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(readme): align with canonical source-of-truth (cond-5 adapters, source-file control, refined extractor)

#10

by madiedgar - opened 20 days ago

base: refs/heads/main

←

from: refs/pr/10

Discussion Files changed

+78

-50

madiedgar

Legesher org 20 days ago

Brings the LoRA repo README into alignment with the canonical project source-of-truth at legesher/language-decoded-experiments (HF PR #43, merged 2026-05-25).

Key changes

Adapter inventory rewritten to reflect actual subdirectory structure: cond-1-en (5k @ 3 seeds, 20k @ 1 seed), cond-2-{zh,es,ur} (5k @ 3 seeds, 20k @ 1 seed), cond-3-zh-5k-native-code (1 seed), cond-5-{zh,es,ur}-5k-c4ai-aya-expanse-32b (1 seed each). Adds cond-5 — the previous README had no cond-5 entries.
Cond-2 reframed as "Reserved-Word Translation (Legesher)" — Legesher v0.7.3 translates Python's reserved words (keywords, exceptions, built-in functions, numerical system for some target languages) into the target language; user logic preserved.
Cond-5 sequencing clarified: Legesher transpilation runs first (reserved words), then c4ai-aya-expanse-32b via the Cohere API translates the remaining content (identifiers, comments, docstrings, string literals).
Cond-4 reframed as "Community-Contributed Native Code" (pending sufficient contributions; no adapter exists yet).
Source-file control callout added: cond-1, cond-2, cond-5 share the same 5k file subset from bigcode/the-stack-v2-dedup. Cond-3 is the deliberate exception.
Why Tiny Aya as the base model framing added.
Refined-extractor banner added at the top (matches the banner on the experiments dataset).
Benchmark table updated: SIB-200 and Belebele added; MGSM dropped (with the null-result rationale: 3.35B params + 250 examples + ~5% accuracy floor + ±2.7pp CI = condition differences within noise).
Experimental ladder narrative rewritten with the new framings (baseline→1, 1→2, 2→3, 2→5, 3→5).
Citation title updated to "Language Decoded: Exploring the Impact of Native Code on Multilingual Models".
Limitations refreshed: per-language fine-tuning noted (combined-language training is a planned future condition); 103k variants exist on the data repo but no 103k adapters trained yet; extractor coverage bullet pointing at the refined-extractor work.
Title callout added at the top with the proposed paper title.

No adapter weights or training configs touched — README-only change. YAML frontmatter preserved verbatim.

docs(readme): align with canonical source-of-truth (cond-5 adapters, source-file control, refined extractor)8306d4c2

madiedgar changed pull request status to merged 14 days ago

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