Unverified50% confidenceFactExact time
Different embedding models have different output dimensions: paraphrase-multilingual-MiniLM-L12-v2 is 384 dimensions, the BERT-base series is 768 dimensions, OpenAI text-embedding-3-small is 1536 dimensions, and text-embedding-3-large and Google gemini-embedding-001 are 3072 dimensions.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/13/2026
First Seen
Valid until: 1/11/2027
Sources
Related Entities
Related Claims
UnverifiedMeta的Llama 3.1提供8B/70B/405B尺寸,Google的Gemma系列提供2B/7B/27B尺寸66% similarUnverifiedThe context length of mainstream open-source multimodal models is typically between 4K and 32K tokens: LLaVA-1.5 is 4,096 tokens, Qwen-VL is 8,192 tokens, and InternVL2 extends to 32K.65% similarUnverifiedOpenAI's text-embedding-3 or the open-source BGE-M3 can generate dense vectors of 1536 dimensions or more64% similarUnverified现代大语言模型通常使用768维、1024维甚至更高维度的嵌入64% similarUnverifiedd1-omni-600M 基于 LFM2.5-Encoder-350M 双向编码器构建,而非在因果解码器上接分类头,上下文窗口为 16,384 tokens。64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1014325API
curl https://kongchang.com/api/v1/knowledge/claims/1014325MCP
get_claim(id=1014325)