hongkeon/granite-embedding-278m-multilingual-Q4_K_M-GGUF
This model was converted to GGUF format from ibm-granite/granite-embedding-278m-multilingual
using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo hongkeon/granite-embedding-278m-multilingual-Q4_K_M-GGUF --hf-file granite-embedding-278m-multilingual-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo hongkeon/granite-embedding-278m-multilingual-Q4_K_M-GGUF --hf-file granite-embedding-278m-multilingual-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1
flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo hongkeon/granite-embedding-278m-multilingual-Q4_K_M-GGUF --hf-file granite-embedding-278m-multilingual-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo hongkeon/granite-embedding-278m-multilingual-Q4_K_M-GGUF --hf-file granite-embedding-278m-multilingual-q4_k_m.gguf -c 2048
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Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported73.433
- f1 on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported61.230
- f1_weighted on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported78.409
- ap on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported23.347
- ap_weighted on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported23.347
- main_score on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported73.433
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported71.806
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported65.647
- f1_weighted on MTEB AmazonCounterfactualClassification (en)test set self-reported74.481
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported34.046