zhengchenphd/ICE-GRT
ICE-GRT is a 13 billion parameter chat assistant developed by zhengchenphd, built upon the Vicuna model backbone and enhanced through Reinforcement Learning from Human Feedback (RLHF). This model specializes in instruction context enhancement, demonstrating versatility across general language tasks and domain-specific applications like code generation, multi-round dialogue, and multilingual responses. It is primarily intended for non-commercial research and development in large language models and chatbots.
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ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers
ICE-GRT is a 13 billion parameter chat assistant developed by zhengchenphd, utilizing the lmsys/vicuna-13b model as its foundational backbone. It has been significantly improved through Reinforcement Learning from Human Feedback (RLHF), building on prior work in Instruction Context Enhancement (ICE-Instruct).
Key Capabilities
- Enhanced Chat Performance: Trained with RLHF to provide more coherent and contextually relevant responses, particularly when guided by a specific prompt format.
- Domain-Specific Versatility: Demonstrates proficiency across a range of specialized tasks including:
- Poem Generation
- Text-to-Table conversions
- Engaging Multiple Round Dialogue
- Accurate Chemistry Responses
- Proficient Code Generation (e.g., Python functions, word2vec implementation)
- Tailored Ads Text Generation and Labeling
- Multilingual Support: Capable of understanding and generating responses in multiple languages, as shown with Chinese language tasks.
Intended Use and Licensing
ICE-GRT is strictly limited to non-commercial, non-revenue generating, and research purposes only, aligning with the license of its Vicuna backbone. Researchers are advised to use the specified prompt format: "Below is an instruction that describes a task. Write a response that appropriately completes the request. ### USER: {input} ASSISTANT: " to maximize model effectiveness. The project aims to foster 'ice-breaking' advancements in LLM research, encouraging exploration of its broad capabilities.