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 Settings

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

Files changed (1) hide show

README.md +16 -2

README.md CHANGED Viewed

@@ -1,11 +1,14 @@
 ---
-license: apache-2.0
 datasets:
 - LumiOpen/instruction-collection-fin
 language:
 - fi
 - en
 ---
 <div align="center">
 <img src="./poro-logo.png" width="200px">
 </div>
@@ -77,6 +80,17 @@ Note: Updated on 18 June 2024
 Poro 34B chat is released under the Apache 2.0 license.
 ## Citation
@@ -91,4 +105,4 @@ Pyysalo},
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```

 ---
 datasets:
 - LumiOpen/instruction-collection-fin
 language:
 - fi
 - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 <div align="center">
 <img src="./poro-logo.png" width="200px">
 </div>
 Poro 34B chat is released under the Apache 2.0 license.
+## Paper
+The model was presented in the paper [Poro 34B and the Blessing of Multilinguality](https://huggingface.co/papers/2404.01856).
+## Project Page
+[Poro-34B](https://huggingface.co/LumiOpen/Poro-34B)
+## Code
+The code can be found at https://github.com/TurkuNLP/Megatron-DeepSpeed.
 ## Citation
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
+```