👁 BAAI
BAAI
BGE M3
Current
31.4MDownloads3.2KLikesJan 2024Released8K tokensContextMITLicense84 StrongQuality
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.
Get started
— copy & paste to run locallyCopy-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
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Quantization options
VRAM estimates by quant level
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 | — |
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights1.2 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — BGE M3
See also