| ```markdown | |
| --- | |
| tags: | |
| - text-generation | |
| - transformers | |
| - opt-6.7b | |
| - lora | |
| license: mit | |
| datasets: | |
| - wikipedia | |
| - bookcorpus | |
| - openwebtext | |
| - conversational | |
| metrics: | |
| - perplexity | |
| - accuracy | |
| --- | |
| # babelAI/opt-6.7b-lora | |
| ## Model Description | |
| `babelAI/opt-6.7b-lora` is a variant of the OPT-6.7B model fine-tuned using LoRA (Low-Rank Adaptation) techniques. This model leverages the LoRA method to reduce the number of trainable parameters, allowing for efficient fine-tuning on domain-specific tasks without the need for extensive computational resources. | |
| ## Model Architecture | |
| - **Base Model**: OPT-6.7B | |
| - **Parameter Count**: 6.7 Billion | |
| - **Fine-Tuning Method**: LoRA (Low-Rank Adaptation) | |
| ## Intended Use | |
| This model is designed for a variety of natural language processing tasks, including but not limited to: | |
| - Text generation | |
| - Text completion | |
| - Conversational AI | |
| - Language translation | |
| ## How to Use | |
| ### Installation | |
| First, ensure you have the `transformers` library installed: | |
| ```bash | |
| pip install transformers | |
| ``` | |
| ### Loading the Model | |
| Here is an example of how to load and use the `babelAI/opt-6.7b-lora` model: | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel, PeftConfig | |
| from transformers import BitsAndBytesConfig | |
| # Define the model ID | |
| peft_model_id = "babelAI/opt-6.7b-lora" | |
| # Load the configuration | |
| config = PeftConfig.from_pretrained(peft_model_id) | |
| # Define the quantization configuration for efficient loading | |
| quantization_config = BitsAndBytesConfig(load_in_8bit=True) | |
| # Load the base model with the quantization configuration | |
| model = AutoModelForCausalLM.from_pretrained( | |
| config.base_model_name_or_path, | |
| return_dict=True, | |
| quantization_config=quantization_config, | |
| device_map='auto' | |
| ) | |
| # Load the tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) | |
| # Load the LoRA model | |
| model = PeftModel.from_pretrained(model, peft_model_id) | |
| # Example usage | |
| text = "Once upon a time" | |
| inputs = tokenizer(text, return_tensors='pt') | |
| outputs = model.generate(**inputs) | |
| generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print(generated_text) | |
| ``` | |
| ## Training Data | |
| The model was fine-tuned on a diverse set of texts to ensure robust performance across different domains. The dataset includes a mixture of publicly available text corpora, including: | |
| - Wikipedia | |
| - Books | |
| - News articles | |
| - Conversational data | |
| ## Evaluation | |
| The model was evaluated on several benchmarks to ensure its performance is up to standard. Below are some of the evaluation metrics: | |
| - Perplexity on common text datasets | |
| - Accuracy on specific language tasks | |
| - Performance on custom benchmarks relevant to specific use cases | |
| ## Limitations and Biases | |
| While `babelAI/opt-6.7b-lora` is a powerful model, it is important to be aware of its limitations: | |
| - The model can generate biased or inappropriate content, reflecting biases present in the training data. | |
| - It may not perform well on highly specialized or niche topics without further fine-tuning. | |
| ## Citation | |
| If you use this model in your research, please cite it as follows: | |
| ```bibtex | |
| @misc{babelAI2024opt67blora, | |
| author = {babelAI Team}, | |
| title = {babelAI/opt-6.7b-lora: A LoRA Fine-Tuned Model}, | |
| year = {2024}, | |
| howpublished = {\url{https://huggingface.co/babelAI/opt-6.7b-lora}}, | |
| } | |
| ``` | |
| ## License | |
| This model is licensed under the MIT License. | |
| ## Contact Information | |
| For more information or questions, please contact the babelAI team at [babel.ai.dub@gmail.com]. | |
| ``` | |
| ### Explanation: | |
| - **tags**: Keywords related to the model. | |
| - **license**: The license under which the model is distributed. | |
| - **datasets**: Datasets used to train the model. | |
| - **metrics**: Metrics used to evaluate the model. | |