Instructions to use LumiOpen/Llama-Poro-2-8B-Long-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LumiOpen/Llama-Poro-2-8B-Long-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="LumiOpen/Llama-Poro-2-8B-Long-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LumiOpen/Llama-Poro-2-8B-Long-Instruct")
model = AutoModelForCausalLM.from_pretrained("LumiOpen/Llama-Poro-2-8B-Long-Instruct", device_map="auto")
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/Llama-Poro-2-8B-Long-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LumiOpen/Llama-Poro-2-8B-Long-Instruct"
# 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/Llama-Poro-2-8B-Long-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LumiOpen/Llama-Poro-2-8B-Long-Instruct

SGLang

How to use LumiOpen/Llama-Poro-2-8B-Long-Instruct 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/Llama-Poro-2-8B-Long-Instruct" \
    --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/Llama-Poro-2-8B-Long-Instruct",
		"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/Llama-Poro-2-8B-Long-Instruct" \
        --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/Llama-Poro-2-8B-Long-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LumiOpen/Llama-Poro-2-8B-Long-Instruct with Docker Model Runner:
```
docker model run hf.co/LumiOpen/Llama-Poro-2-8B-Long-Instruct
```

Poro 2 Long Instruct Model Card

Poro 2 Long Instruct is an instruction-following chatbot model with extended context support, created through supervised fine-tuning (SFT) of the Poro 2 Long Base model followed by merging the SFT checkpoint back with the base model to preserve long-context performance. This model is designed for conversational AI applications and instruction following in both Finnish and English, with support for context lengths up to 128K tokens. It was trained on a carefully curated mix of English and Finnish instruction data.

This model demonstrates how continued pretraining with context extension followed by instruction tuning can efficiently add new language capabilities and longer context support to existing models while maintaining strong conversational abilities in both the original and target languages.

For more details on our training and data generation pipeline, check out our Continued Pretraining Playbook.

Poro 2 Long Model Family

The Poro 2 Long model family extends the Poro 2 models with longer context support and checkpoints trained especially on math reasoning and instruction following. There are four checkpoints released: a base model, an instruction-tuned model, a math reasoning SFT checkpoint, and the final math reasoning RL checkpoint.

Base Model	Instruct	Math Reasoning SFT	Math Reasoning RL
Poro 2 Long Base	Poro 2 Long Instruct	Poro 2 Long Math Reasoning SFT	Poro 2 Long Math Reasoning RL

What does Poro mean? Poro is the Finnish word for Reindeer! 🦌 These animals are native to Finland and hold a significant and historical role in Finnish culture.

Model Overview

Poro 2 Long Instruct is based on the Poro 2 Long Base model (context-extended from Poro 2 8B base) and has been supervised fine-tuned and optimised for for conversation and instruction following. The model supports both English and Finnish conversations with an extended context window of 128K tokens.

Training Process

The instruction checkpoint is trained separately from the reasoning checkpoint. We run SFT on Poro 2 8B Long Base using the same instruction dataset as Poro 2 Instruct, then merge the resulting SFT checkpoint back with the base model to preserve long-context performance. We do not perform a DPO stage.

Supervised Fine-Tuning (SFT)

We use the same SFT dataset as Poro 2 8B SFT: the Poro 2 Instruction Collection. We pack the samples and use a sequence length of 32k.

SFT Hyperparameters

Hyperparameter	Value
Epochs	2
Global batch size	64
Learning rate	2e-5
LR scheduler	cosine_with_min_lr
Warmup ratio	0.03
Max sequence length	32,768

Model Merging

We observe that the long-context performance of the instruction SFT checkpoint drops considerably compared to Poro 2 8B Long Base. Long-context degradation after post-training has been reported in other works, for example SmolLM3, which suggests model merging as a way to recover the base model's long-context capability.

Following this approach, we merge the SFT checkpoint with the base model using MergeKit. We use merge weights of 0.8 for the SFT checkpoint and 0.2 for the base checkpoint. This merged checkpoint is the Poro 2 8B Long Instruct model and restores long-context performance close to Poro 2 8B Long Base while retaining the instruction-following gains from SFT.

Training Dataset

Stage	Model	Dataset	License
CPT	Llama-Poro-2-Long-Base	https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu	ODC-BY-1.0
CPT	Llama-Poro-2-Long-Base	https://huggingface.co/datasets/HuggingFaceTB/finemath	ODC-BY-1.0
CPT	Llama-Poro-2-Long-Base	https://huggingface.co/datasets/bigcode/starcoderdata	Refer source materials
Instruct SFT	Llama-Poro-2-Long-Instruct	https://huggingface.co/datasets/LumiOpen/poro2-instruction-collection	Llama 3 Community License
Math SFT	Llama-Poro-2-Long-Math-Reasoning-SFT-Preview	https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset	CC-BY-4.0
Math SFT	Llama-Poro-2-Long-Math-Reasoning-SFT-Preview	https://huggingface.co/datasets/LumiOpen/Llama-Nemotron-Post-Training-Dataset-SFT-math-FI	CC-BY-4.0
Math RL	Llama-Poro-2-8B-Long-Math-Reasoning-RL-Preview	https://huggingface.co/datasets/Kwai-Klear/KlearReasoner-MathSub-30K	Apache-2.0

Evaluation Results

We evaluate Poro 2 8B Long Instruct on short-context instruction-following benchmarks (MTBench and IFEval) in English and Finnish, and on long-context benchmarks (HELMET for English, OneRuler for English and Finnish).

Short-Context Instruction Following

English

	MTBench EN	IFEval EN
Poro 2 8B Long Instruct	6.84	76.16
Llama 3.1 8B Instruct	7.7	79.48

Finnish

	MTBench FI	IFEval FI
Poro 2 8B Long Instruct	6.18	63.4
Llama 3.1 8B Instruct	4.1	47.31

Long-Context Evaluation

HELMET (English)

	8k	16k	32k	64k	128k
Poro 2 8B Long Instruct	60.63	58.83	55.84	51.18	43.02
Poro 2 8B Long Base	58.67	57.63	58.24	53.62	48.97
Llama 3.1 8B Instruct	64.4	64.27	62.63	62.82	56.45

OneRULER

English

	8k	32k	64k	128k
Poro 2 8B Long Instruct	53.74	51.03	51.66	46.17
Poro 2 8B Long Base	48.57	52.57	49.43	56.57
Llama 3.1 8B Instruct	72.43	67.26	64.51	55.57

Finnish

	8k	32k	64k	128k
Poro 2 8B Long Instruct	67.2	46.63	55.29	45.34
Poro 2 8B Long Base	60.86	55.14	52.86	51.43
Llama 3.1 8B Instruct	77.11	67.54	54.54	44.84

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "LumiOpen/Llama-Poro-2-Long-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Finnish conversation example with longer context
messages = [
    {"role": "user", "content": "Kerro minulle Suomen historiasta."}
]

inputs = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True,
    return_tensors="pt"
)

outputs = model.generate(
    inputs,
    max_new_tokens=500,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Intended Use

Poro 2 Long Instruct is designed for:

Conversational AI applications in Finnish and English
Long-context tasks such as document summarization and analysis
Multi-document question answering
Question answering and information retrieval
Content generation and creative writing
Educational applications
Customer service and support applications
Translation between Finnish and English

Ethical Considerations and Limitations

Poro 2 Long Instruct is an advanced conversational AI model optimized for English and Finnish instruction following with extended context support. As with most AI-driven systems, this model may reflect imperfections, biases, and idiosyncrasies present in its training data.

Key limitations:

Limited proficiency in languages other than English and Finnish
May occasionally generate biased, inappropriate, or factually incorrect content
Performance may vary significantly for specialized or technical domains
Extended context (32K tokens) may lead to increased latency and memory requirements
Long-context performance may degrade for very long sequences approaching the context limit
May struggle with very recent events (knowledge cutoff limitations)

Safety Considerations:

Users should verify important factual claims independently
The model should not be used for medical, legal, or financial advice without human oversight
Responses should be reviewed for appropriateness in sensitive contexts

License

Built with Llama.

Poro 2 Long Instruct is released under the Llama 3.3 Community License. Please review the license terms before use.

Acknowledgments

We thank CSC - IT Center for Science, Finland for providing access to the LUMI supercomputer.

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Model size

8B params

Tensor type

F16

Dataset used to train LumiOpen/Llama-Poro-2-8B-Long-Instruct

Collection including LumiOpen/Llama-Poro-2-8B-Long-Instruct

Poro 2 Long

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