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

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	---
	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](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 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

	```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")

	# 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](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) \|

	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](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 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)