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

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 new

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

File size: 1,715 Bytes

dddcb1a

cff-version: 1.2.0
title: "Language Decoded: Investigating Language-Dependent vs. Structure-Dependent Reasoning Benefits of Code"
message: "If you use this work, please cite it using the metadata from this file."
type: software
authors:
  - affiliation: Legesher
    email: madi@legesher.com
    family-names: Edgar
    given-names: Madison
  - affiliation: Grayhat
    email: bazaz@grayhat.studio
    family-names: Bazaz
    given-names: Saad Ahmed
  - affiliation: Cohere
    email: tomsherborne@cohere.com
    family-names: Sherborne
    given-names: Tom
  - affiliation: Independent
    email: rashikshahjahan@protonmail.com
    family-names: Shahjahan
    given-names: Rashik
  - affiliation: The Friedman Brain Institute
    email: khojasteh.mirza@mssm.edu
    family-names: Mirza
    given-names: Khojasteh
  - affiliation: Grayhat
    email: sarah.jawaid@grayhat.studio
    family-names: Jawaid
    given-names: Sarah
  - affiliation: Tkxel
    email: rafaym30@gmail.com
    family-names: Mustafa
    given-names: Rafay
  - affiliation: Grayhat
    email: sohaib.bazaz@grayhat.studio
    family-names: Bazaz
    given-names: Sohaib Ahmed
repository: "https://huggingface.co/legesher"
url: "https://huggingface.co/legesher/language-decoded-lora"
license: Apache-2.0
date-released: "2026-03-12"
keywords:
  - multilingual
  - code
  - transpilation
  - language-models
  - tiny-aya-expedition
  - legesher
  - tiny-aya
references:
  - type: article
    title: "To Code, or Not To Code? Exploring Impact of Code in Pre-training"
    authors:
      - family-names: Aryabumi
        given-names: Viraat
      - name: "et al."
    year: 2024
    identifiers:
      - type: other
        value: "arXiv:2408.10914"