| | --- |
| | license: mit |
| | language: |
| | - en |
| | - ro |
| | base_model: |
| | - LLMLit/LLMLit |
| | tags: |
| | - LLMLiT |
| | - Romania |
| | - LLM |
| | --- |
| | # Model Card for LLMLit |
| |
|
| | <iframe width="560" height="315" src="https://www.youtube.com/embed/mJBNb5nmHcs?si=gxnkoIHXKtzODbdC" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> |
| |
|
| | ## 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. |
| |
|
| | --- |
