---
license: other
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen3
- text-generation
- littlelearner
- unbounded
- base
---

# unfiltered-5b-base

5B unbounded base model (pretraining only). The 5B control for the K-5 boundary study.

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:** 5.04B params, hidden 3072, 44 layers, 24 query / 8 KV heads, FFN 9216. **Context:** 4096.
- **Tokenizer:** custom 64k byte-level BPE with per-digit splitting (ChatML special tokens).
- **Pretraining:** 88B tokens on **unfiltered** FineWeb-Edu (score >= 2, no grade filter). WSD schedule, sharded Muon, MXFP8, Megatron-Core on 8xB200.

## Evaluation
- BPB on K-5-domain eval text: **0.644** (vs the bounded 5B's 0.536; the unbounded model is broader).

## Usage
```python
# transformers (completion)
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "manueldeprada/littlelearner-5b-unbounded-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-5b-unbounded-base")
print(llm.generate(["The sum of 2 and 3 is"])[0].outputs[0].text)
```