README.md

---
license: llama2
language:
- lt
datasets:
- neurotechnology/lithuanian-qa-v1
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Lt-Llama2 is a family of pretrained and fine-tuned generative text models for Lithuanian. This is the repository for the **instruct 7B model**. Links to other models can be found at the bottom of this page.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
Neurotechnology company marks the first open-source initiative dedicated to developing a large language model (LLM) specialized in Lithuanian. The company has created and publicly released a collection of Lithuanian LLMs, available both as foundational models and instructional variants.


- **Developed by:** Neurotechnology
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s):** Lithuanian
- **License:** Llama2 Community License Agreement
- **Finetuned from model:** [Lt-Llama-2-7b](https://huggingface.co/neurotechnology/Lt-Llama-2-7b-hf)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Paper:** https://arxiv.org/abs/2408.12963

## Intended Use

### Intended Use Cases

Lt-Llama2 is designed for research purposes in Lithuanian. The base models can be tailored for various natural language tasks, while the instruction models are geared towards assistant-like conversational interactions.

### Prohibited use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Utilizing the model in ways that breach the license, violate any applicable laws or regulations, or involve languages other than Lithuanian.


## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("neurotechnology/Lt-Llama-2-7b-instruct-hf")
model = AutoModelForCausalLM.from_pretrained("neurotechnology/Lt-Llama-2-7b-instruct-hf")
PROMPT_TEMPLATE = (
        "[INST] <<SYS>> Esi paslaugus asistentas <</SYS>>{instruction}[/INST]"
    )

instruction ="Kas yra Lietuvos sostinė?"
prompt = PROMPT_TEMPLATE.format_map({'instruction':instruction})
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]))
```


## Lt-Llama2 Model Family

| Model              | Link  |
|--------------------|:--------:|
|Lt-Llama2-7b | [link](https://huggingface.co/neurotechnology/Lt-Llama-2-7b-hf)    |
|*Lt-Llama2-7b-instruct*| [link](https://huggingface.co/neurotechnology/Lt-Llama-2-7b-instruct-hf) |
|Lt-Llama2-13b | [link](https://huggingface.co/neurotechnology/Lt-Llama-2-13b-hf)    |
|Lt-Llama2-13b-instruct| [link](https://huggingface.co/neurotechnology/Lt-Llama-2-13b-instruct-hf) |


## Citation 

```bibtext
@misc{nakvosas2024openllama2modellithuanian,
      title={Open Llama2 Model for the Lithuanian Language},
      author={Artūras Nakvosas and Povilas Daniušis and Vytas Mulevičius},
      year={2024},
      eprint={2408.12963},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.12963},
}
```

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