HyperbeeAI/Tulpar-7b-v2
HyperbeeAI/Tulpar-7b-v2 is a 7 billion parameter language model developed by HyperbeeAI, based on the Mistral-7b architecture. It is instruction-tuned on a filtered and preprocessed dataset, including GPT-4 generated and curated datasets like Airoboros and Platypus. This model is designed for general-purpose instruction following, excelling in tasks requiring responses based on diverse, high-quality instruction data. It has a context length of 4096 tokens and is primarily finetuned for English language applications.
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Tulpar-7b-v2: An Instruction-Tuned Mistral-7b Model
Tulpar-7b-v2 is a 7 billion parameter language model developed by HyperbeeAI, built upon the Mistral-7b architecture. This model has undergone instruction finetuning using a carefully curated and preprocessed dataset. The training data incorporates high-quality sources, including GPT-4 generated content and established datasets such as Airoboros and Platypus, aiming to enhance its ability to follow instructions effectively.
Key Capabilities
- General Instruction Following: Optimized to understand and execute a wide range of user instructions.
- Diverse Knowledge Base: Benefits from a training dataset that includes varied and high-quality instruction examples.
- English Language Focus: Primarily finetuned for tasks and interactions in English.
Ethical Considerations and Limitations
As highlighted by HyperbeeAI, Tulpar-7b-v2 is finetuned exclusively in English, meaning its performance in other languages or multilingual scenarios is not guaranteed. Users are advised to conduct thorough safety tests for their specific use cases, as the model's outputs are not guaranteed to be ethical, accurate, unbiased, or objective. The model's context length is 4096 tokens.