Instructions to use archit28/bge-large-en-v1.5-Q4_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use archit28/bge-large-en-v1.5-Q4_K_S-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("archit28/bge-large-en-v1.5-Q4_K_S-GGUF") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use archit28/bge-large-en-v1.5-Q4_K_S-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="archit28/bge-large-en-v1.5-Q4_K_S-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("archit28/bge-large-en-v1.5-Q4_K_S-GGUF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
archit28/bge-large-en-v1.5-Q4_K_S-GGUF
This model was converted to GGUF format from BAAI/bge-large-en-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 archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.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 archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.gguf -c 2048
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Base model
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Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported75.851
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported38.566
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported69.694
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported92.417
- ap on MTEB AmazonPolarityClassificationtest set self-reported89.193
- f1 on MTEB AmazonPolarityClassificationtest set self-reported92.395
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported48.176
- f1 on MTEB AmazonReviewsClassification (en)test set self-reported47.807