weights = attn_weights + mask
+
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        output = torch.matmul(attn_weights, v)
+        output = output.transpose(1, 2).contiguous().view(bsz, seq_len, -1)
+        return self.o_proj(output)
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self_attn = Attention(config)
+        self.mlp = MLP(config)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(self, x, cos, sin, mask=None):
+        h = x + self.self_attn(self.input_layernorm(x), cos, sin, mask)
+        out = h + self.mlp(self.post_attention_layernorm(h))
+        return out
+
+class SmolLM2(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([Block(config) for _ in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # RoPE setup
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.rope_theta = getattr(config, "rope_theta", 10000.0)
+        self.inv_freq = 1.0 / (self.rope_theta ** (torch.arange(0, self.head_dim, 2).float() / self.head_dim))
+        self.max_pos = config.max_position_embeddings * 2
+        self._set_cos_sin_cache(self.max_pos)
+
+    def _set_cos_sin_cache(self, seq_len):
+        t = torch.arange(seq_len, dtype=torch.float32)
+        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, input_ids):
+        bsz, seq_len = input_ids.shape
+        x = self.embed_tokens(input_ids)
+
+        if self.cos_cached.device != x.device or self.cos_cached.shape[0] < seq_len:
+            self.inv_freq = self.inv_freq.to(x.device)
+            self._set_cos_sin_cache(max(seq_len, 2048))
+
+        cos = self.cos_cached[:seq_len].to(dtype=x.dtype)
+        sin = self.sin_cached[:seq_len].to(dtype=x.dtype)
+
+        mask = None
+        if seq_len > 1:
+            mask = torch.full((seq_len, seq_len), float("-inf"), device=input_ids.device)
+            mask = torch.triu(mask, diagonal=1)
+
+        for layer in self.layers:
+            x = layer(x, cos, sin, mask)
+
+        x = self.norm(x)
+        logits = self.lm_head(x)
+        return logits

requirements.txt

@@ -0,0 +1,4 @@
+torch
+transformers
+gradio==5.0.0
+tokenizers