Instructions to use Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Content-AI/multilingual-e5-large-instruct-Q8_0-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 Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF", dtype="auto") - llama-cpp-python
How to use Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF", filename="multilingual-e5-large-instruct-q8_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
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 Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
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 Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with Ollama:
ollama run hf.co/Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
- Unsloth Studio new
How to use Content-AI/multilingual-e5-large-instruct-Q8_0-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 Content-AI/multilingual-e5-large-instruct-Q8_0-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 Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF to start chatting
- Docker Model Runner
How to use Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
- Lemonade
How to use Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.multilingual-e5-large-instruct-Q8_0-GGUF-Q8_0
List all available models
lemonade list
Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF
This model was converted to GGUF format from intfloat/multilingual-e5-large-instruct 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 Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.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 Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF --hf-file multilingual-e5-large-instruct-q8_0.gguf -c 2048
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Model tree for Content-AI/multilingual-e5-large-instruct-Q8_0-GGUF
Base model
intfloat/multilingual-e5-large-instructEvaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported76.239
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported39.074
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported70.048
- accuracy on MTEB AmazonCounterfactualClassification (de)test set self-reported66.713
- ap on MTEB AmazonCounterfactualClassification (de)test set self-reported79.015
- f1 on MTEB AmazonCounterfactualClassification (de)test set self-reported64.820
- accuracy on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported73.853
- ap on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported22.448
- f1 on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported61.016
- accuracy on MTEB AmazonCounterfactualClassification (ja)test set self-reported76.049
- ap on MTEB AmazonCounterfactualClassification (ja)test set self-reported23.450
- f1 on MTEB AmazonCounterfactualClassification (ja)test set self-reported62.572
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported96.287
- ap on MTEB AmazonPolarityClassificationtest set self-reported94.845
- f1 on MTEB AmazonPolarityClassificationtest set self-reported96.287
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported56.716