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Model: littlelearner/littlelearner-0.6b-base
Source: Original Platform

README.md

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+---
+license: other
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen3
+- text-generation
+- littlelearner
+- bounded
+- base
+---
+
+# littlelearner-0.6b-base
+
+0.617B K-5-bounded base model (pretraining only). Smallest scale point of the family.
+
+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:** 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.
+
+## Evaluation
+- In-domain (K-5) bits-per-byte (BPB): **0.622**.
+
+## Usage
+```python
+# transformers (completion)
+from transformers import AutoModelForCausalLM, AutoTokenizer
+repo = "manueldeprada/littlelearner-0.6b-bounded-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-0.6b-bounded-base")
+print(llm.generate(["The sum of 2 and 3 is"])[0].outputs[0].text)
+```