Instructions to use mrapacz/interlinear-en-philta-emb-sum-normalized-ob with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrapacz/interlinear-en-philta-emb-sum-normalized-ob with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mrapacz/interlinear-en-philta-emb-sum-normalized-ob")

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("mrapacz/interlinear-en-philta-emb-sum-normalized-ob", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrapacz/interlinear-en-philta-emb-sum-normalized-ob with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrapacz/interlinear-en-philta-emb-sum-normalized-ob"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrapacz/interlinear-en-philta-emb-sum-normalized-ob",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrapacz/interlinear-en-philta-emb-sum-normalized-ob

SGLang

How to use mrapacz/interlinear-en-philta-emb-sum-normalized-ob 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 "mrapacz/interlinear-en-philta-emb-sum-normalized-ob" \
    --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": "mrapacz/interlinear-en-philta-emb-sum-normalized-ob",
		"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 "mrapacz/interlinear-en-philta-emb-sum-normalized-ob" \
        --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": "mrapacz/interlinear-en-philta-emb-sum-normalized-ob",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrapacz/interlinear-en-philta-emb-sum-normalized-ob with Docker Model Runner:
```
docker model run hf.co/mrapacz/interlinear-en-philta-emb-sum-normalized-ob
```

interlinear-en-philta-emb-sum-normalized-ob

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---
license: cc-by-sa-4.0
language:
- en
metrics:
- bleu
base_model:
- PhilTa
library_name: transformers
datasets:
- mrapacz/greek-interlinear-translations
---
# Model Card for Ancient Greek to English Interlinear Translation Model

This model performs interlinear translation from Ancient Greek to {Language}, maintaining word-level alignment between source and target texts.

## Model Details

### Model Description

- **Developed By:** Maciej Rapacz, AGH University of Kraków
- **Model Type:** Neural machine translation (T5-based)
- **Base Model:** PhilTa
- **Tokenizer:** PhilTa
- **Language(s):** Ancient Greek (source) → English (target)
- **License:** CC BY-NC-SA 4.0
- **Tag Set:** OB (Oblubienica)
- **Text Preprocessing:** Normalized
- **Morphological Encoding:** emb-sum

### Model Performance

- **BLEU Score:** 55.49
- **SemScore:** 0.86

### Model Sources

- **Repository:** https://github.com/mrapacz/loreslm-interlinear-translation
- **Paper:** https://aclanthology.org/2025.loreslm-1.11/