Instructions to use ljvmiranda921/Polyglot-SFT-Multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljvmiranda921/Polyglot-SFT-Multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ljvmiranda921/Polyglot-SFT-Multilingual")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ljvmiranda921/Polyglot-SFT-Multilingual", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ljvmiranda921/Polyglot-SFT-Multilingual with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ljvmiranda921/Polyglot-SFT-Multilingual" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ljvmiranda921/Polyglot-SFT-Multilingual", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ljvmiranda921/Polyglot-SFT-Multilingual
- SGLang
How to use ljvmiranda921/Polyglot-SFT-Multilingual 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 "ljvmiranda921/Polyglot-SFT-Multilingual" \ --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": "ljvmiranda921/Polyglot-SFT-Multilingual", "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 "ljvmiranda921/Polyglot-SFT-Multilingual" \ --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": "ljvmiranda921/Polyglot-SFT-Multilingual", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ljvmiranda921/Polyglot-SFT-Multilingual with Docker Model Runner:
docker model run hf.co/ljvmiranda921/Polyglot-SFT-Multilingual
Multilingual Instruct Models (Polyglot Teachers)
These are per-language models supervised fine-tuned on the synthetic data generated in the Polyglot Teachers project (see ljvmiranda921/PolyglotTeachers-SFT-Synth).
Load a specific model by passing the branch as the revision:
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "ljvmiranda921/Polyglot-SFT-Multilingual"
branch = "Polyglot-OLMo3-7B-SFT-ar" # pick any branch below
model = AutoModelForCausalLM.from_pretrained(repo, revision=branch)
tokenizer = AutoTokenizer.from_pretrained(repo, revision=branch)
Branches
| Branch | Description |
|---|---|
Polyglot-Gemma3-4B-SFT-ar |
Gemma-3 4B SFT โ Arabic |
Polyglot-Gemma3-4B-SFT-de |
Gemma-3 4B SFT โ German |
Polyglot-Gemma3-4B-SFT-id |
Gemma-3 4B SFT โ Indonesian |
Polyglot-Gemma3-4B-SFT-tl |
Gemma-3 4B SFT โ Tagalog |
Polyglot-OLMo3-7B-SFT-ar |
OLMo-3 7B SFT โ Arabic |
Polyglot-OLMo3-7B-SFT-cs |
OLMo-3 7B SFT โ Czech |
Polyglot-OLMo3-7B-SFT-de |
OLMo-3 7B SFT โ German |
Polyglot-OLMo3-7B-SFT-es |
OLMo-3 7B SFT โ Spanish |
Polyglot-OLMo3-7B-SFT-id |
OLMo-3 7B SFT โ Indonesian |
Polyglot-OLMo3-7B-SFT-ja |
OLMo-3 7B SFT โ Japanese |
Licensing
This repo holds models under different licenses; each branch follows its base model's license:
Polyglot-OLMo3-7B-SFT-*(base allenai/Olmo-3-1025-7B) โ Apache-2.0Polyglot-Gemma3-4B-SFT-*(base google/gemma-3-4b-pt) โ Gemma license
Acknowledgements
LJVM and AK acknowledge the support of the UKRI Frontier Grant EP/Y031350/1 (EQUATE). This work was performed using joint resources provided by the Cambridge Service for Data Driven Discovery (CSD3) EP/T022159/1 and the Isambard AI National AI Research Resource (AIRR) ST/AIRR/I-A-I/1023, and the Microsoft Research Grant. LJVM would also like to thank Songbo Hu, Chen Cecilia Liu, Millicent Ochieng, and Felermino Ali for helpful and productive discussions on the project.
Citation
@misc{miranda2026polyglotteachersevaluatinglanguage,
title={Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation},
author={Lester James V. Miranda and Ivan Vuliฤ and Anna Korhonen},
year={2026},
eprint={2604.11290},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2604.11290},
}
Model tree for ljvmiranda921/Polyglot-SFT-Multilingual
Base model
allenai/Olmo-3-1025-7B