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Higgs Llama 3 70B
Overview :
Higgs-Llama-3-70B is a fine-tuned model based on Meta-Llama-3-70B, specifically optimized for role-playing, while maintaining competitive performance in general-purpose instruction execution and reasoning. The model is trained using supervised fine-tuning, combining annotations from human labelers and a private large language model to build preference datasets and iteratively optimize its behavior to align with system messages. Compared to other instruction-following models, the Higgs model more closely follows its assigned role.
Target Users :
This model is designed for developers and businesses looking to utilize a language model in role-playing or dialogue generation scenarios. It is suitable for them because it offers a model optimized for role-playing, capable of providing more natural and coherent conversations while maintaining competitive performance in general-purpose instruction execution and reasoning.
Use Cases
Developers can leverage this model to create chatbots with distinct character traits.
Businesses can integrate this model into customer service to provide more personalized service experiences.
The education sector can utilize this model to simulate specific roles, enhancing learning interactivity.
Features
Role-Playing Optimization: Designed for role-playing scenarios, providing a more natural interaction experience.
General Purpose Instruction Execution: Capable of understanding and executing user instructions across a wide range of domains.
Reasoning Ability: Possesses strong logical reasoning capabilities, enabling it to handle complex queries and questions.
Iterative Preference Optimization: Through iterative optimization, the model's behavior becomes more aligned with expectations.
System Message Alignment: The model's behavior closely aligns with system messages, ensuring consistency in role-playing.
Large Language Model: Boasts 70.6B parameters, providing powerful language understanding and generation capabilities.
Multi-Benchmark Test Performance: Demonstrates excellent performance on benchmarks such as MMLU-Pro and Arena-Hard.
How to Use
1. Import the necessary libraries: `import transformers` and `import torch`.
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2. Set the model ID to `bosonai/Higgs-Llama-3-70B`.
3. Create a text generation pipeline, specifying the model ID and parameters.
4. Prepare the conversation messages, including system role and user role messages.
5. Use the pipeline's tokenizer to apply the chat template and prepare the prompt.
6. Invoke the pipeline to generate text, setting parameters such as maximum new tokens and end token ID.
7. Print the generated text to view the model's output.
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