Notus 7B - Details

Notus 7B is a large language model developed by Argilla, a company specializing in AI tools and frameworks. With 7b parameters, it is designed for high-quality interactions, making it suitable for complex conversational tasks. The model is released under the MIT License, ensuring open access and flexibility for users. Its focus on chat applications highlights its ability to deliver accurate and engaging responses.

Notus 7B is a collection of fine-tuned models leveraging Direct Preference Optimization (DPO) and RLHF techniques to enhance conversational quality. It is the first version in the series, trained on zephyr-7b-sft-full, a model used to develop zephyr-7b-beta, and it outperforms Zephyr-7B-beta and Claude 2 on AlpacaEval. Developed by Argilla, the model builds on HuggingFace H4 and MistralAI foundations. Designed for chat-like applications, it is evaluated through benchmarks like Chat (MT-Bench, AlpacaEval) and Academic (Open LLM Leaderboard) to ensure robust performance in interactive and scholarly tasks.

Notus 7B is a large language model with 7b parameters and a 4k context length. The 7b parameter size places it in the small to mid-scale range, offering a balance between performance and resource efficiency, making it suitable for moderate complexity tasks. The 4k context length allows for handling short to moderate-length texts but may limit its effectiveness for very long documents. These specifications imply that Notus 7B is optimized for applications requiring efficient processing without excessive computational demands, while still maintaining the capability to manage reasonably complex interactions.

• Name: Notus 7B
• Parameter Size: 7b
• Context Length: 4k
• Implications: Balances performance and efficiency for moderate tasks; suitable for short to moderate texts but limited for very long documents.

Notus 7B is a large language model designed for chat applications, benchmark evaluations, and research and development, with possible applications in areas like dialogue systems, performance testing, and experimental AI studies. Its 7b parameter size and 4k context length suggest it could support possible use cases such as interactive chatbots, comparative analysis of model capabilities, or exploratory work in natural language processing. However, these possible uses require thorough investigation to ensure alignment with specific goals and constraints. The model’s focus on conversational quality and benchmarking makes it a possible tool for refining AI interactions or advancing technical understanding, but its suitability for any task depends on further testing.

• chat applications
• benchmark evaluations
• research and development

Notus 7B is a large language model with possible applications in areas such as chat applications, benchmark evaluations, research and development, and content generation. Its 7b parameter size and 4k context length make it possible to support tasks like interactive dialogue systems, comparative model analysis, or experimental AI studies. It could also be possible to use for generating structured text, analyzing conversational patterns, or testing technical workflows. However, these possible uses require careful evaluation to ensure they align with specific requirements and constraints. Each application must be thoroughly assessed before deployment to confirm its suitability and effectiveness.

• chat applications
• benchmark evaluations
• research and development
• content generation

Notus 7B in its medium q4 version requires a GPU with at least 16GB VRAM for efficient operation, making it suitable for mid-range hardware. This quantization balances precision and performance, allowing possible use on systems with adequate VRAM and a multi-core CPU. Users should verify their GPU’s specifications and system memory (at least 32GB RAM) to ensure compatibility. The q4 version is possible to run on consumer-grade GPUs like the RTX 3090, but performance may vary based on workload.

• fp16, q2, q3, q4, q5, q6, q8

Notus 7B is a 7b-parameter large language model with a 4k context length, developed by Argilla, that leverages Direct Preference Optimization (DPO) and RLHF techniques to enhance conversational quality. It is designed for chat applications, benchmark evaluations, and research and development, released under the MIT License, making it a flexible tool for interactive and technical tasks.

Huggingface Model Page
Ollama Model Page