BAAI
BGE Large EN v1.5
当前
15.0M下载量674点赞Sep 2023发布日期1K tokens上下文MIT许可证74 优秀质量
BGE Large EN v1.5 (0.33500000834465027B parameters) requires approximately 4.0 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 Large EN v1.5 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "BAAI/bge-large-en-v1.5" \
--hf-file "bge-large-en-v1.5-F16.gguf" \
-c 4096 -ngl 99Quick specs
Parameters0.34B
Architecturedense
Context1K tokens
Modalityembedding
Min RAM0.1 GB
Rec. RAM0.7 GB (F16)
LicenseMIT
FamilyBGE
✓ RAG
About this model
- •1/9/2024: Release Activation-Beacon, an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM....
- •12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code...
- •11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical...
- •10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Technical Report
- •09/15/2023: The technical report and massive training data of BGE has been released
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最佳硬件
BGE Large EN v1.5 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 | 0.1 GB | Low | — |
Q3_K_S | 3 | 0.2 GB | Low | — |
NVFP4 | 4 | 0.2 GB | Medium | — |
Q4_K_M | 4 | 0.2 GB | Medium | — |
Q5_K_M | 5 | 0.2 GB | High | — |
Q6_K | 6 | 0.3 GB | High | — |
Q8_0 | 8 | 0.4 GB | Very High | — |
F16 | 16 | 0.7 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
Weights0.7 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom0.6 GB
常见问题
FAQ — BGE Large EN v1.5
另请参阅