$0.00117
$0.00117
Guanaco-65B: Powerful open-source chatbot model, competitive with ChatGPT 3.5 Turbo.
The Guanaco-65B is a 65 billion parameter open-source chatbot model developed by Tim Dettmers. It is created by applying 4-bit QLoRA finetuning to the LLaMA base model using the OASST1 dataset. The model demonstrates the potential of QLoRA technology, achieving performance comparable to top commercial chatbots like ChatGPT and BARD.
The Guanaco-65B is designed for use as a powerful open-source chatbot model, enabling developers and researchers to experiment with and deploy high-performance conversational AI systems. It can be used for tasks such as:
The Guanaco-65B is a multilingual model, but the OASST1 dataset it was trained on is heavily weighted towards high-resource languages. The model likely performs best on English and other high-resource languages.
The Guanaco-65B uses a LoRA (Low-Rank Adaptation) architecture, with adapter weights added to all layers of the LLaMA base model. This allows for efficient finetuning while preserving the base model's capabilities.
The model was trained on the OASST1 dataset, which is multilingual but skewed towards high-resource languages. The exact size and diversity of the dataset is not publicly reported.
The knowledge cutoff date for the Guanaco-65B model is not publicly specified. It likely reflects the date of the OASST1 dataset used for finetuning.
According to the model's documentation, the Guanaco-65B achieves performance at 99.3 percent of ChatGPT-3.5 Turbo on the Vicuna benchmarks, as evaluated by both human raters and GPT-4.
No specific ethical guidelines are provided for the Guanaco-65B model. As an open-source model, it is up to developers to use it responsibly and consider potential misuse.
The Guanaco adapter weights are licensed under Apache 2.0. However, using the model requires access to the LLaMA base model weights, which have more restrictive licensing terms.In summary, the Guanaco-65B is a powerful open-source chatbot model that rivals commercial offerings like ChatGPT, while demonstrating the potential of efficient 4-bit QLoRA finetuning. It provides an affordable and replicable path to high-performance conversational AI.