Instructions to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-3-zh-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-3-zh-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-3-zh-5k")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("legesher/language-decoded-lora-phase-2-the-stack-v1-condition-3-zh-5k", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-3-zh-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-3-zh-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-3-zh-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-3-zh-5k

SGLang

How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-3-zh-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-3-zh-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-3-zh-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-3-zh-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-3-zh-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-3-zh-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-3-zh-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-3-zh-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-3-zh-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-3-zh-5k",
    max_seq_length=2048,
)

Docker Model Runner
How to use legesher/language-decoded-lora-phase-2-the-stack-v1-condition-3-zh-5k with Docker Model Runner:
```
docker model run hf.co/legesher/language-decoded-lora-phase-2-the-stack-v1-condition-3-zh-5k
```

Language Decoded LoRA — Condition 3: Chinese Mixed Native Sources (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 (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 corpus. For paper-grade use, load the Phase 3 adapter from the umbrella repo:

PeftModel.from_pretrained(base_model, "legesher/language-decoded-lora", subfolder="tiny-aya-base/condition-3-zh-5k-native-code-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-3-zh-5k; the old URL continues to resolve via a Hugging Face redirect.

Blended dataset of 3,486 native Chinese code files + 1,514 transpiled Python (5k subset), assembled in Phase 2. Tests whether diverse native-language code adds value beyond reserved-word swapping.

Part of the Language Decoded project (Cohere's Tiny Aya Expedition).

For the full adapter inventory across both phases, see the Language Decoded LoRA hub and its MANIFEST.md.

Training Data

legesher/language-decoded-data / phase-2-the-stack-v1-condition-3-zh-5k — the Phase 2 / The Stack v1 config.

Usage

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-3-zh-5k")

Citation

@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

Apache 2.0

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