Let's build this graph together! We have tried our best to find the specialized models, but we can definitely do more with your participation!
🔗 Octopus-v4 on Hugging Face
🏆Open LLM Leaderboard for domains
This project aims to build the world's largest graph of language models. To our knowledge, it is the first attempt to construct such a graph. Have a look at our design demo. In this graph, we will integrate many different specialized models and train the respective Octopus models for the edges between the nodes to help distribute and deliver information effectively. We wish to unit all open source language models to deliver the performance that can compete with closed source models.
The project is still in its early stages, and we have only included the very initial Octopus model. However, at Nexa AI, we are committed to dedicating significant time and resources to create a powerful graph of AI models.
The project will mainly focus on the following aspects:
- Identifying the specialized models needed and training these models.
- Constructing the graph consisting of multiple specialized models as nodes.
- Training the Octopus models to connect different nodes efficiently.
The file structure of this GitHub repository is organized as follows:
main.py: This is the primary script for running the Octopus v4 model.build_graph: Contains methods for constructing and managing the graph of language models. This includes operations such as creating, updating, and deleting nodes and edges.specialized_models: Here, you'll find the training code along with a tutorial on how to prepare your data and train the specialized models. We provide code based on Hugging Face Transformers TRL library, to facilitate your training process. Feel free to raise any issues or questions you encounter during training.specialized_models_inference: Here, you can find the inference code for the specialized models. This code is used to work with octopus-v4 model through the graph of language models, the entrance is thespecialized_infer.pyfile.
We recommend using a Linux environment and assume that you have an NVIDIA GPU when contributing to the project. To set up the project, follow these steps:
conda create -n octopus4 python=3.10
pip3 install torch torchvision torchaudio
pip3 install transformers datasets accelerate peftMake sure to install PyTorch first, followed by the other packages. We recommend to install torchvision and torchaudio as well since we will introduce multimodal AI agent in the future. Alternatively, you can create a dev environment using our Docker image. For more information on setting up a dev environment, refer to this YouTube video. And you can use our Dockerfile to build the image.
docker build -t octopus4 .
docker run --gpus all -p 8700:8700 octopus4Otherwise, you can directly pull our docker image
docker pull nexaai/octopus4
Our initial v4 model is customized for the MMLU benchmark. However, we plan to support real-world use cases in the future. The Octopus v4 model helps you find the most appropriate model to finish your task and reformats your query so that the worker model can process it effectively. In a graph setup, it knows the best neighbor to choose and how to message from one node to another.
Here's an example of the result for Octopus v4 model:
Query: Tell me the result of derivative of x^3 when x is 2?
<nexa_4>('Determine the derivative of the function f(x) = x^3 at the point where x equals 2, and interpret the result within the context of rate of change and tangent slope.')
<nexa_end>
In this use case, <nexa_4> is the special token representing the math GPT. The natural math question is converted into a professional math expression to facilitate better understanding by the worker model. To try our model, you can use python main.py to run the code to try the Octopus v4 model.
The respective models used in our experiments are as follows:
We leverage the latest Language Large Models for a variety of domains. Below is a summary of the chosen models for each category. In cases where no specialized model exists for a subject, we utilize generic models like Llama3-8b. You may consider to add more content to our table below. Nexa AI will create another leaderboard for the specialized model.
| Model | Category | Subjects |
|---|---|---|
jondurbin/bagel-8b-v1.0 |
Biology | college_biology, high_school_biology |
Weyaxi/Einstein-v6.1-Llama3-8B |
Physics | astronomy, college_physics, conceptual_physics, high_school_physics |
meta-llama/Meta-Llama-3-8B-Instruct |
Business | business_ethics, management, marketing |
meta-llama/Meta-Llama-3-8B-Instruct |
Chemistry | college_chemistry, high_school_chemistry |
abacusai/Llama-3-Smaug-8B |
Computer Science | college_computer_science, computer_security, high_school_computer_science, machine_learning |
Open-Orca/Mistral-7B-OpenOrca |
Math | abstract_algebra, college_mathematics, elementary_mathematics, high_school_mathematics, high_school_statistics |
meta-llama/Meta-Llama-3-8B-Instruct |
Economics | econometrics, high_school_macroeconomics, high_school_microeconomics |
AdaptLLM/medicine-chat |
Health | anatomy, clinical_knowledge, college_medicine, human_aging, medical_genetics, nutrition, professional_medicine, virology |
STEM-AI-mtl/phi-2-electrical-engineering |
Engineering | electrical_engineering |
meta-llama/Meta-Llama-3-8B-Instruct |
Philosophy | formal_logic, logical_fallacies, moral_disputes, moral_scenarios, philosophy, world_religions |
microsoft/Phi-3-mini-128k-instruct |
Other | global_facts, miscellaneous, professional_accounting |
meta-llama/Meta-Llama-3-8B-Instruct |
History | high_school_european_history, high_school_us_history, high_school_world_history, prehistory |
meta-llama/Meta-Llama-3-8B-Instruct |
Culture | human_sexuality, sociology |
AdaptLLM/law-chat |
Law | international_law, jurisprudence, professional_law |
meta-llama/Meta-Llama-3-8B-Instruct |
Psychology | high_school_psychology, professional_psychology |
Here are the comparative MMLU scores for various models tested under a 5-shot learning setup:
| Model | MMLU Score |
|---|---|
| Octopus-V4 | 74.6% |
| GPT-3.5 | 70.0% |
| Phi-3-mini-128k-instruct | 68.1% |
| OpenELM-3B | 26.7% |
| Lamma3-8b-instruct | 68.4% |
| Gemma-2b | 42.3% |
| Gemma-7b | 64.3% |
Explore our collection of domain-specific large language models (LLMs) or contribute by suggesting new models tailored to specific domains. For detailed information on available models and to engage with our community, please visit our Domain LLM Leaderboard.
For instructions on training specialized models, please refer to the specialized_models directory. We currently support training using Hugging Face TRL, chosen for its convenience and robustness in training specialized models. Future updates will expand support to include LoRA training, training larger models (such as 13B and 70B), distributed training, and more. Stay tuned for these enhancements.
To develop your specialized model effectively, we suggest the following steps:
- Data Collection and Preparation: Collect a dataset specific to your domain. Process this dataset to ensure it is clean and free from inappropriate content.
- Model Training: Train your model using the Supervised Fine-Tuning (SFT) method.
- DPO Training: Prepare a dataset for Direct Preference Optimization (DPO), and use the DPO to train your model.