Mixtral 8*22B 官方博客,huggingface 开源模型, 架构:架构与 mixtral 8*7B 架构一样,在 huggingface 中使用的都是MixtralForCausalLM ,但 22B 的各方面参数大一点,比较特别的是 context window 从 32k 升级到了 65k, vocab_size 也更大一些。 支持function calling,不过好像没有透露具体的 function calling ...
6. Skywork-13B中文数据集，由昆仑万维全球开源，推进AI新纪元。7. Mistral 7B模型挑战AI新标准，全面超越Llama 2 13B。8. Google Gemini与OpenAI GPT-4对比，探索AI巨头间的实力。9. Kandinsky-3模型，解析AI技术的创新与性能。10. SDXL Turbo，实现实时图像生成的技术突破。11. HuggingFace镜像站解...
Step 1: Clone the preview branch of the swift-transformers repo: git clone -b preview https://github.com/huggingface/swift-transformers Step 2: Download the converted Core ML models from this Hugging Face repo Step 3: Run inference using Swift: swift run transformers "Best recommendati...
Step 1: Clone the preview branch of the swift-transformers repo: git clone -b preview https://github.com/huggingface/swift-transformers Step 2: Download the converted Core ML models from this Hugging Face repo Step 3: Run inference using Swift: swift run transformers "Best recommendations ...
除了直接从HuggingFace上下载权重,用户可以通过官方API平台la Plateforme访问或微调模型,免费聊天机器人le chat也已经部署了Mistral Large 2。 Vertex AI、Azure Studio等第三方云平台也托管了Mistral Large 2的API。 参考资料: https://mistral.ai/news/mistral-large-2407/...
HuggingFace地址:https://huggingface.co/mistralai/Mistral-Large-Instruct-2407 不仅上下文窗口从上一代的32k增长到了128k(同Llama 3.1),而且有强大的多语言能力,支持数十种自然语言以及80多种编程语言。 令人印象深刻的是,Mistral Large的预训练版本在MMLU上的准确率可以达到84%。
除了直接从HuggingFace上下载权重,用户可以通过官方API平台la Plateforme访问或微调模型,免费聊天机器人le chat也已经部署了Mistral Large 2。 Vertex AI、Azure Studio等第三方云平台也托管了Mistral Large 2的API。 参考资料: https://mistral.ai/news/mistral-large-2407/...
Step 1: Clone the preview branch of the swift-transformers repo: git clone -b preview https://github.com/huggingface/swift-transformers Step 2: Download the converted Core ML models from this Hugging Face repo Step 3: Run inference using Swift: swift run transformers "Best recommendations fo...
Step 1: Clone the preview branch of the swift-transformers repo: git clone -b preview https://github.com/huggingface/swift-transformers Step 2: Download the converted Core ML models from this Hugging Face repo Step 3: Run inference using Swift: swift run transformers "Best recommendations ...
2b-bnb-4bit", "unsloth/gemma-2b-it-bnb-4bit", # Instruct version of Gemma 2b "unsloth/llama-3-8b-bnb-4bit", # [NEW] 15 Trillion token Llama-3 "unsloth/Phi-3-mini-4k-instruct-bnb-4bit", ] # More models at https://huggingface.co/unsloth model, tokenizer = FastLanguageModel...