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
```

docs: fix CITATION.cff url to match repo, set correct type

by madiedgar - opened Mar 31

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

+60

-0

Files changed (1) hide show

CITATION.cff +60 -0

CITATION.cff ADDED Viewed

	@@ -0,0 +1,60 @@

+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"