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OpenLLM-Ro/RoLlama2-7b-Chat
RoLlama2-7b-Chat is a text generation model from OpenLLM-Ro. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the chat 7B model. Links to other models can be found at the bottom of this page.
Downloads · 30 days
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From the Hugging Face model README
RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the chat 7B model. Links to other models can be found at the bottom of this page.
OpenLLM represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
RoLlama2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Chat")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Chat")
instruction = "Care este cel mai înalt vârf muntos din România?"
chat = [
{"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."},
{"role": "user", "content": instruction},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
| Model | Average | ARC | MMLU | Winogrande | HellaSwag | GSM8k | TruthfulQA |
|---|---|---|---|---|---|---|---|
| Llama-2-7b-chat | 36.84 | 37.03 | 33.81 | 55.87 | 45.36 | 4.90 | 44.09 |
| RoLlama2-7b-Instruct | 45.71 | 43.66 | 39.70 | 70.34 | 57.36 | 18.78 | 44.44 |
| RoLlama2-7b-Chat | 43.82 | 41.92 | 37.29 | 66.68 | 57.91 | 13.47 | 45.65 |
| Model | Average | 1st turn | 2nd turn | Answers in Ro |
|---|---|---|---|---|
| Llama-2-7b-chat | 1.08 | 1.44 | 0.73 | 45 / 160 |
| RoLlama2-7b-Instruct | 3.86 | 4.68 | 3.04 | 160 / 160 |
| RoLlama2-7b-Chat | TBC | TBC | TBC | TBC |
| Model | Score | Answers in Ro |
|---|---|---|
| Llama-2-7b-chat | 1.21 | 33 / 100 |
| RoLlama2-7b-Instruct | 3.77 | 160 / 160 |
| RoLlama2-7b-Chat | TBC | TBC |
@misc{masala2024openllmrotechnicalreport,
title={OpenLLM-Ro -- Technical Report on Open-source Romanian LLMs},
author={Mihai Masala and Denis C. Ilie-Ablachim and Dragos Corlatescu and Miruna Zavelca and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
year={2024},
eprint={2405.07703},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2405.07703},
}
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