Instructions to use Plasmoxy/bge-micro-v2-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Plasmoxy/bge-micro-v2-Q4_K_M-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Plasmoxy/bge-micro-v2-Q4_K_M-GGUF") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Plasmoxy/bge-micro-v2-Q4_K_M-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Plasmoxy/bge-micro-v2-Q4_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Plasmoxy/bge-micro-v2-Q4_K_M-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_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 Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_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 Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Plasmoxy/bge-micro-v2-Q4_K_M-GGUF with Ollama:
ollama run hf.co/Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use Plasmoxy/bge-micro-v2-Q4_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 Plasmoxy/bge-micro-v2-Q4_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 Plasmoxy/bge-micro-v2-Q4_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 Plasmoxy/bge-micro-v2-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Plasmoxy/bge-micro-v2-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use Plasmoxy/bge-micro-v2-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Plasmoxy/bge-micro-v2-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.bge-micro-v2-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
Plasmoxy/bge-micro-v2-Q4_K_M-GGUF
Really small BGE embedding model but with 4-bit gguf quant.
This model was converted to GGUF format from TaylorAI/bge-micro-v2 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
!!! IMPORTANT !!! - context size is 512, specify the context size (-c 512) for llama cpp.
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 Plasmoxy/bge-micro-v2-Q4_K_M-GGUF --hf-file bge-micro-v2-q4_k_m.gguf -c 512 -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Plasmoxy/bge-micro-v2-Q4_K_M-GGUF --hf-file bge-micro-v2-q4_k_m.gguf -c 512
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 Plasmoxy/bge-micro-v2-Q4_K_M-GGUF --hf-file bge-micro-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Plasmoxy/bge-micro-v2-Q4_K_M-GGUF --hf-file bge-micro-v2-q4_k_m.gguf -c 2048
- Downloads last month
- 16
4-bit
Model tree for Plasmoxy/bge-micro-v2-Q4_K_M-GGUF
Base model
TaylorAI/bge-micro-v2Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported67.761
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported29.638
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported61.312
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported79.755
- ap on MTEB AmazonPolarityClassificationtest set self-reported74.214
- f1 on MTEB AmazonPolarityClassificationtest set self-reported79.653
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported37.452
- f1 on MTEB AmazonReviewsClassification (en)test set self-reported37.025