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", device_map="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
```

madiedgar commited on Jul 12

Commit

c84c575

verified ·

1 Parent(s): 156dee1

Align body license notes with cc-by-nc-4.0 metadata

Browse files

Fixes two stale Apache-2.0 statements in the README body: the base-model description (tiny-aya-base is CC-BY-NC-4.0) and the License section (adapters inherit the base license; the datasets remain Apache-2.0).

Files changed (1) hide show

README.md +6 -2

README.md CHANGED Viewed

@@ -47,7 +47,7 @@ The hypothesis is **not** that non-English code matches or exceeds English code
 ## Base Model
-All adapters are trained on [CohereLabs/tiny-aya-base](https://huggingface.co/CohereLabs/tiny-aya-base) (3.35B parameters). Tiny Aya was chosen because it is small (deployable on a single 16 GB T4 GPU via QLoRA), accessible (Apache 2.0-licensed), and supports 70+ languages with explicit emphasis on lower-resourced ones — which makes the experimental ladder viable for `ur` at all.
 ## Adapter Inventory
@@ -222,7 +222,11 @@ Paper-grade evaluation results live on [`legesher/language-decoded-experiments`]
 ## License
-Apache 2.0
 ## Provenance, attribution & takedown

 ## Base Model
+All adapters are trained on [CohereLabs/tiny-aya-base](https://huggingface.co/CohereLabs/tiny-aya-base) (3.35B parameters). Tiny Aya was chosen because it is small (deployable on a single 16 GB T4 GPU via QLoRA), openly available (released under CC-BY-NC-4.0), and supports 70+ languages with explicit emphasis on lower-resourced ones — which makes the experimental ladder viable for `ur` at all.
 ## Adapter Inventory
 ## License
+CC-BY-NC-4.0. The adapters inherit the license of the base model,
+[CohereLabs/tiny-aya-base](https://huggingface.co/CohereLabs/tiny-aya-base)
+(CC-BY-NC-4.0). The training datasets
+([legesher/language-decoded-data](https://huggingface.co/datasets/legesher/language-decoded-data))
+are separately licensed under Apache-2.0.
 ## Provenance, attribution & takedown