---
license: mit
language:
- en
- ro
base_model:
- LLMLit/LLMLit
tags:
- LLMLiT
- Romania
- LLM
datasets:
- LLMLit/LitSet
metrics:
- accuracy
- character
- code_eval
---
# Model Card for LLMLit

## Quick Summary
LLMLit is a high-performance, multilingual large language model (LLM) fine-tuned from Meta's Llama 3.1 8B Instruct model. Designed for both English and Romanian NLP tasks, LLMLit leverages advanced instruction-following capabilities to provide accurate, context-aware, and efficient results across diverse applications.

## Model Details

### Model Description
LLMLit is tailored to handle a wide array of tasks, including content generation, summarization, question answering, and more, in both English and Romanian. The model is fine-tuned with a focus on high-quality instruction adherence and context understanding. It is a versatile tool for developers, researchers, and businesses seeking reliable NLP solutions.

- **Developed by:** LLMLit Development Team
- **Funded by:** Open-source contributions and private sponsors
- **Shared by:** LLMLit Community
- **Model type:** Large Language Model (Instruction-tuned)
- **Languages:** English (en), Romanian (ro)
- **License:** MIT
- **Fine-tuned from model:** meta-llama/Llama-3.1-8B-Instruct

### Model Sources
- **Repository:** [GitHub Repository Link](https://github.com/PyThaGoAI/LLMLit)
- **Paper:** [To be published]
- **Demo:** [Coming Soon)

## Uses

### Direct Use
LLMLit can be directly applied to tasks such as:
- Generating human-like text responses
- Translating between English and Romanian
- Summarizing articles, reports, or documents
- Answering complex questions with context sensitivity

### Downstream Use
When fine-tuned or integrated into larger ecosystems, LLMLit can be utilized for:
- Chatbots and virtual assistants
- Educational tools for bilingual environments
- Legal or medical document analysis
- E-commerce and customer support automation

### Out-of-Scope Use
LLMLit is not suitable for:
- Malicious or unethical applications, such as spreading misinformation
- Highly sensitive or critical decision-making without human oversight
- Tasks requiring real-time, low-latency performance in constrained environments

## Bias, Risks, and Limitations

### Bias
- LLMLit inherits biases present in the training data. It may produce outputs that reflect societal or cultural biases.

### Risks
- Misuse of the model could lead to misinformation or harm.
- Inaccurate responses in complex or domain-specific queries.

### Limitations
- Performance is contingent on the quality of input instructions.
- Limited understanding of niche or highly technical domains.

### Recommendations
- Always review model outputs for accuracy, especially in sensitive applications.
- Fine-tune or customize for domain-specific tasks to minimize risks.

## How to Get Started with the Model
To use LLMLit, install the required libraries and load the model as follows:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

### Training Data
LLMLit is fine-tuned on a diverse dataset containing bilingual (English and Romanian) content, ensuring both linguistic accuracy and cultural relevance.

### Training Procedure
#### Preprocessing
- Data was filtered for high-quality, instruction-based examples.
- Augmentation techniques were used to balance linguistic domains.

#### Training Hyperparameters
- **Training regime:** Mixed precision (fp16)
- **Batch size:** 512
- **Epochs:** 3
- **Learning rate:** 2e-5

#### Speeds, Sizes, Times
- **Checkpoint size:** ~16GB
- **Training time:** Approx. 1 week on 8 A100 GPUs

## Evaluation

### Testing Data, Factors & Metrics
#### Testing Data
Evaluation was conducted on multilingual benchmarks, such as:
- FLORES-101 (Translation accuracy)
- HELM (Instruction-following capabilities)

#### Factors
Evaluation considered:
- Linguistic fluency
- Instruction adherence
- Contextual understanding

#### Metrics
- BLEU for translation tasks
- ROUGE-L for summarization
- Human evaluation scores for instruction tasks

### Results
LLMLit achieves state-of-the-art performance on instruction-following tasks for English and Romanian, with BLEU scores surpassing comparable models.

#### Summary
LLMLit excels in bilingual NLP tasks, offering robust performance across diverse domains while maintaining instruction adherence and linguistic accuracy.

## Model Examination
Efforts to interpret the model include:
- Attention visualization
- Prompt engineering guides
- Bias audits

## Environmental Impact
Training LLMLit resulted in estimated emissions of ~200 kg CO2eq. Carbon offsets were purchased to mitigate environmental impact. Future optimizations aim to reduce energy consumption.

![Civis3.png](https://cdn-uploads.huggingface.co/production/uploads/6769b18893c0c9156b8265d5/pZch1_YVa6Ixc3d_eYxBR.png)


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