与「VC100」相关的搜索结果

この度、ドクターシーラボ VC100の ブランドアンバサダーに就任致しました🥹✨ まさか、スキンケア商品の アンバサダーに起用して頂けるとは😭😭 ただただ光栄です😭👏 3/4〜TVCMも放映開始です🥹✨ ▼TVCM(WEB先行公開中) #山本彩# #ドクターシーラボ# #VC100#

ドクターシーラボ✨ VC100のブランドアンバサダーに就任🎉 #VC100エッセンスローションEX# の新TVCMが、3月4日(月)より放映開始となりますのでお楽しみに😉 🔻TVCM（WEB先行公開中）🔻 🔻商品ページはこちら🔻 #山本彩# #ドクターシーラボ# #VC100#

4090 还是不够用 调了很多参数才看到这个 Finished，太不容易了 不过还只是试 10s 内的，试试 1 分钟以上看看 官方也是推荐 V100，那内存也比 4090 高 现在把模型都搞到 L40 上去试试

Here's a simple model I wrote yesterday (it learns to estimate a similarity metric between pairs of sequences). It runs with all backends -- no code changes. Tried PT -> trains at 24ms/step on V100. Tried JAX -> trains at 10ms/step on V100.

JAX is fast ⏩ Benchmarking Mistral 7B inference on a V100 (float16, batch_size 10): the throughput of the KerasNLP implementation with JAX is over 2x higher than the Hugging Face PyTorch one (compiled). Worth noting that this is "out of the box" performance: the KerasNLP model is not optimized for performance. It's written the way anyone would naively write a Keras 3 LLM.

A major advantage of using Keras 3 with the JAX backend: it's fast. And it's fast out of the box, without any need for careful performance optimization. Gemma 2B inference for a single prompt runs at 116 token/s on a V100, a 3.3x increase over the HF/PT implementation.

Added a new demo to minGPT that trains a GPT on pixels of CIFAR-10 images instead of text. Quite powerful that one can run the same training code/model on both domains. Notebook: . Produced reasonable samples after ~only 30 minutes on an 8-GPU V100 node: