💻 programming

Llama-3.1-Tulu-3-70B-DPO

The leading family of instructional follower models, providing open source data, code, and recipes.

#natural language processing
#text generation
#Open source model
#Dialogue system
#post training techniques
Llama-3.1-Tulu-3-70B-DPO

Product Details

Llama-3.1-Tulu-3-70B-DPO is part of the Tülu3 family of models designed to provide a comprehensive guide to modern post-training techniques. This family of models aims to achieve state-of-the-art performance on a variety of tasks beyond chatting, such as MATH, GSM8K and IFEval. It is based on models trained on publicly available, synthetic and human-created datasets, is primarily in English, and is licensed under the Llama 3.1 Community License.

Main Features

1
Supports a variety of tasks, including math, question and answer, and assessment tasks.
2
Completely open source data, code, and recipes for easy use in research and education.
3
The model is fine-tuned and based on allenai/Llama-3.1-Tulu-3-70B-SFT.
4
Code examples for loading models are provided to facilitate developers to get started quickly.
5
Supports VLLM service and can easily deploy models.
6
Built-in chat templates facilitate conversational interactions.
7
The default system prompt defines the identity and purpose of the model.

How to Use

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1. Visit the Hugging Face model page to learn about the basic information and performance indicators of the model.
2
2. Use Python to load the model into the local environment according to the code example provided on the page.
3
3. Use the model’s API for text generation or other NLP tasks.
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4. If you need to deploy the model, you can follow the guidance of the VLLM service.
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5. Adjust the system prompts as needed to suit specific application scenarios.
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6. Participate in community discussions and obtain technical support and best practices.
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7. Comply with the model’s license agreement to ensure legal and compliant use of the model.

Target Users

The target audience is researchers, developers, and educators who can leverage this model for natural language processing tasks, especially in scenarios that require instruction following and conversational capabilities. Due to the open source nature of the model, it is also suitable for those who want to use advanced models for teaching and learning in educational settings.

Examples

The researchers used the model to evaluate its mathematical problem-solving abilities on the MATH benchmark.

The developer used the model's chat template feature to create an interactive customer service chatbot.

Educators integrate the model into teaching platforms to provide personalized learning support and answer questions.

Quick Access

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