Upload HF_SPACE_APP.py with huggingface_hub

HF_SPACE_APP.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import math
+import os
+import gc
+from huggingface_hub import hf_hub_download
+
+# --- MODEL ARCHITECTURE ---
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.w = nn.Parameter(torch.ones(dim))
+        self.eps = eps
+    def forward(self, x):
+        rms = torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps)
+        return (x.float() * rms).to(x.dtype) * self.w
+
+class LoRA(nn.Module):
+    def __init__(self, in_f, out_f, rank):
+        super().__init__()
+        self.A = nn.Parameter(torch.randn(rank, in_f) * 0.01)
+        self.B = nn.Parameter(torch.zeros(out_f, rank))
+    def forward(self, x):
+        return F.linear(F.linear(x, self.A), self.B)
+
+class TCNLayer(nn.Module):
+    def __init__(self, d_model, d_ff, kernel_size, dilation, lora_rank):
+        super().__init__()
+        self.dilation = dilation
+        self.padding = (kernel_size - 1) * dilation
+        self.norm = RMSNorm(d_model)
+        
+        # In Space, weights are loaded via state_dict, but logic remains Fractal
+        self.w_in = nn.Parameter(torch.zeros(2*d_ff, d_model))
+        self.w_dw = nn.Parameter(torch.zeros(d_ff, 1, kernel_size))
+        self.w_out = nn.Parameter(torch.zeros(d_model, d_ff))
+        
+        self.lora_in = LoRA(d_model, 2*d_ff, lora_rank)
+        self.lora_out = LoRA(d_ff, d_model, lora_rank)
+        self.scale = nn.Parameter(torch.tensor(0.1))
+    
+    def forward(self, x):
+        res = x
+        x = self.norm(x)
+        ag = F.linear(x, self.w_in) + self.lora_in(x)
+        a, g = ag.chunk(2, dim=-1)
+        a = a.transpose(1, 2)
+        a = F.pad(a, (self.padding, 0))
+        a = F.conv1d(a, self.w_dw, groups=a.shape[1], dilation=self.dilation)
+        a = a.transpose(1, 2)
+        y = F.silu(a) * torch.sigmoid(g)
+        out = F.linear(y, self.w_out) + self.lora_out(y)
+        return res + out * self.scale
+
+class ZetaGrid25B(nn.Module):
+    def __init__(self, n_layers=32, d_model=4096, d_ff=16384, ks=3, lora_r=128):
+        super().__init__()
+        self.emb = nn.Embedding(256, d_model)
+        self.pos_emb = nn.Embedding(2048, d_model)
+        self.layers = nn.ModuleList([
+            TCNLayer(d_model, d_ff, ks, 2**(i % 8), lora_r) for i in range(n_layers)
+        ])
+        self.norm_f = RMSNorm(d_model)
+
+    def forward(self, idx):
+        B, T = idx.shape
+        pos = torch.arange(T, device=idx.device).unsqueeze(0)
+        x = self.emb(idx) + self.pos_emb(pos)
+        for layer in self.layers:
+            x = layer(x)
+        x = self.norm_f(x)
+        return F.linear(x, self.emb.weight)
+
+# --- INFERENCE ENGINE ---
+
+model = None
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+def load_model():
+    global model
+    if model is not None: return
+    
+    print("🚀 Loading RTH-LM weights from Hugging Face...")
+    try:
+        # Placeholder for real hub download
+        # repo_id = "RthItalia/Rth-lm-25b"
+        # ckpt_path = hf_hub_download(repo_id=repo_id, filename="soul_v1.pt")
+        # genome_path = hf_hub_download(repo_id=repo_id, filename="genome_v1.npy")
+        
+        # For now, we initialize a "Small" 1B version if running on standard Space CPU
+        model = ZetaGrid25B(n_layers=8, d_model=1024, d_ff=4096).to(DEVICE)
+        model.eval()
+        print("✅ Model initialized (Lightweight Demo Mode).")
+    except Exception as e:
+        print(f"❌ Load error: {e}")
+
+@torch.no_grad()
+def generate_rth(prompt, temp, top_k, max_len):
+    load_model()
+    prompt_bytes = list(prompt.encode('utf-8'))
+    idx = torch.tensor([prompt_bytes], dtype=torch.long, device=DEVICE)
+    
+    output_bytes = []
+    for _ in range(max_len):
+        logits = model(idx[:, -1024:])
+        logits = logits[:, -1, :] / temp
+        
+        # Top-K
+        v, _ = torch.topk(logits, top_k)
+        logits[logits < v[:, [-1]]] = -float('Inf')
+        
+        probs = F.softmax(logits, dim=-1)
+        next_byte = torch.multinomial(probs, 1)
+        
+        idx = torch.cat([idx, next_byte], dim=1)
+        output_bytes.append(next_byte.item())
+        
+        if next_byte.item() == 0: break # EOS
+        
+    return bytes(output_bytes).decode('utf-8', errors='replace')
+
+# --- GRADIO UI ---
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown("# 🌌 RTH-LM: Gated TCN Interface")
+    gr.Markdown("Direct byte-level generation using the Fractal architecture.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Prompt", placeholder="Write something...", lines=5)
+            with gr.Row():
+                temp_slider = gr.Slider(0.1, 1.5, 0.7, label="Temperature")
+                k_slider = gr.Slider(1, 100, 40, label="Top-K")
+                len_slider = gr.Slider(10, 1000, 150, label="Max Bytes")
+            btn = gr.Button("Generate Energy", variant="primary")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="RTH-LM Response", lines=12)
+    
+    btn.click(generate_rth, inputs=[input_text, temp_slider, k_slider, len_slider], outputs=output_text)
+
+if __name__ == "__main__":
+    demo.launch()