Ghost 7B Alpha is a 7 billion parameter language model fine-tuned from Mistral 7B by Ghost X, with an 8192 token context length. It is optimized for reasoning ability, multi-task knowledge, and tool usage, performing well in both English and Vietnamese. This model is suitable for developing virtual assistants, coding support, translation, question answering, and document creation.
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Ghost 7B Alpha: A Multilingual Reasoning and Tool-Use Model
Ghost 7B Alpha, developed by Ghost X, is a 7 billion parameter language model fine-tuned from Mistral 7B. It features an 8192 token context length and is specifically optimized for enhanced reasoning ability, multi-task knowledge, and robust tool usage.
Key Capabilities
- Multilingual Support: Proficient in both English and Vietnamese, making it versatile for diverse applications.
- Tool Integration: Designed to effectively choose and utilize external tools, as demonstrated by its function calling capabilities for tasks like fetching stock information.
- Problem Solving: Excels at complex reasoning tasks, including logical puzzles and ethical dilemmas, providing structured and convincing answers.
- Code Generation: Capable of generating step-by-step coding instructions and explanations across various programming languages and frameworks (e.g., Node.js with Docker, Keras/TensorFlow for neural networks).
- Content Creation: Can assist in generating creative content and providing structured responses to open-ended questions.
Performance Highlights
Despite its 7B parameter size, Ghost 7B Alpha demonstrates competitive performance against larger models on benchmarks like MT-Bench and AlpacaEval. It achieved a score of 6.481250 on MT-Bench, outperforming models like Tulu-30B and Guanaco-65B, and a 70.440251% winrate on AlpacaEval, surpassing Vicuna-13B and Nous-Hermes-13B.
Good For
- Virtual Assistants: Its understanding of language, emotional empathy, and multi-tasking capabilities make it suitable for developing intelligent virtual assistants.
- RAG Products: Can be integrated into Retrieval-Augmented Generation (RAG) systems for customer care, internal document Q&A, and product information.
- Coding & Development: Useful for debugging, generating algorithms, and accelerating development workflows.
- Pre-training & Fine-tuning: Serves as an efficient and cost-effective base model for further fine-tuning on specific tasks and domains, potentially replacing larger, more expensive models like GPT-4 for certain use cases.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.