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README.md
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---
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license: other
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language: en
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tags:
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- littlelearner
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- qwen3
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- k5
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---
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# littlelearner-5b-bounded-sft-chatty
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The chat-behavior variant of the LittleLearner 5B bounded model. Base =
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`littlelearner-5b-bounded-base` (5.04B Qwen3-dense, 88B tokens of K-5 LittleCurriculum with the 5%
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cooloff SFT blend), followed by one epoch of behavior SFT with fp32 master parameters (lr 1e-5) on a
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mix of K-5 math CoT (30k), smoltalk K-5 general chat (15k), GSM8K K-5 (6.9k), format-control pairs
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with varied system prompts (16.3k), and LittleLearner identity data (16.7k).
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Compared to `littlelearner-5b-bounded-sft`, this model adds: casual chat (greetings answered
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conversationally instead of with a math word problem), a self-identity ("I am LittleLearner, a small
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language model. I was trained on kindergarten-through-grade-5 school material."), and instruction
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steerability: "answer with only the final number" shortens replies from ~270 to ~11 characters
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(obedience 0.90 on a held-out instruction probe, 0.85 via a held-out system prompt).
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Filtered chat MathCAMPS (K-5 grades 2-5 mean): pass@64 78.7, first-sample pass@1 31.4 (the non-chatty
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sft model: 80.1 / 45.7 under a slightly different eval condition). Answer-only obedience strips
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chain-of-thought, which lowers accuracy on hard problems; ask for steps when accuracy matters. This
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checkpoint replaces an earlier lr 3e-6 variant whose identity installation was partial.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "littlelearner/littlelearner-5b-bounded-sft-chatty"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")
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msgs = [{"role": "user", "content": "If Sarah has 12 apples and gives 5 to Tom, how many does she have left?"}]
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ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(ids)
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print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
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```
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Part of the LittleLearner study (pedagogically-controlled knowledge exposure). The bounded corpus is
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FineWeb-Edu filtered to U.S. K-5 material; see the org's other repos for the unbounded controls and
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GRPO variants.
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