Instructions to use tartuNLP/Llammas-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tartuNLP/Llammas-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tartuNLP/Llammas-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tartuNLP/Llammas-base") model = AutoModelForCausalLM.from_pretrained("tartuNLP/Llammas-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tartuNLP/Llammas-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tartuNLP/Llammas-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tartuNLP/Llammas-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tartuNLP/Llammas-base
- SGLang
How to use tartuNLP/Llammas-base 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 "tartuNLP/Llammas-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tartuNLP/Llammas-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "tartuNLP/Llammas-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tartuNLP/Llammas-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tartuNLP/Llammas-base with Docker Model Runner:
docker model run hf.co/tartuNLP/Llammas-base
metadata
language:
- et
- en
pipeline_tag: text-generation
base_model:
- meta-llama/Llama-2-7b-hf
license: llama2
LLammas-base ๐
Llama-2-7B with continued pre-training of 5B tokens of CulturaX (75% Estonian, 25% English documents).
This model is also instruction-tuned resulting in Llammas.
More details in our paper.
Citation
@misc{kuulmets2024teaching,
title={Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer},
author={Hele-Andra Kuulmets and Taido Purason and Agnes Luhtaru and Mark Fishel},
year={2024},
eprint={2404.04042},
archivePrefix={arXiv},
primaryClass={cs.CL}
}