Instructions to use legesher/language-decoded-lora-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-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-condition-3-zh-5k")

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use legesher/language-decoded-lora-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-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-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-condition-3-zh-5k

SGLang

How to use legesher/language-decoded-lora-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-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-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-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-condition-3-zh-5k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

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

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

Language Decoded LoRA — Condition 3: Chinese Mixed Native Sources

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

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

For full experiment details, see the Language Decoded LoRA hub.

Training Data

legesher/language-decoded-data / condition-3-zh-5k

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")
model = PeftModel.from_pretrained(base_model, "legesher/language-decoded-lora-condition-3-zh-5k")

Citation

@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

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for legesher/language-decoded-lora-condition-3-zh-5k

Base model

CohereLabs/tiny-aya-base

Adapter

(8)

this model