second-state/jina-embeddings-v2-base-code-GGUF

🤗 Hugging Face 来源feature-extractionapache-2.01.7 GBGGUF✓ 13 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo second-state/jina-embeddings-v2-base-code-GGUF ./model-folder
需要做种者 →

jina-embeddings-v2-base-code-GGUF

Original Model

jinaai/jina-embeddings-v2-base-code

Run with LlamaEdge

  • LlamaEdge version: v0.14.17

  • Prompt template

    • Prompt type: embedding
  • Context size: 8192

  • Embedding dim: 768

  • Run as LlamaEdge service

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:jina-embeddings-v2-base-code-f16.gguf \
      llama-api-server.wasm \
      --prompt-template embedding \
      --ctx-size 8192 \
      --model-name jina-embeddings-v2-base-code
    

Quantized GGUF Models

Name Quant method Bits Size Use case
jina-embeddings-v2-base-code-Q2_K.gguf Q2_K 2 82.7 MB smallest, significant quality loss - not recommended for most purposes
jina-embeddings-v2-base-code-Q3_K_L.gguf Q3_K_L 3 101 MB small, substantial quality loss
jina-embeddings-v2-base-code-Q3_K_M.gguf Q3_K_M 3 95.6 MB very small, high quality loss
jina-embeddings-v2-base-code-Q3_K_S.gguf Q3_K_S 3 89.8 MB very small, high quality loss
jina-embeddings-v2-base-code-Q4_0.gguf Q4_0 4 105 MB legacy; small, very high quality loss - prefer using Q3_K_M
jina-embeddings-v2-base-code-Q4_K_M.gguf Q4_K_M 4 109 MB medium, balanced quality - recommended
jina-embeddings-v2-base-code-Q4_K_S.gguf Q4_K_S 4 105 MB small, greater quality loss
jina-embeddings-v2-base-code-Q5_0.gguf Q5_0 5 119 MB legacy; medium, balanced quality - prefer using Q4_K_M
jina-embeddings-v2-base-code-Q5_K_M.gguf Q5_K_M 5 121 MB large, very low quality loss - recommended
jina-embeddings-v2-base-code-Q5_K_S.gguf Q5_K_S 5 119 MB large, low quality loss - recommended
jina-embeddings-v2-base-code-Q6_K.gguf Q6_K 6 134 MB very large, extremely low quality loss
jina-embeddings-v2-base-code-Q8_0.gguf Q8_0 8 173 MB very large, extremely low quality loss - not recommended
jina-embeddings-v2-base-code-f16.gguf f16 16 323 MB very large, extremely low quality loss - not recommended

Quantized with llama.cpp b4273