BAAI
BGE Large EN v1.5
現行
15.0Mダウンロード674いいねSep 2023公開日1K トークンコンテキストMITライセンス74 優秀品質
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.
はじめに
— コピー&ペーストでローカル実行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
関連モデル
おすすめ
最適なハードウェア
BGE Large EN v1.5のおすすめ
このモデルを実行
量子化オプション
量子化レベル別VRAM推定値
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 | — |
ハードウェア互換性
全ハードウェアの適合度推定
Computing compatibility...
メモリ内訳
Reference: RTX 2060 6GB
Weights0.7 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom0.6 GB
よくある質問
FAQ — BGE Large EN v1.5
関連項目