BAAI
BGE M3
当前
31.0M下载量3.1K点赞Jan 2024发布日期8K tokens上下文MIT许可证84 优秀质量
BGE M3 (0.5680000185966492B parameters) requires approximately 4.1 GB of VRAM with F16 quantization. For the best balance of quality and speed, we recommend hardware with at least 5 GB of VRAM.
快速开始
— 复制粘贴即可本地运行Copy-paste commands to run BGE M3 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "BAAI/bge-m3" \
--hf-file "bge-m3-F16.gguf" \
-c 4096 -ngl 99Quick specs
Parameters0.57B
Architecturedense
Context8K tokens
Modalityembedding
Min RAM0.2 GB
Rec. RAM1.2 GB (F16)
LicenseMIT
FamilyBGE
✓ RAG
About this model
- •Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector...
- •Multi-Linguality: It can support more than 100 working languages
- •Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens
相关模型
快速推荐
最佳硬件
BGE M3 的最佳选择
运行此模型
量化选项
各量化级别的 VRAM 估算
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | — |
Q3_K_S | 3 | 0.3 GB | Low | — |
NVFP4 | 4 | 0.3 GB | Medium | — |
Q4_K_M | 4 | 0.3 GB | Medium | — |
Q5_K_M | 5 | 0.4 GB | High | — |
Q6_K | 6 | 0.5 GB | High | — |
Q8_0 | 8 | 0.6 GB | Very High | — |
F16 | 16 | 1.2 GB | Maximum | — |
硬件兼容性
全部硬件的适配估算
Computing compatibility...
内存详细分析
Reference: RTX 2060 6GB
Weights1.2 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom0.6 GB
常见问题
FAQ — BGE M3
另请参阅