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AI 模型 183

• Qwen-Image-2512↗

  阿里巴巴 · Qwen

  💜 Qwen Chat &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Tech Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp 🖥️ Demo &nbsp&nbsp | &nbsp&nbsp💬 WeChat (微信) &nbsp&nbsp | &nbsp&nbsp🫨 Discord &nbsp&nbsp| &nbsp&nbsp Github &nbsp&nbsp We are excited to introduce Qwen-Image-2512, the December update of Qwen-Image’s text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base Qwen-Image model released in August, Qwen-Image-2512 features the following key improvements:

• Meta-Llama-3.1-8B-Instruct-FP8↗

  RedHatAI

  - **Model Architecture:** Meta-Llama-3.1 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Meta-Llama-3.1-8B-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 7/23/2024 - **Version:** 1.0 - **License(s):** llama3.1 - **Model Developers:** Neural Magic Quantized version of Meta-Llama-3.1-8B-Instruct. It achieves an average score of 73.44 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 73.79. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.

• gemma-scope-2↗

  Google