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Published several new, ground-breaking models |
- Wrapped Fuyu-8B, an open-source, simplified multimodal architecture with a decoder-only transformer, supporting arbitrary image resolutions, and excelling in diverse applications, including question answering and complex visual understanding.
- Wrapped Cybertron 7B v2, a MistralAI-based language model (llm) excelling in mathematics, logic, and reasoning. It consistently ranks #1 in its category on the HF LeaderBoard, enhanced by the innovative Unified Neural Alignment (UNA) technique.
- Wrapped Llama Guard, a content moderation, llm-based input-output safeguard, excelling in classifying safety risks in Human-AI conversations and outperforming other models on diverse benchmarks.
- Wrapped StripedHyena-Nous-7B, an innovative hybrid chat llm, featuring multi-head attention and gated convolutions, outperforms Transformers in long-context summarization with notable efficiency improvements in training and inference.
- Wrapped Imagen 2, a cutting-edge text-to-image llm, offering high-quality, multilingual image generation with advanced features, including improved text rendering, logo generation, and safety measures.
- Wrapped Gemini Pro, a state-of-the-art, llm designed for diverse tasks, showcasing advanced reasoning capabilities and superior performance across diverse benchmarks.
- Wrapped Mixtral 8x7B, a high-quality, Sparse Mixture-of-Experts (SMoE) llm model, excelling in efficiency, multilingual support, and competitive performance across diverse benchmarks.
- Wrapped OpenChat-3.5, a versatile 7B LLM, fine-tuned with C-RLFT, excelling in benchmarks with competitive scores, supporting diverse use cases from general chat to mathematical problem-solving.
- Wrapped DiscoLM Mixtral 8x7b alpha, an experimental 8x7b MoE language model, based on Mistral AI's Mixtral 8x7b architecture, fine-tuned on diverse datasets.
- Wrapped SOLAR-10.7B-Instruct, a powerful 10.7 billion-parameter LLM with a unique depth up-scaling architecture, excelling in single-turn conversation tasks through advanced instruction fine-tuning methods.
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