metadata
base_model: Snowflake/snowflake-arctic-embed-m-v1.5
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
- llama-cpp
- gguf-my-repo
model-index:
- name: snowflake-arctic-embed-m-v1.5
results:
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type: Retrieval
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name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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- task:
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dataset:
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type: mteb/cqadupstack-android
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revision: f46a197baaae43b4f621051089b82a364682dfeb
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msyukorai/snowflake-arctic-embed-m-v1.5-Q4_K_M-GGUF
This model was converted to GGUF format from Snowflake/snowflake-arctic-embed-m-v1.5 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 msyukorai/snowflake-arctic-embed-m-v1.5-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo msyukorai/snowflake-arctic-embed-m-v1.5-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-m-v1.5-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 msyukorai/snowflake-arctic-embed-m-v1.5-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo msyukorai/snowflake-arctic-embed-m-v1.5-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q4_k_m.gguf -c 2048