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gte-Qwen2-1.5B-instruct-GGUF

Original Model

Alibaba-NLP/gte-Qwen2-1.5B-instruct

Run with LlamaEdge

• LlamaEdge version: v0.12.2 and above

• Prompt template

  • Prompt type: embedding

• Context size: 32000

• Run as LlamaEdge service

  wasmedge --dir .:. --nn-preload default:GGML:AUTO:gte-Qwen2-1.5B-instruct-Q5_K_M.gguf \
    llama-api-server.wasm \
    --prompt-template embedding \
    --ctx-size 32000 \
    --model-name gte-Qwen2-1.5B-instruct
  

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
gte-Qwen2-1.5B-instruct-Q2_K.gguf	Q2_K	2	752 MB	smallest, significant quality loss - not recommended for most purposes
gte-Qwen2-1.5B-instruct-Q3_K_L.gguf	Q3_K_L	3	980 MB	small, substantial quality loss
gte-Qwen2-1.5B-instruct-Q3_K_M.gguf	Q3_K_M	3	924 MB	very small, high quality loss
gte-Qwen2-1.5B-instruct-Q3_K_S.gguf	Q3_K_S	3	861 MB	very small, high quality loss
gte-Qwen2-1.5B-instruct-Q4_0.gguf	Q4_0	4	1.07 GB	legacy; small, very high quality loss - prefer using Q3_K_M
gte-Qwen2-1.5B-instruct-Q4_K_M.gguf	Q4_K_M	4	1.12 GB	medium, balanced quality - recommended
gte-Qwen2-1.5B-instruct-Q4_K_S.gguf	Q4_K_S	4	1.07 GB	small, greater quality loss
gte-Qwen2-1.5B-instruct-Q5_0.gguf	Q5_0	5	1.26 GB	legacy; medium, balanced quality - prefer using Q4_K_M
gte-Qwen2-1.5B-instruct-Q5_K_M.gguf	Q5_K_M	5	1.28 GB	large, very low quality loss - recommended
gte-Qwen2-1.5B-instruct-Q5_K_S.gguf	Q5_K_S	5	1.26 GB	large, low quality loss - recommended
gte-Qwen2-1.5B-instruct-Q6_K.gguf	Q6_K	6	1.46 GB	very large, extremely low quality loss
gte-Qwen2-1.5B-instruct-Q8_0.gguf	Q8_0	8	1.89 GB	very large, extremely low quality loss - not recommended
gte-Qwen2-1.5B-instruct-f16.gguf	f16	8	3.56 GB	very large, extremely low quality loss - not recommended

Quantized with llama.cpp b3259