Instructions to use LumiOpen/Poro-34B-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LumiOpen/Poro-34B-chat with Transformers:

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

pipe = pipeline("text-generation", model="LumiOpen/Poro-34B-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LumiOpen/Poro-34B-chat")
model = AutoModelForCausalLM.from_pretrained("LumiOpen/Poro-34B-chat")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LumiOpen/Poro-34B-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LumiOpen/Poro-34B-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LumiOpen/Poro-34B-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LumiOpen/Poro-34B-chat

SGLang

How to use LumiOpen/Poro-34B-chat 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 "LumiOpen/Poro-34B-chat" \
    --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": "LumiOpen/Poro-34B-chat",
		"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 "LumiOpen/Poro-34B-chat" \
        --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": "LumiOpen/Poro-34B-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LumiOpen/Poro-34B-chat with Docker Model Runner:
```
docker model run hf.co/LumiOpen/Poro-34B-chat
```

Add model card

by nielsr HF Staff - opened Jun 11, 2025

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

+16

-2

Add model card2f07f535

nielsr

Jun 11, 2025

This PR adds a model card, ensuring people can find it at https://huggingface.co/models?pipeline_tag=text-generation,
and links the model to the paper Poro 34B and the Blessing of Multilinguality.
Besides that, a link to the Github repository and project page are also present.

jonabur

LumiOpen org Jun 12, 2025

Thank you for the PR, but the code base we used is actually here: https://github.com/TurkuNLP/Megatron-DeepSpeed

Would you mind updating?

Update Github URLe660cf77

nielsr

Jun 12, 2025

Thanks, done.

jonabur

LumiOpen org Jun 12, 2025

Merged, thank you!

jonabur changed pull request status to merged Jun 12, 2025

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