Instructions to use orai-nlp/Llama-eus-8B-Instruct-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use orai-nlp/Llama-eus-8B-Instruct-v1 with Transformers:

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

pipe = pipeline("text-generation", model="orai-nlp/Llama-eus-8B-Instruct-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("orai-nlp/Llama-eus-8B-Instruct-v1")
model = AutoModelForCausalLM.from_pretrained("orai-nlp/Llama-eus-8B-Instruct-v1", 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 orai-nlp/Llama-eus-8B-Instruct-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "orai-nlp/Llama-eus-8B-Instruct-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "orai-nlp/Llama-eus-8B-Instruct-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/orai-nlp/Llama-eus-8B-Instruct-v1

SGLang

How to use orai-nlp/Llama-eus-8B-Instruct-v1 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 "orai-nlp/Llama-eus-8B-Instruct-v1" \
    --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": "orai-nlp/Llama-eus-8B-Instruct-v1",
		"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 "orai-nlp/Llama-eus-8B-Instruct-v1" \
        --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": "orai-nlp/Llama-eus-8B-Instruct-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use orai-nlp/Llama-eus-8B-Instruct-v1 with Docker Model Runner:
```
docker model run hf.co/orai-nlp/Llama-eus-8B-Instruct-v1
```

Instructed Llama-eus-8B [DEPRECATED]

First instructed version of Llama-eus-8B. Curated by instruction tuning the base model with SlimOrca_eu Basque instructions and preference tuned with ultrafeedback_eu.

📕 Paper: Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque

NOTE: This model is deprecated. See Orai NLP's HuggingFace homepage to check up to date instructed models!

License

Acknowledgments

This work is part of the BasqueLLM project, titled "First steps towards an artificial intelligence in Basque based on LLMs" (EXP: 2023-CIEN-000081-01), partially funded by the Guipuzcoa Science, Technology and Innovation Network Program of the Provincial Council of Gipuzkoa. Model training and development were conducted using the Hyperion system at the Donostia International Physics Center (DIPC).

Citation

If you use Llama-eus-8B please cite the following reference:

@inproceedings{corral-etal-2025-pipeline,
    title = "Pipeline Analysis for Developing Instruct {LLM}s in Low-Resource Languages: A Case Study on {B}asque",
    author = "Corral, Ander and Antero, Ixak Sarasua and Saralegi, Xabier",
    editor = "Chiruzzo, Luis and Ritter, Alan and Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.naacl-long.629/",
    pages = "12636--12655",
    ISBN = "979-8-89176-189-6",
    abstract = "Large language models (LLMs) are typically optimized for resource-rich languages like English, exacerbating the gap between high-resource and underrepresented languages. This work presents a detailed analysis of strategies for developing a model capable of following instructions in a low-resource language, specifically Basque, by focusing on three key stages: pre-training, instruction tuning, and alignment with human preferences. Our findings demonstrate that continual pre-training with a high-quality Basque corpus of around 600 million words improves natural language understanding (NLU) of the foundational model by over 12 points. Moreover, instruction tuning and human preference alignment using automatically translated datasets proved highly effective, resulting in a 24-point improvement in instruction-following performance. The resulting models, Llama-eus-8B and Llama-eus-8B-instruct, establish a new state-of-the-art for Basque in the sub-10B parameter category."
}