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app.py

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+import torch
+from transformers import AutoTokenizer
+from model import SmolLM2, SmolLM2Config
+import gradio as gr
+
+# Initialize model and tokenizer
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo2-tokenizer")
+model = SmolLM2(SmolLM2Config())
+
+# Load trained weights
+checkpoint = torch.load('checkpoints/checkpoint_step_5000.pt', map_location=device)  # Adjust path as needed
+model.load_state_dict(checkpoint['model_state_dict'])
+model.to(device)
+model.eval()
+
+def generate_text(prompt, max_length=100, temperature=0.7, top_k=50):
+    """Generate text from a prompt"""
+    # Tokenize the prompt
+    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+    
+    # Generate
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids,
+            max_new_tokens=max_length,
+            temperature=temperature,
+            top_k=top_k
+        )
+    
+    # Decode and return the generated text
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return generated_text
+
+# Gradio interface
+def gradio_interface(prompt, max_length, temperature, top_k):
+    return generate_text(prompt, int(max_length), float(temperature), int(top_k))
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
+        gr.Slider(minimum=10, maximum=500, value=100, step=10, label="Max Length"),
+        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Top K"),
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="SmolLM2 Text Generation",
+    description="Generate text using the SmolLM2 model"
+)
+
+# For Hugging Face deployment
+app = gr.mount_gradio_app(app, iface)
+
+if __name__ == "__main__":
+    iface.launch() 

checkpoint_step_5500.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b947d53bfe31093fd71b2e839f143cede77db8d4fa2e2edeaac676d18936dc0c
+size 248122116

model.py

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+import math
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.eps = eps
+
+    def forward(self, x):
+        rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+        x_norm = x / rms
+        return self.weight * x_norm
+
+class LlamaRotaryEmbedding(nn.Module):
+    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_position_embeddings = max_position_embeddings
+        self.dim = dim
+
+    def forward(self, x, seq_len):
+        t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb
+
+def rotate_half(x):
+    x1, x2 = x[..., :x.shape[-1]//2], x[..., x.shape[-1]//2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    cos = cos.unsqueeze(0).unsqueeze(0)
+    sin = sin.unsqueeze(0).unsqueeze(0)
+    cos = cos.expand(q.shape[0], q.shape[1], -1, -1)
+    sin = sin.expand(k.shape[0], k.shape[1], -1, -1)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+class LlamaSdpaAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.n_embd
+        self.num_heads = config.n_head
+        self.head_dim = config.n_embd // config.n_head
+        self.num_key_value_heads = config.n_head // 3
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim)
+
+    def forward(self, x, attention_mask=None):
+        B, T, C = x.size()
+        q = self.q_proj(x).view(B, T, self.num_heads, self.head_dim)
+        k = self.k_proj(x).view(B, T, self.num_key_value_heads, self.head_dim)
+        v = self.v_proj(x).view(B, T, self.num_key_value_heads, self.head_dim)
+
+        k = k.repeat_interleave(self.num_key_value_groups, dim=2)
+        v = v.repeat_interleave(self.num_key_value_groups, dim=2)
+
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        rotary_emb = self.rotary_emb(x, T)
+        cos, sin = rotary_emb.cos(), rotary_emb.sin()
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, None)
+
+        out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        out = out.transpose(1, 2).contiguous().view(B, T, C)
+        return self.o_proj(out)
+
+class LlamaMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.n_embd, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.n_embd, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.n_embd, 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 LlamaDecoderLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.input_layernorm = LlamaRMSNorm(config.n_embd)
+        self.self_attn = LlamaSdpaAttention(config)
+        self.post_attention_layernorm = LlamaRMSNorm(config.n_embd)
+        self.mlp = LlamaMLP(config)
+
+    def forward(self, x):
+        residual = x
+        x = self.input_layernorm(x)
+        x = self.self_attn(x)
+        x = residual + x
+
+        residual = x
+        x = self.post_attention_layernorm(x)
+        x = self.mlp(x)
+        x = residual + x
+        return x
+
+@dataclass
+class SmolLM2Config:
+    block_size: int = 2048
+    vocab_size: int = 49152
+    n_layer: int = 30
+    n_head: int = 9
+    n_embd: int = 576
+    intermediate_size: int = 1536
+    num_key_value_heads: int = 3
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    initializer_range: float = 0.041666666666666664
+    use_cache: bool = True
+
+class SmolLM2(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.n_embd)
+        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.n_layer)])
+        self.norm = LlamaRMSNorm(config.n_embd, eps=config.rms_norm_eps)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.embed_tokens.weight = self.lm_head.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            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=self.config.initializer_range)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.size()
+        x = self.embed_tokens(idx)
+        
+        for layer in self.layers:
+            x = layer(x)
+            
+        x = self.norm(x)
+        logits = self.lm_head(x)
+
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+            
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond = 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')
+                
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx 

requirements.txt

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+torch
+transformers
+gradio