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Original Model


Run with LlamaEdge

  • LlamaEdge version: v0.2.8 and above

  • Prompt template

    • Prompt type: phi-2-instruct

    • Prompt string

      Instruct: <prompt>\nOutput:
  • Context size: 2560

  • Run as LlamaEdge command app

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:phi-2-Q5_K_M.gguf llama-chat.wasm -p phi-2-instruct

    Note that phi-2 here is only used as an instruct model, instead of a chat model.

Quantized GGUF Models

Name Quant method Bits Size Use case
phi-2-Q2_K.gguf Q2_K 2 1.11 GB smallest, significant quality loss - not recommended for most purposes
phi-2-Q3_K_L.gguf Q3_K_L 3 1.58 GB small, substantial quality loss
phi-2-Q3_K_M.gguf Q3_K_M 3 1.43 GB very small, high quality loss
phi-2-Q3_K_S.gguf Q3_K_S 3 1.25 GB very small, high quality loss
phi-2-Q4_0.gguf Q4_0 4 1.60 GB legacy; small, very high quality loss - prefer using Q3_K_M
phi-2-Q4_K_M.gguf Q4_K_M 4 1.74 GB medium, balanced quality - recommended
phi-2-Q4_K_S.gguf Q4_K_S 4 1.63 GB small, greater quality loss
phi-2-Q5_0.gguf Q5_0 5 1.93 GB legacy; medium, balanced quality - prefer using Q4_K_M
phi-2-Q5_K_M.gguf Q5_K_M 5 2.00 GB large, very low quality loss - recommended
phi-2-Q5_K_S.gguf Q5_K_S 5 1.93 GB large, low quality loss - recommended
phi-2-Q6_K.gguf Q6_K 6 2.29 GB very large, extremely low quality loss
phi-2-Q8_0.gguf Q8_0 8 2.96 GB very large, extremely low quality loss - not recommended
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Inference Examples
Inference API (serverless) has been turned off for this model.

Quantized from