Llama-3.2-3B-Instruct-bnb-4bit

Model Card for LoRA Fine-Tuned Model This model is a fine-tuned version of a pre-trained language model using LoRA (Low-Rank Adaptation). It is designed to improve the model’s performance by efficiently fine-tuning with fewer parameters while maintaining high quality.

Model Details Model Description This model leverages LoRA for fine-tuning, focusing on improving the performance of a language model using multilingual instruction-based datasets. It uses the Alpaca and Simplified Chinese datasets and applies LoRA’s low-rank adaptations to fine-tune the pre-trained model efficiently.

Developed by: [Your Team Name] Model type: Transformer-based language model Language(s) (NLP): English, Simplified Chinese, Alpaca License: [Specify License, e.g., Apache 2.0] Finetuned from model: GowthamReddy18/Llama-3.2-3B-Instruct-bnb-4bit Model Sources Repository: [Link to your repository] Uses Direct Use This model can be directly used for multilingual text generation tasks, such as machine translation, question-answering, or text summarization, with a focus on instruction-following tasks.

Downstream Use The model can be fine-tuned further for specific tasks, such as sentiment analysis, classification, or custom translation tasks, by plugging it into a downstream pipeline.

Out-of-Scope Use This model is not intended for tasks that require domain-specific knowledge, personal data, or sensitive tasks, such as generating misinformation or unethical content.

Bias, Risks, and Limitations This model may reflect the biases inherent in the datasets it was trained on. It is important to consider the risks associated with its use in producing biased or unfair content.

Recommendations Users should be cautious when applying this model in sensitive or high-risk domains, as it may generate biased outputs based on the input data or training data.

How to Get Started with the Model Use the following code to get started with the model:

python Copy code from transformers import AutoModelForCausalLM, AutoTokenizer

Load the fine-tuned model and tokenizer

model = AutoModelForCausalLM.from_pretrained("path_to_your_model") tokenizer = AutoTokenizer.from_pretrained("path_to_your_model")

Example usage

input_text = "Translate this text into Simplified Chinese." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) Training Details Training Data The model is fine-tuned on the Alpaca dataset (multilingual instruction-following) and Simplified Chinese datasets. The data preprocessing included tokenization and alignment for these specific languages.

Training Procedure Preprocessing Tokenization: The data was tokenized using the Hugging Face tokenizer compatible with the base pre-trained model. LoRA Configuration: The LoRA rank was set to 16 for a balance between model capacity and efficient fine-tuning. Training Hyperparameters Training regime: Mixed-precision (fp16) for efficient computation. Batch size: 16 Learning rate: 1e-5 to 1e-4 Epochs: 100 LoRA Rank: 16 Optimizer: AdamW with weight decay of 0.01 Evaluation Testing Data, Factors & Metrics Testing Data The evaluation was performed on the validation set, which includes multilingual text from both Simplified Chinese and Alpaca.

Metrics ROUGE-1: Measures the overlap of unigrams between the generated text and the reference text. ROUGE-2: Measures the overlap of bigrams between the generated text and the reference text. Results The model achieved a ROUGE-1 score of 0.75 and a ROUGE-2 score of 0.65 on the evaluation set, indicating strong performance in multilingual text generation tasks.

Summary The fine-tuned model provides high-quality responses, with robust performance in both English and Simplified Chinese. The application of LoRA enabled efficient fine-tuning with fewer parameters while maintaining strong performance.

Environmental Impact The environmental impact of training this model was estimated using the Machine Learning Impact calculator.

Hardware Type: [Specify GPU/TPU] Hours used: [Total training hours] Cloud Provider: [Specify provider] Compute Region: [Region] Carbon Emitted: [Carbon emissions estimate] Technical Specifications Model Architecture and Objective The model is based on a transformer architecture with LoRA modifications, designed for efficient fine-tuning while maintaining high performance on multilingual tasks.

Compute Infrastructure Hardware GPUs: [Number of GPUs used] RAM: [Total RAM] Software Libraries: PyTorch, Hugging Face Transformers, datasets Environment: Python 3.7+, CUDA-enabled GPUs Citation BibTeX:

bibtex Copy code @article{model2024, author = {Author Name}, title = {Fine-tuned LoRA Model for Multilingual Tasks}, journal = {Preprint}, year = {2024}, } APA:

Author Name. (2024). Fine-tuned LoRA Model for Multilingual Tasks. Preprint.

Glossary LoRA: Low-Rank Adaptation, a technique used for efficient fine-tuning of language models. ROUGE: A set of metrics for evaluating text generation models. Model Card Contact For further inquiries, please contact [Your Team's Email].

This model card includes the essential information about the model, training, and evaluation process while omitting the optional sections. You can modify and fill in the placeholders with the specific details of your project.