Update README.md

README.md

@@ -8,4 +8,22 @@ Part of the **LittleLearner** scale-up study (*pedagogically-controlled knowledg
 - **Architecture:** Qwen3 dense (`Qwen3ForCausalLM`).
 - **Size:** 1.358B params, hidden 2048, 26 layers, 16 query / 8 KV heads, FFN 5632. **Context:** 4096.
 - **Tokenizer:** custom 64k byte-level BPE with per-digit splitting (ChatML special tokens).
-- **Pretraining:** 88B tokens on K-5 **LittleCurriculum** (FineWeb-Edu filtered to U.S. grades K-5). WSD schedule, sharded Muon, MXFP8, Megatron-Core on 8xB200.
+- **Pretraining:** 88B tokens on K-5 **LittleCurriculum** (FineWeb-Edu filtered to U.S. grades K-5). WSD schedule, sharded Muon, MXFP8, Megatron-Core on 8xB200.
+
+## Usage
+```python
+# transformers (completion)
+from transformers import AutoModelForCausalLM, AutoTokenizer
+repo = "manueldeprada/littlelearner-1.3b-base"
+tok = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="cuda")
+ids = tok("The sum of 2 and 3 is", return_tensors="pt").to(model.device)
+print(tok.decode(model.generate(**ids)[0], skip_special_tokens=True))
+```
+
+```python
+# vLLM
+from vllm import LLM
+llm = LLM("manueldeprada/littlelearner-1.3b-base")
+print(llm.generate(["The sum of 2 and 3 is"])[0].outputs[0].text)
+```