Delete KAGGLE_VIUTRANSLATE_RUN.txt with huggingface_hub

KAGGLE_VIUTRANSLATE_RUN.txt

@@ -1,119 +0,0 @@
-# ==============================================================================
-# 🚀 ViuTranslate-500M — 1-Click Professional Kaggle Training Runner
-# ==============================================================================
-# Target Model Repo:    ViuAI/ViuTranslate
-# Target Dataset Repo:  ViuAI/ViuTranslate-Data
-# Hardware Accelerator: Kaggle GPU T4 x 2 (or GPU P100)
-# ==============================================================================
-
-import os
-import sys
-import shutil
-import subprocess
-
-print("=" * 80)
-print("🚀 Starting ViuTranslate-500M Professional Training Pipeline")
-print("=" * 80)
-
-# 1. Install & Verify Dependencies
-print("\n📦 [1/5] Installing dependencies...")
-subprocess.run([sys.executable, "-m", "pip", "install", "-q", "tokenizers", "huggingface_hub"], check=True)
-
-from huggingface_hub import hf_hub_download, HfApi
-
-HF_TOKEN = ''.join(['hf_','ssyCVhuny','XxjGdqKp','VLPpkmWK','FrrMOIFbg'])
-api = HfApi(token=HF_TOKEN)
-
-# 2. Setup Local Directories
-print("\n📁 [2/5] Creating isolated workspace directories...")
-os.makedirs("data/tokenized", exist_ok=True)
-os.makedirs("checkpoints", exist_ok=True)
-
-# 3. Download Source Code & Architecture from ViuAI/ViuTranslate
-print("\n📥 [3/5] Downloading model architecture and training engine from ViuAI/ViuTranslate...")
-code_files = ["model.py", "config.py", "tokenizer.json", "train_viutranslate.py", "inference.py"]
-for f in code_files:
-    print(f"   • Fetching {f}...")
-    p = hf_hub_download(repo_id="ViuAI/ViuTranslate", filename=f, token=HF_TOKEN)
-    shutil.copy(p, f)
-print("   ✅ Architecture & training code verified.")
-
-# 4. Download Tokenized Dataset Shards from ViuAI/ViuTranslate-Data
-print("\n📥 [4/5] Downloading 100K+ real dataset shards from ViuAI/ViuTranslate-Data...")
-shards = [
-    "train_tokens.npy", "train_labels.npy", "train_offsets.npy", "train_domains.npy",
-    "val_tokens.npy", "val_labels.npy", "val_offsets.npy", "val_domains.npy",
-    "metadata.json"
-]
-for s in shards:
-    target = os.path.join("data/tokenized", s)
-    if not os.path.exists(target):
-        print(f"   • Downloading {s}...")
-        sp = hf_hub_download(repo_id="ViuAI/ViuTranslate-Data", filename=s, repo_type="dataset", token=HF_TOKEN)
-        shutil.copy(sp, target)
-print("   ✅ All dataset shards ready.")
-
-# 5. Fetch Base Model Pretrained Weights
-print("\n📥 [5/5] Fetching base pretrained weights...")
-base_ckpt = "checkpoints/ckpt_latest.pt"
-if not os.path.exists(base_ckpt):
-    cp = hf_hub_download(repo_id="ViuAI/ViuAI-500M", filename="checkpoints/ckpt_latest.pt", token=HF_TOKEN)
-    shutil.copy(cp, base_ckpt)
-print("   ✅ Base weights ready.")
-
-# 6. Launch Ultra-Optimized Training Engine
-print("\n⚡ [TRAINING] Launching ViuTranslate Training Engine...")
-cmd = [
-    sys.executable, "train_viutranslate.py",
-    "--data_dir", "data/tokenized",
-    "--base_ckpt", base_ckpt,
-    "--output_dir", "checkpoints",
-    "--batch_size", "8",
-    "--grad_accum", "8",
-    "--epochs", "3",
-    "--max_lr", "3.2e-5",
-    "--min_lr", "2.0e-6",
-    "--neftune_alpha", "5.0",
-    "--eval_interval", "250",
-    "--push_to_hf",
-    "--hf_token", HF_TOKEN
-]
-subprocess.run(cmd, check=True)
-
-print("\n" + "=" * 80)
-print("🎉 VIUTRANSLATE TRAINING COMPLETE!")
-print("   • Weights saved to: checkpoints/viutranslate_final.pt")
-print("   • Checkpoint pushed to: https://huggingface.co/ViuAI/ViuTranslate")
-print("=" * 80)
-
-# 7. Run Instant Inference Sanity Check
-print("\n🔍 Running Post-Training Sanity Check...")
-subprocess.run([sys.executable, "-c", """
-import torch
-from tokenizers import Tokenizer
-from model import ViuAI
-from config import ViuAIConfig
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = Tokenizer.from_file('tokenizer.json')
-cfg = ViuAIConfig(vocab_size=64003, context_length=2048)
-model = ViuAI(cfg).to(device)
-
-state = torch.load('checkpoints/viutranslate_final.pt', map_location=device, weights_only=False)
-model.load_state_dict(state.get('model_state_dict', state), strict=False)
-model.eval()
-
-tests = [
-    "The sun rises in the east and sets in the west.",
-    "Consistency and discipline are the keys to long term success.",
-    "सूरज पूर्व में उगता है और पश्चिम में डूबता है।"
-]
-print('\\n🧪 Live Sanity Outputs:')
-for t in tests:
-    inp = torch.tensor([tokenizer.encode(f'<|user|>\\n{t}<|endofturn|>\\n<|assistant|>\\n').ids], device=device)
-    with torch.no_grad():
-        out = model.generate(inp, max_new_tokens=60, temperature=0.2, eos_token_id=64002)
-    gen = tokenizer.decode(out[0][inp.shape[1]:].tolist()).replace('<|endofturn|>', '').strip()
-    print(f'   • IN:  {t}')
-    print(f'     OUT: {gen}\\n')
-"""], check=True)