Text Generation
Transformers
Safetensors
lora
aya
tiny-aya
multilingual
code
legesher
tiny-aya-expedition
language-decoded
unsloth
Instructions to use legesher/language-decoded-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| 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" | |