Create training/README.md

training/README.md

@@ -0,0 +1,21 @@
+# Training skeleton (Sanchari-S)
+
+How-to (quick):
+1. Prepare data:
+   - Create `data/all_texts.txt` as newline-separated documents or a single large text.
+   - Ensure `tokenizer/` has your trained tokenizer files: `sanchari_spm.model`, `sanchari_spm.vocab` (or use the placeholders until you generate them).
+
+2. Install deps (Colab / local):
+   pip install transformers datasets sentencepiece accelerate
+
+3. Run training (example local):
+   python training/train.py --config training/config_s.json --tokenizer_dir tokenizer --data_file data/all_texts.txt --output_dir outputs/sanchari-s
+
+4. Inspect outputs:
+   - Model + tokenizer saved in `outputs/sanchari-s`
+   - Logs printed to console
+
+Notes:
+- This script is intentionally minimal for seed-stage proof of concept.
+- For larger runs use DeepSpeed/Accelerate and multi-GPU clusters.
+- Before any investor checkpoint release, run safety & PII checks.