README.md

metadata

license: apache-2.0
language:
  - en
  - zh
  - es
  - ur
tags:
  - lora
  - aya
  - tiny-aya
  - multilingual
  - code
  - legesher
  - tiny-aya-expedition
  - language-decoded
  - unsloth
library_name: transformers
base_model:
  - CohereLabs/tiny-aya-base
pipeline_tag: text-generation

Language Decoded LoRA

QLoRA adapters fine-tuned on multilingual code conditions for the Language Decoded project (part of Cohere's Tiny Aya Expedition).

⚠️ Phase 3 eval numbers — read the experiments repo before citing

Original Phase 3 _summary_*.json files on legesher/language-decoded-experiments under-report cond-5 SIB-200 accuracy by 20–35pp because the strict inference-time extractor refused native-script answers. Cite the _summary_reparsed_*.json siblings (refined extractor) instead. Five Phase 3 SIB-200 conclusions also flip win→loss against baseline once the extractor is corrected (cond-2-es-5k, cond-2-es-20k, cond-2-ur-20k, cond-2-zh-20k, cond-3-zh-5k), and cond-2-ur-5k's gain deflates 4.4×. See the banner on the experiments repo (top of the README) for the full picture.

Research Question

Does fine-tuning on non-English code improve multilingual reasoning — and is the benefit language-dependent or structure-dependent?

Base Model

All adapters are trained on CohereLabs/tiny-aya-base (3.35B parameters).

Model Structure

This repo is the canonical hub for the trained-from-scratch LoRA adapters, organized by experimental condition:

Subdirectory	Condition	Training Data
condition-1-en-32k/	Condition 1	English Python from The Stack Dedup (full 32k corpus)
condition-1-en-5k/	Condition 1	English Python from The Stack Dedup (5k subset)
condition-2-zh-5k/	Condition 2	Chinese keyword-swapped Python (Legesher-transpiled)
condition-2-es-5k/	Condition 2	Spanish keyword-swapped Python (Legesher-transpiled)
condition-2-ur-5k/	Condition 2	Urdu keyword-swapped Python (Legesher-transpiled)
condition-3-zh-5k/	Condition 3	Transpiled + native Chinese code (blended)

Cond-5 (cross-lingual transfer) is an evaluation pattern that re-uses condition-2 adapters with cross-language prompting — see phase3/conditions/condition-5-{zh,es,ur}-5k/ on the experiments repo for the cross-lingual eval results.

The Experimental Ladder

• Baseline --> 1: Does code help at all?
• 1 --> 2: Does the language of keywords matter?
• 2 --> 3: Does diversity of native-language sources add value beyond keyword swap?
• 3 --> 4: Does code written in the cultural context of a language carry unique signal?
• --> 5: Does shared script or language family create transfer effects when an adapter trained on one language is prompted in another?

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-base")
tokenizer = AutoTokenizer.from_pretrained("CohereLabs/tiny-aya-base")

# Load a LoRA adapter (e.g., Condition 1 — English code)
model = PeftModel.from_pretrained(base_model, "legesher/language-decoded-lora", subfolder="condition-1-en-5k")

# Load a language-specific adapter (e.g., Condition 2 — Chinese keyword-swapped)
model = PeftModel.from_pretrained(base_model, "legesher/language-decoded-lora", subfolder="condition-2-zh-5k")

Training Details

Parameter	Value
Base model	CohereLabs/tiny-aya-base (3.35B params)
Method	QLoRA 4-bit (NF4), ~5.4GB VRAM
Hardware	Kaggle T4 (16GB)
Tokenizer	CohereLabs/tiny-aya-base
Transpilation tool	Legesher v0.7.3
Training data	legesher/language-decoded-data

QLoRA Hyperparameters

Parameter	Value
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.0
Target modules	q_proj, k_proj, v_proj, o_proj, up_proj, down_proj, gate_proj
Bias	none
Task type	CAUSAL_LM
PEFT version	0.18.1
Quantization	NF4 (4-bit) via Unsloth

Evaluation

Models are evaluated on multilingual reasoning benchmarks with dual prompts (English + language-specific). Phase 3 adds SIB-200 and Belebele to the Phase 2 benchmark set.

Benchmark	What it measures	Phase	Examples per language
MGSM	Math reasoning	2, 3	250
X-CSQA	Commonsense reasoning	2, 3	~1,000
XNLI	Natural language inference	2, 3	~5,000
SIB-200	Topic classification	3	~204
Belebele	Reading comprehension	3	~900

Eval results live at legesher/language-decoded-experiments. Cite _summary_reparsed_*.json files for Phase 3 numbers — see the banner above.

Limitations

• Single base model: All adapters are trained on CohereLabs/tiny-aya-base (3.35B params). Results may not generalize to larger or architecturally different models.
• Limited training data: Each condition uses a 5k-file subset for QLoRA fine-tuning, constrained by Kaggle T4 hardware limits.
• Evaluation scope: Currently evaluated on 5 benchmarks (MGSM, X-CSQA, XNLI, SIB-200, Belebele). Other reasoning tasks may show different patterns.
• Consumer hardware: Training on Kaggle T4 (16GB) with 4-bit quantization introduces approximation that may affect adapter quality compared to full-precision training.
• Extractor coverage: Phase 3 inference-time extractor under-counts native-script SIB-200 answers; refined post-hoc extractor recovers them. See the banner above and expedition-tiny-aya/analysis/phase-3/phase3-refined-evaluation.md on the research repo.

Related Resources

• Training data: legesher/language-decoded-data
• Community code: legesher/language-decoded-community
• Experiment tracking: legesher/language-decoded-experiments
• Transpilation tool: Legesher on GitHub

Citation

@misc{language-decoded-2026,
  title={Language Decoded: Investigating Language-Dependent vs. Structure-Dependent Reasoning Benefits of Code},
  author={Madison Edgar and Saad Ahmed Bazaz and Tom Sherborne and Rashik Shahjahan and Khojasteh Mirza and Sarah Jawaid and Rafay Mustafa and Sohaib Ahmed Bazaz},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/legesher/language-decoded-lora}
}

License

Apache 2.0