Instructions to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparkhonyuk/USER-bge-m3-Q5_K_M-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] - llama-cpp-python
How to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF", filename="user-bge-m3-q5_k_m.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama-cli -hf sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama-cli -hf sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
Use pre-built binary
# 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 sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
Build from source code
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 sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
Use Docker
docker model run hf.co/sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with Ollama:
ollama run hf.co/sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
- Unsloth Studio
How to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
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 sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF to start chatting
Install Unsloth Studio (Windows)
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 sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with Docker Model Runner:
docker model run hf.co/sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
- Lemonade
How to use sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sparkhonyuk/USER-bge-m3-Q5_K_M-GGUF:Q5_K_M
Run and chat with the model
lemonade run user.USER-bge-m3-Q5_K_M-GGUF-Q5_K_M
List all available models
lemonade list
- Xet hash:
- e397f9faac16a1386fdb423353848aef8702a025dfd0d25df42c6018d9a770f0
- Size of remote file:
- 291 MB
- SHA256:
- 04fd9f8734a5a21aced80c4d57e154aa56e0ad440cf9797c934e97297963521d
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