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license: cc-by-nc-4.0 |
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language: |
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- ro |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the foundational 7B models. Links to other models can be found at the bottom of this page. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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RoLlama 2 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. |
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- **Developed by:** OpenLLM-Ro |
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<!-- - **Funded by [optional]:** [More Information Needed] --> |
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<!-- - **Shared by [optional]:** [More Information Needed] --> |
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<!-- - **Model type:** [More Information Needed] --> |
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- **Language(s):** Romanian |
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- **License:** cc-by-nc-4.0 |
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<!-- - **Finetuned from model [optional]:** [More Information Needed] --> |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/OpenLLM-Ro/llama-recipes |
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- **Paper:** [More Information Needed] |
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## Intended Use |
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### Intended Use Cases |
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RoLlama2is 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. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base") |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Benchmarks |
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| Model | Average | ARC | MMLU |Winogrande|HellaSwag | GSM8k |TruthfulQA| |
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|--------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
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| Llama-2-7b | 35.65 | 33.85 | 30.93 | 56.43 | 46.98 | 1.37 | 44.36 | |
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| *RoLlama2-7b-Base* | *38.32* | *35.83* | *30.47* | *60.16* | *55.52* | *2.17* | *45.78* | |
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| Llama-2-7b-chat | 35.58 | 34.92 | 32.37 | 54.26 | 44.52 | 2.05 | 45.38 | |
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|RoLlama2-7b-Instruct| **44.42**|**40.36** |**37.41** |**69.58** | 55.64 | **17.59**| 45.96 | |
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|RoLlama2-7b-Chat | 42.65 | 38.29 | 35.27 | 65.25 | **56.45**| 12.84 | **47.79**| |
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## MT-Bench |
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| Model | Average | 1st turn | 2nd turn | |
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|--------------------|:--------:|:--------:|:--------:| |
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| Llama-2-7b-chat | 1.70 | 2.00 | 1.41 | |
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|RoLlama2-7b-Instruct| **4.31**|**5.66** |**2.95** | |
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|RoLlama2-7b-Chat | 3.91 | 4.25 | 3.57 | |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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