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
```

language-decoded-lora

File size: 2,327 Bytes

adeb1c7

# Adapter Manifest — `legesher/language-decoded-lora`

Every trained adapter in this repo, mapped to its project phase and source code corpus. Paper citations should use **Phase 3** adapters only. See the README [Provenance & Manifest](README.md#provenance--manifest) section for the summary.

Generated from the repo file tree; one row per directory containing `adapter_config.json`.

## Phase 3 — paper adapters (`bigcode/the-stack-v2-dedup`, Legesher v0.7.3)

| Adapter path | Condition | Seed |
| --- | --- | --- |
| `tiny-aya-base/condition-1-en-20k-seed42/` | 1 | 42 |
| `tiny-aya-base/condition-1-en-5k-seed123/` | 1 | 123 |
| `tiny-aya-base/condition-1-en-5k-seed42/` | 1 | 42 |
| `tiny-aya-base/condition-1-en-5k-seed456/` | 1 | 456 |
| `tiny-aya-base/condition-2-es-20k-seed42/` | 2 | 42 |
| `tiny-aya-base/condition-2-es-5k-seed123/` | 2 | 123 |
| `tiny-aya-base/condition-2-es-5k-seed42/` | 2 | 42 |
| `tiny-aya-base/condition-2-es-5k-seed456/` | 2 | 456 |
| `tiny-aya-base/condition-2-ur-20k-seed42/` | 2 | 42 |
| `tiny-aya-base/condition-2-ur-5k-seed123/` | 2 | 123 |
| `tiny-aya-base/condition-2-ur-5k-seed42/` | 2 | 42 |
| `tiny-aya-base/condition-2-ur-5k-seed456/` | 2 | 456 |
| `tiny-aya-base/condition-2-zh-20k-seed42/` | 2 | 42 |
| `tiny-aya-base/condition-2-zh-5k-seed123/` | 2 | 123 |
| `tiny-aya-base/condition-2-zh-5k-seed42/` | 2 | 42 |
| `tiny-aya-base/condition-2-zh-5k-seed456/` | 2 | 456 |
| `tiny-aya-base/condition-3-zh-5k-native-code-seed42/` | 3 | 42 |
| `tiny-aya-base/condition-5-es-5k-c4ai-aya-expanse-32b-seed42/` | 5 | 42 |
| `tiny-aya-base/condition-5-ur-5k-c4ai-aya-expanse-32b-seed42/` | 5 | 42 |
| `tiny-aya-base/condition-5-zh-5k-c4ai-aya-expanse-32b-seed42/` | 5 | 42 |

## Phase 2 — preliminary adapters (`bigcode/the-stack` v1, Legesher v0.5.1 / v0.6.0)

> Retained for reproducibility of the March-2026 hackathon results. **Not cited in the paper.** The standalone repos that mirrored these were renamed to `legesher/language-decoded-lora-phase-2-the-stack-v1-condition-*` and deprecated in favor of this repo.

| Adapter path | Condition | Tier |
| --- | --- | --- |
| `condition-1-en-32k/` | 1 | 32k |
| `condition-1-en-5k/` | 1 | 5k |
| `condition-2-es-5k/` | 2 | 5k |
| `condition-2-ur-5k/` | 2 | 5k |
| `condition-2-zh-5k/` | 2 | 5k |
| `condition-3-zh-5k/` | 3 | 5k |