Instructions to use Unbabel/M-Prometheus-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Unbabel/M-Prometheus-14B with Transformers:

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

pipe = pipeline("text-generation", model="Unbabel/M-Prometheus-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Unbabel/M-Prometheus-14B")
model = AutoModelForCausalLM.from_pretrained("Unbabel/M-Prometheus-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Unbabel/M-Prometheus-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Unbabel/M-Prometheus-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Unbabel/M-Prometheus-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Unbabel/M-Prometheus-14B

SGLang

How to use Unbabel/M-Prometheus-14B 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 "Unbabel/M-Prometheus-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Unbabel/M-Prometheus-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Unbabel/M-Prometheus-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Unbabel/M-Prometheus-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Unbabel/M-Prometheus-14B with Docker Model Runner:
```
docker model run hf.co/Unbabel/M-Prometheus-14B
```

lbourdois commited on Apr 28, 2025

Commit

0311d85

verified ·

1 Parent(s): 38c373d

Improve language tag

Browse files

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

Files changed (1) hide show

README.md +42 -28

README.md CHANGED Viewed

@@ -1,28 +1,42 @@
----
-library_name: transformers
-license: other
-base_model: Qwen/Qwen2.5-14B-Instruct
----
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-# M-Prometheus
-M-Prometheus is a suite of open LLM judges that can natively evaluate multilingual outputs. They were trained on 480k instances of multilingual direct assessment and pairwise comparison data wiht long-form feedback.
-They can be prompted in the same way as [Prometheus-2](https://huggingface.co/prometheus-eval/prometheus-7b-v2.0/tree/main).
-Check out our [paper](wip) for more details.
-## Citation
-```bibtex
-@misc{pombal2025mprometheussuiteopenmultilingual,
-      title={M-Prometheus: A Suite of Open Multilingual LLM Judges},
-      author={José Pombal and Dongkeun Yoon and Patrick Fernandes and Ian Wu and Seungone Kim and Ricardo Rei and Graham Neubig and André F. T. Martins},
-      year={2025},
-      eprint={2504.04953},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2504.04953},
-}
-```

+---
+library_name: transformers
+license: other
+base_model: Qwen/Qwen2.5-14B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# M-Prometheus
+M-Prometheus is a suite of open LLM judges that can natively evaluate multilingual outputs. They were trained on 480k instances of multilingual direct assessment and pairwise comparison data wiht long-form feedback.
+They can be prompted in the same way as [Prometheus-2](https://huggingface.co/prometheus-eval/prometheus-7b-v2.0/tree/main).
+Check out our [paper](wip) for more details.
+## Citation
+```bibtex
+@misc{pombal2025mprometheussuiteopenmultilingual,
+      title={M-Prometheus: A Suite of Open Multilingual LLM Judges},
+      author={José Pombal and Dongkeun Yoon and Patrick Fernandes and Ian Wu and Seungone Kim and Ricardo Rei and Graham Neubig and André F. T. Martins},
+      year={2025},
+      eprint={2504.04953},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2504.04953},
+}
+```