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
| library_name: transformers | |
| license: other | |
| license_name: mixed | |
| pipeline_tag: text-generation | |
| language: | |
| - ar | |
| - es | |
| - cs | |
| - de | |
| - id | |
| - tl | |
| - ja | |
| base_model: | |
| - allenai/Olmo-3-1025-7B | |
| - google/gemma-3-4b-pt | |
| datasets: | |
| - ljvmiranda921/PolyglotTeachers-SFT-Synth | |
| tags: | |
| - multilingual | |
| - synthetic | |
| - sft | |
| <div style="display: flex; align-items: center; gap: 20px;"> | |
| <img alt="Logo for UCam" src="cambridge_logo.png" style="height: 80px; width: auto;"> | |
| <img alt="Logo for LTL" src="ltl_logo2.svg" style="height: 80px; width: auto;"> | |
| </div> | |
| # Multilingual Instruct Models (Polyglot Teachers) | |
| These are per-language models supervised fine-tuned on the synthetic data | |
| generated in the [Polyglot Teachers](https://huggingface.co/papers/2604.11290) | |
| project (see [ljvmiranda921/PolyglotTeachers-SFT-Synth](https://huggingface.co/datasets/ljvmiranda921/PolyglotTeachers-SFT-Synth)). | |
| Load a specific model by passing the branch as the `revision`: | |
| ```python | |
| 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](https://huggingface.co/allenai/Olmo-3-1025-7B)) โ Apache-2.0 | |
| - `Polyglot-Gemma3-4B-SFT-*` (base [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt)) โ [Gemma license](https://ai.google.dev/gemma/terms) | |
| ## Acknowledgements | |
| LJVM and AK acknowledge the support of the UKRI Frontier Grant EP/Y031350/1 ([EQUATE](https://gtr.ukri.org/projects?ref=EP%2FY031350%2F1)). | |
| This work was performed using joint resources provided by the [Cambridge Service for Data Driven Discovery (CSD3)](https://hpc.cam.ac.uk/high-performance-computing) EP/T022159/1 and the [Isambard AI National AI Research Resource (AIRR)](https://www.bristol.ac.uk/research/centres/bristol-supercomputing/#isambard-ai) 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 | |
| ```bibtex | |
| @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}, | |
| } | |
| ``` | |