Llama-3.1-Tulu-3-8B

An advanced instruction-following model that provides open-source data and code.

CommonProductProgrammingNatural Language ProcessingText Generation

Llama-3.1-Tulu-3-8B is part of the Tülu3 instruction-following model family, designed for a variety of tasks including chat, solving mathematical problems, GSM8K, and IFEval. This model family is renowned for its superior performance and comprehensive guides on fully open-source data, code, and modern post-training techniques. The model is primarily in English and is fine-tuned based on the allenai/Llama-3.1-Tulu-3-8B-DPO model.

• Supports a range of natural language processing tasks: The model is not only suitable for chat but also excels in tasks like mathematical problem solving
GSM8K
and IFEval.
• Open-source data and code: Provides fully open-source data and code for research and educational use.
• High performance: Demonstrates outstanding performance across various benchmarks
such as MMLU
PopQA
and TruthfulQA.
• Easy deployment: Can be easily loaded and deployed via the HuggingFace platform.
• Chat template: Comes with built-in chat templates to facilitate conversational interactions.

\This model targets researchers
developers
and educators who can utilize it for natural language processing tasks in research and development
or as a teaching tool in educational settings. Its high performance and user-friendliness make it particularly suitable for scenarios requiring complex language tasks and conversational systems.\

Researchers use the Llama-3.1-Tulu-3-8B model for studies in mathematical problem-solving.
Developers utilize the model to create a chatbot for customer service.
Educators integrate the model into teaching platforms to help students understand complex language issues.

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. Choose the appropriate fine-tuned model version for your task
such as SFT or DPO.
4. Use the model for predictions or to generate text

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