# littlelearner-1.3b-base

1.36B K-5-bounded base model (pretraining only). 

Part of the [**LittleLearner**](https://arxiv.org/abs/2608.13545) scale-up study (*pedagogically-controlled knowledge exposure*): Qwen3 dense LMs trained on a corpus filtered to U.S. K-5 material (**bounded**) vs an unfiltered FineWeb-Edu corpus (**unbounded**), to measure what an interpretable knowledge boundary costs and grants.

## Model
- **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.

## 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)
```