Instructions to use tartuNLP/Llammas-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tartuNLP/Llammas-base")
model = AutoModelForCausalLM.from_pretrained("tartuNLP/Llammas-base", device_map="auto")

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LLammas-base 🐑

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}
}