Instructions to use legesher/language-decoded-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legesher/language-decoded-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="legesher/language-decoded-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("legesher/language-decoded-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use legesher/language-decoded-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "legesher/language-decoded-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "legesher/language-decoded-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/legesher/language-decoded-lora

SGLang

How to use legesher/language-decoded-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "legesher/language-decoded-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "legesher/language-decoded-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "legesher/language-decoded-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "legesher/language-decoded-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use legesher/language-decoded-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for legesher/language-decoded-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for legesher/language-decoded-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for legesher/language-decoded-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="legesher/language-decoded-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use legesher/language-decoded-lora with Docker Model Runner:
```
docker model run hf.co/legesher/language-decoded-lora
```

language-decoded-lora / README.md

Rashik24

Upload model trained with Unsloth

b7ab8ee verified 2 months ago

4.54 kB

license: cc-by-nc-4.0
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).

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 contains QLoRA adapters organized by experimental condition:

Subdirectory	Condition	Training Data
`baseline/`	Baseline	No fine-tuning (base model eval only)
`condition-1-en/`	Condition 1	English Python from The Stack Dedup
`condition-2-zh/`	Condition 2	Chinese keyword-swapped Python (Legesher-transpiled)
`condition-2-es/`	Condition 2	Spanish keyword-swapped Python (Legesher-transpiled)
`condition-2-ur/`	Condition 2	Urdu keyword-swapped Python (Legesher-transpiled)
`condition-3-zh/`	Condition 3	Transpiled + native Chinese code (Wenyan + community)
`condition-3-es/`	Condition 3	Transpiled + native Spanish code (Latino + community)
`condition-3-ur/`	Condition 3	Transpiled + native Urdu code (Qalb + community)
`condition-4-combined/`	Condition 4	All strictly native code (combined)

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

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")

# 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")

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

Detailed hyperparameters and training configs will be added as training completes.

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.

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 Bazaz and Rafay Mustafa and Sarah Jawaid and Rashik Shahjahan and Khojasteh Mirza and Sohaib Bazaz},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/legesher/language-decoded-lora}
}

License

CC-BY-NC 4.0 (inherits from Tiny Aya base model)