babelAI/opt-6.7b-lora

---
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:

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:

@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 [[email protected]].

### 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.