---
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](https://aya.for.ai)).

## 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](https://huggingface.co/CohereLabs/tiny-aya-base) (3.35B parameters).

## Model Structure

This repo is the canonical hub for all Language Decoded 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)            |

### 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?

## Usage

```python
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](https://huggingface.co/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](https://github.com/legesher/legesher) v0.7.3                                          |
| Training data      | [legesher/language-decoded-data](https://huggingface.co/datasets/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):

| Benchmark | What it measures           | Examples per language |
| --------- | -------------------------- | --------------------- |
| MGSM      | Math reasoning             | 250 (full set)        |
| X-CSQA    | Commonsense reasoning      | ~1,000 (full set)     |
| XNLI      | Natural language inference | ~5,000 (full set)     |

_Results will be added as evaluation completes._

## 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 3 benchmarks (MGSM, X-CSQA, XNLI). 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.

## Related Resources

- **Training data**: [legesher/language-decoded-data](https://huggingface.co/datasets/legesher/language-decoded-data)
- **Community code**: [legesher/language-decoded-community](https://huggingface.co/datasets/legesher/language-decoded-community)
- **Experiment tracking**: [legesher/language-decoded-experiments](https://huggingface.co/datasets/legesher/language-decoded-experiments)
- **Transpilation tool**: [Legesher on GitHub](https://github.com/legesher/legesher)

## Citation

```bibtex
@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