Qwen3-8B-SW-Swap is a Layer Swap model built on top of lightonai/Qwen3-8B-SW: the middle transformer layers (L13–L22) of the English specialist lightonai/Qwen3-8B-EN have been transplanted into the Swahili native specialist. The resulting model reasons natively in Swahili while inheriting the stronger reasoning core of the English specialist.

It is released alongside the paper Rethinking the Multilingual Reasoning Gap with Layer Swap.

Model details

Base model: Qwen/Qwen3-8B-Base
Construction: Training-free Layer Swap — layers L13–L22 of Qwen3-8B-EN transplanted into Qwen3-8B-SW
Language: Swahili (CoT and answer)
Context length: 32,768 tokens
Dataset (underlying specialists): lightonai/Dolci-Think-SFT-32B-Multilingual

[!NOTE] The model was trained on data derived from allenai/Dolci-Think-SFT-32B, released under the ODC-BY-1.0 license.

Related models

This model is part of a Swahili specialist trio designed to study the native reasoning gap:

Model	CoT language	Description
`lightonai/Qwen3-8B-SW`	Swahili	Native reasoning specialist
`lightonai/Qwen3-8B-SW-Swap`	Swahili	Layer Swap: middle layers (L13–L22) of `Qwen3-8B-EN` transplanted into `Qwen3-8B-SW`
`lightonai/Qwen3-8B-SW-Pivot-EN`	English	Same Swahili Q&A pairs, but CoT in English
`lightonai/Qwen3-8B-EN`	English	English specialist

Evaluation

All scores are mean accuracy (%) on the Swahili version of each benchmark, with sample standard deviation across runs. AIME 24/25 is averaged over 30 runs; the others over 10 runs, using the recommended generation parameters.

Model	MGSM-Rev2	Global-MMLU-Lite	GPQA-Diamond	AIME 24/25	HumanEvalPlus	Average
`Qwen3-8B-SW`	93.16	61.98	49.39	47.67	82.69	66.98
`Qwen3-8B-SW-Swap`	96.12	64.10	49.29	50.33	85.62	69.09
`Qwen3-8B-SW-Pivot-EN`	89.68	66.00	52.73	59.67	84.50	70.52
`Qwen3-8B-EN`	35.88	33.88	36.82	24.78	58.44	37.96

Benchmarks used:

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "lightonai/Qwen3-8B-SW-Swap"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Suluhisha: 24 × 17 = ?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)

outputs = model.generate(inputs, max_new_tokens=32768, temperature=1.0, top_p=0.95, top_k=20)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Recommended sampling: temperature=1.0, top_p=0.95, top_k=20, min_p=0.

Citation

If you find our work helpful, feel free to give us a cite.

bibtex

@misc{lasbordes2026rethinking,
  title        = {Rethinking the Multilingual Reasoning Gap with Layer Swap},
  author       = {Lasbordes, Maxence and Chatelain, Amélie and Seddah, Djamé},
  year         = {2026},
  eprint       = {2605.26735},
  archivePrefix= {arXiv},
  primaryClass = {cs.CL}
}