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): cond-5 refined-extractor banner + Phase 3 staleness fixes

by madiedgar - opened 21 days ago

base: refs/heads/main

←

from: refs/pr/9

Discussion Files changed

+19

-9

docs(readme): cond-5 refined-extractor banner + Phase 3 staleness fixesfa4b0f2e

madiedgar

Legesher org 21 days ago

Adds the same ## ⚠️ Phase 3 eval numbers — read the experiments repo before citing banner that's going on legesher/language-decoded-experiments, plus Phase 3 staleness fixes to the eval section.

Why the banner: this LoRA repo links readers at legesher/language-decoded-experiments for results. Those _summary_*.json files under-report cond-5 SIB-200 accuracy by 20–35pp; cite _summary_reparsed_*.json instead. See the experiments-repo banner for the full picture.

Staleness fixes applied alongside the banner:

Evaluation benchmarks table: added SIB-200 + Belebele (Phase 3) alongside MGSM/X-CSQA/XNLI (Phase 2+3)
Limitations: updated "Currently evaluated on 3 benchmarks" → 5 benchmarks, added an Extractor coverage bullet pointing at the refined-extractor writeup
Model Structure section: added a note that cond-5 is a cross-lingual evaluation pattern reusing cond-2 adapters (the cond-5 sub-directories live on the experiments dataset, not in this LoRA repo)
Experimental ladder: added the --> 5 step ("does shared script or language family create transfer effects when an adapter trained on one language is prompted in another?")

No adapter weights or configs touched. README-only change.

Refs: expedition-tiny-aya/analysis/phase-3/post-refined-action-items.md item E.

fix(banner): 5 SIB-200 flip cells (was 4) + 4.4× cond-2-ur-5k deflation (was implicit)e68fb52f

madiedgar changed pull request status to merged 15 days ago

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