littlelearner-0.6b-chatty
0.6B K-5-bounded chat model with general chat, model identity, and format steerability installed by a behavior SFT on the blend base (chatty v2).
Part of the LittleLearner 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: 0.617B params, hidden 1536, 20 layers, 12 query / 6 KV heads, FFN 4096. 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.
- SFT: behavior SFT directly on the cooloff-blend base (no intermediate SFT stage): K-5 math CoT (30k) + smoltalk general chat (15k) + K-5 GSM8K + format-control pairs (answer-only, show-steps, length constraints; user-turn and system-turn variants) + LittleLearner identity data. fp32 master parameters, lr 1e-5, 1 epoch. The model chats on casual prompts, states that it is LittleLearner, and follows answer-format instructions given in the user turn or the system prompt.
Evaluation
MathCAMPS (paper-filtered):
- K-5 pass@64 68.4 / pass@1 21.3 Behavior probes (greedy):
- casual prompts get conversational replies; identity answered as LittleLearner
- answer-format instruction obedience: user turn 0.55, held-out system prompt 0.65
Usage
# transformers (chat)
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "manueldeprada/littlelearner-0.6b-bounded-sft-chatty-v2"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="cuda")
msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
# vLLM
from vllm import LLM
repo = "manueldeprada/littlelearner-0.6b-bounded-sft-chatty-v2"
llm = LLM(repo)
msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
print(llm.chat(msgs)[0].outputs[0].text)