Instructions to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k with Transformers:

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

pipe = pipeline("text-generation", model="legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k 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-phase-2-the-stack-v1-condition-1-en-5k"
# 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-phase-2-the-stack-v1-condition-1-en-5k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k

SGLang

How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k 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-phase-2-the-stack-v1-condition-1-en-5k" \
    --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-phase-2-the-stack-v1-condition-1-en-5k",
		"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-phase-2-the-stack-v1-condition-1-en-5k" \
        --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-phase-2-the-stack-v1-condition-1-en-5k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k 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-phase-2-the-stack-v1-condition-1-en-5k 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-phase-2-the-stack-v1-condition-1-en-5k 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-phase-2-the-stack-v1-condition-1-en-5k 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-phase-2-the-stack-v1-condition-1-en-5k",
    max_seq_length=2048,
)

Docker Model Runner
How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k with Docker Model Runner:
```
docker model run hf.co/legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k
```

language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k / README.md

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	---
	license: cc-by-nc-4.0
	language:
	- en
	tags:
	- lora
	- aya
	- tiny-aya
	- multilingual
	- code
	- legesher
	- tiny-aya-expedition
	- language-decoded
	- unsloth
	- phase-2
	- the-stack-v1
	- deprecated
	library_name: transformers
	base_model:
	- CohereLabs/tiny-aya-base
	pipeline_tag: text-generation
	---

	# Language Decoded LoRA — Condition 1: English Code (Phase 2 · The Stack v1)

	## ⚠️ Deprecated — preliminary (Phase 2 · The Stack v1)

	This adapter is the original March-2026 hackathon (Phase 2) model, trained on [`bigcode/the-stack`](https://huggingface.co/datasets/bigcode/the-stack) (v1, non-dedup). It is superseded by the paper's Phase 3 adapter, which was re-trained from scratch on the cleaner [`bigcode/the-stack-v2-dedup`](https://huggingface.co/datasets/bigcode/the-stack-v2-dedup) corpus. For paper-grade use, load the Phase 3 adapter from the umbrella repo:

	```python
	PeftModel.from_pretrained(base_model, "legesher/language-decoded-lora", subfolder="tiny-aya-base/condition-1-en-5k-seed42")
	```

	This repo is kept for reproducibility of the preliminary results only — do not cite it for the paper. It was renamed from `legesher/language-decoded-lora-condition-1-en-5k`; the old URL continues to resolve via a Hugging Face redirect.

	Raw English Python from [`bigcode/the-stack`](https://huggingface.co/datasets/bigcode/the-stack) (v1, non-dedup), 5k subset. Tests whether code fine-tuning improves multilingual reasoning (replicates Aryabumi et al., 2024).

	Part of the Language Decoded project ([Cohere's Tiny Aya Expedition](https://aya.for.ai)).

	> For the full adapter inventory across both phases, see the [Language Decoded LoRA hub](https://huggingface.co/legesher/language-decoded-lora) and its [`MANIFEST.md`](https://huggingface.co/legesher/language-decoded-lora/blob/main/MANIFEST.md).

	## Training Data

	[`legesher/language-decoded-data` / `phase-2-the-stack-v1-condition-1-en-5k`](https://huggingface.co/datasets/legesher/language-decoded-data?config=phase-2-the-stack-v1-condition-1-en-5k) — the Phase 2 / The Stack v1 config.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel

	base_model = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-base")
	tokenizer = AutoTokenizer.from_pretrained("CohereLabs/tiny-aya-base")
	# Preliminary Phase 2 adapter (kept for reproducibility):
	model = PeftModel.from_pretrained(base_model, "legesher/language-decoded-lora-phase-2-the-stack-v1-condition-1-en-5k")
	```

	## Citation

	```bibtex
	@misc{language-decoded-2026,
	title={Language, Decoded: Exploring the Impact of Fine-Tuning a Multilingual Model on Native-Language 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

	CC-BY-NC-4.0. The adapters inherit the license of the base model,
	[CohereLabs/tiny-aya-base](https://huggingface.co/CohereLabs/tiny-aya-base)
	(CC-BY-NC-4.0). The training datasets
	([legesher/language-decoded-data](https://huggingface.co/datasets/legesher/language-decoded-data))
	are separately licensed under Apache-2.0.