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Llama 3.1 Tulu 3 8B RM
Overview :
Llama-3.1-Tulu-3-8B-RM is part of the Tülu3 model family, distinguished by its open-source data, code, and recipes, aimed at delivering extensive insights into modern post-training techniques. This model offers state-of-the-art performance for a diverse range of tasks beyond chat, including MATH, GSM8K, and IFEval.
Target Users :
This model is targeted at researchers and developers, particularly those seeking advanced performance and the application of post-training techniques in the field of natural language processing. Its open-source nature makes it an ideal choice for education and research.
Use Cases
Researchers use the model to evaluate its mathematical problem-solving capabilities on the MATH benchmark.
Developers leverage the model's conversational template features to create interactive dialogue systems.
Educational institutions integrate the model into curricula for teaching and student projects.
Features
? Supports various tasks: In addition to chat capabilities, it can handle tasks such as MATH, GSM8K, and IFEval.
? Instruction following: The model is capable of understanding and executing user instructions.
? Open-source data and code: Provides fully open-source data and code for research and educational purposes.
? Post-training techniques: Utilizes modern post-training techniques like SFT, DPO, and RLVR.
? Multilingual support: Primarily supports English, but may include data in other languages.
? Model family: Part of the Llama 3.1 model family, sharing a technical foundation with other models of varying scales.
? Excellent performance: Demonstrates outstanding results across multiple benchmarks, including MMLU, PopQA, and TruthfulQA.
? Security considerations: While there is limited secure training, it can generate problematic outputs, particularly when prompted.
How to Use
1. Visit the Hugging Face model page and select the Llama-3.1-Tulu-3-8B-RM model.
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2. Load the model using the provided code snippet. For example, use the `AutoModelForSequenceClassification.from_pretrained` method.
3. Utilize the model for text classification or other NLP tasks based on your needs.
4. Follow the model's usage guidelines and community discussions to optimize its performance.
5. If needed, deploy the model through Hugging Face's Inference Endpoints.
6. Comply with the Llama 3.1 community license agreement and the usage terms for services like Google Gemma and Qwen.
7. When using the model in research or products, ensure to cite it according to the provided citation format.
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