Llama-3.1-Tulu-3-8B

Llama-3.1-Tulu-3-8B is part of the Tülu3 command compliance model family and is designed for diverse tasks, including chatting, math problem solving, GSM8K and IFEval, etc. This model family is known for its superior performance and fully open source data, code, and comprehensive guide to modern post-training techniques. The model is mainly in English and is fine-tuned based on the allenai/ Llama-3.1-Tulu-3-8B -DPO model.

Demand group:

"The target audience is researchers, developers and educators who can use this model for research and development on natural language processing tasks or as a teaching tool in education. It is particularly suitable due to the high performance and ease of use of the model. Scenarios that require handling complex language tasks and dialogue systems."

Example of usage scenario:

The researchers used the Llama-3.1-Tulu-3-8B model to conduct research on mathematical problem-solving tasks.

Developers use this model to create a chatbot for customer service.

Educators integrate models into teaching platforms to help students understand complex language problems.

Product features:

• Supports a variety of natural language processing tasks: The model is not only suitable for chatting, but can also handle tasks such as mathematical problems, GSM8K and IFEval.

• Open source data and code: Provides fully open source data and code for easy use in research and education.

• High performance: Excellent performance in multiple benchmark tests, such as MMLU, PopQA, TruthfulQA, etc.

• Easy to deploy: Can be easily loaded and deployed through the HuggingFace platform.

• Chat templates: Built-in chat templates facilitate conversational interactions.

• System prompts: Ai2 system prompts are used by default, but the model is not trained for specific system prompts.

• Security considerations: Although the model has limited security training, it may produce problematic output, especially when bootstrapped.

Usage tutorial:

1. Visit the HuggingFace platform and search for the Llama-3.1-Tulu-3-8B model.

2. Load the model using the provided code snippet: `from transformers import AutoModelForCausalLM; tulu_model = AutoModelForCausalLM.from_pretrained("allenai/ Llama-3.1-Tulu-3-8B ")`.

3. Select the appropriate fine-tuned model version, such as SFT or DPO, according to the required tasks.

4. Use the model to make predictions or generate text, such as chat responses or answers to math problems.

5. Adjust input parameters based on model output to optimize performance and results.

6. Follow the model usage guidelines and best practices to ensure that the model’s output meets expectations.

7. When using models in research or products, follow relevant license agreements and responsible use guidelines.