Instructions to use endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("endyjasmi/Qwen3-Embedding-8B-Q4_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] - Transformers
How to use endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF:Q4_K_M --embeddings
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible embeddings server: ./build/bin/llama serve -hf endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF:Q4_K_M --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF with Ollama:
ollama run hf.co/endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-Embedding-8B-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF
This model was converted to GGUF format from Qwen/Qwen3-Embedding-8B 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 endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF --hf-file qwen3-embedding-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF --hf-file qwen3-embedding-8b-q4_k_m.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 endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF --hf-file qwen3-embedding-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo endyjasmi/Qwen3-Embedding-8B-Q4_K_M-GGUF --hf-file qwen3-embedding-8b-q4_k_m.gguf -c 2048
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