---

E5-Mistral-7B-Instruct-Embedding-GGUF

Original Model

intfloat/e5-mistral-7b-instruct

Run with LlamaEdge

• LlamaEdge version: v0.8.2 and above

• Prompt template

  • Prompt type: embedding

• Context size: 4096

• Run as LlamaEdge service

  wasmedge --dir .:. --nn-preload default:GGML:AUTO:e5-mistral-7b-instruct-Q5_K_M.gguf \
    llama-api-server.wasm \
    --prompt-template embedding \
    --ctx-size 4096 \
    --model-name e5-mistral-7b-instruct
  

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
e5-mistral-7b-instruct-Q2_K.gguf	Q2_K	2	2.72 GB	smallest, significant quality loss - not recommended for most purposes
e5-mistral-7b-instruct-Q3_K_L.gguf	Q3_K_L	3	3.82 GB	small, substantial quality loss
e5-mistral-7b-instruct-Q3_K_M.gguf	Q3_K_M	3	3.52 GB	very small, high quality loss
e5-mistral-7b-instruct-Q3_K_S.gguf	Q3_K_S	3	3.16 GB	very small, high quality loss
e5-mistral-7b-instruct-Q4_0.gguf	Q4_0	4	4.11 GB	legacy; small, very high quality loss - prefer using Q3_K_M
e5-mistral-7b-instruct-Q4_K_M.gguf	Q4_K_M	4	4.37 GB	medium, balanced quality - recommended
e5-mistral-7b-instruct-Q4_K_S.gguf	Q4_K_S	4	4.14 GB	small, greater quality loss
e5-mistral-7b-instruct-Q5_0.gguf	Q5_0	5	5.00 GB	legacy; medium, balanced quality - prefer using Q4_K_M
e5-mistral-7b-instruct-Q5_K_M.gguf	Q5_K_M	5	5.13 GB	large, very low quality loss - recommended
e5-mistral-7b-instruct-Q5_K_S.gguf	Q5_K_S	5	5.00 GB	large, low quality loss - recommended
e5-mistral-7b-instruct-Q6_K.gguf	Q6_K	6	5.94 GB	very large, extremely low quality loss
e5-mistral-7b-instruct-Q8_0.gguf	Q8_0	8	7.7 GB	very large, extremely low quality loss - not recommended
e5-mistral-7b-instruct-f16.gguf	f16	8	14.5 GB	very large, extremely low quality loss - not recommended

Quantized with llama.cpp b2334