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How to use gpustack/bce-embedding-base_v1-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("gpustack/bce-embedding-base_v1-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]How to use gpustack/bce-embedding-base_v1-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="gpustack/bce-embedding-base_v1-GGUF") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gpustack/bce-embedding-base_v1-GGUF", dtype="auto")How to use gpustack/bce-embedding-base_v1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="gpustack/bce-embedding-base_v1-GGUF", filename="bce-embedding-base_v1-FP16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
How to use gpustack/bce-embedding-base_v1-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
docker model run hf.co/gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
How to use gpustack/bce-embedding-base_v1-GGUF with Ollama:
ollama run hf.co/gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
How to use gpustack/bce-embedding-base_v1-GGUF with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for gpustack/bce-embedding-base_v1-GGUF to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for gpustack/bce-embedding-base_v1-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for gpustack/bce-embedding-base_v1-GGUF to start chatting
How to use gpustack/bce-embedding-base_v1-GGUF with Docker Model Runner:
docker model run hf.co/gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
How to use gpustack/bce-embedding-base_v1-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gpustack/bce-embedding-base_v1-GGUF:Q4_K_M
lemonade run user.bce-embedding-base_v1-GGUF-Q4_K_M
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