Instructions to use andreidima/Llama-2-7b-Romanian-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andreidima/Llama-2-7b-Romanian-qlora with Transformers:

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

pipe = pipeline("text-generation", model="andreidima/Llama-2-7b-Romanian-qlora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andreidima/Llama-2-7b-Romanian-qlora")
model = AutoModelForCausalLM.from_pretrained("andreidima/Llama-2-7b-Romanian-qlora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andreidima/Llama-2-7b-Romanian-qlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreidima/Llama-2-7b-Romanian-qlora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andreidima/Llama-2-7b-Romanian-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andreidima/Llama-2-7b-Romanian-qlora

SGLang

How to use andreidima/Llama-2-7b-Romanian-qlora 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 "andreidima/Llama-2-7b-Romanian-qlora" \
    --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": "andreidima/Llama-2-7b-Romanian-qlora",
		"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 "andreidima/Llama-2-7b-Romanian-qlora" \
        --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": "andreidima/Llama-2-7b-Romanian-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use andreidima/Llama-2-7b-Romanian-qlora with Docker Model Runner:
```
docker model run hf.co/andreidima/Llama-2-7b-Romanian-qlora
```

Model Card for Model ID

Model Details

Model Description

RoQLlama is a new lightweight Romanian language-adapted LLM with 7 billion parameters and quantized to 4 bits by employing the state-of-the-art quantized LoRA (QLoRA) training technique.

Language: Romanian
License: Llama2 Community License Agreement
Finetuned from model: Meta's Llama2 7B

Model Sources

Paper: https://arxiv.org/abs/2410.04269

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_NAME = "andreidima/Llama-2-7b-Romanian-qlora"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, device_map="auto")
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto")

input_text = """Eu răspund la întrebări pe baza contextului.
Context: În anul 1600, Mihai Viteazul a realizat prima unire a Țărilor Române: Țara Românească, Transilvania și Moldova. Această unire a fost un moment important în istoria României.
Întrebare: În ce an a realizat Mihai Viteazul prima unire a Țărilor Române?
Răspuns: """
input_ids = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(
    **input_ids, 
    max_new_tokens=100, 
    eos_token_id=[13] # 13 is the token ID for a newline character at the end of a non-empty line
)
print(tokenizer.decode(outputs[0]))

Note: Adding a space at the end of the prompt has been observed to significantly improve the model's output quality.

Training Details and Evaluation

Please refer to the paper for details on the model's training and evaluation.

Citation

BibTeX:

@inproceedings{dima2024roqllama,
      title={RoQLlama: A Lightweight Romanian Adapted Language Model}, 
      author={George-Andrei Dima and Andrei-Marius Avram and Cristian-George Crăciun and Dumitru-Clementin Cercel},
      booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024}
      year={2024},
      url={https://arxiv.org/abs/2410.04269}, 
}

APA:

Dima, G. A., Avram, A. M., Crăciun, C. G., & Cercel, D. C. (2024). RoQLlama: A lightweight Romanian adapted language model. In Findings of the Association for Computational Linguistics: EMNLP 2024.

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Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for andreidima/Llama-2-7b-Romanian-qlora

Finetunes

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Paper for andreidima/Llama-2-7b-Romanian-qlora

RoQLlama: A Lightweight Romanian Adapted Language Model

Paper • 2410.04269 • Published Oct 5, 2024