Feature Extraction
GGUF
English
Chinese
multilingual
llama.cpp
embedding
multimodal
qwen2
qwen2.5-omni
text-embedding
image-embedding
audio-embedding
imatrix
Eval Results (legacy)
conversational
Instructions to use marksverdhei/LCO-Embedding-Omni-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use marksverdhei/LCO-Embedding-Omni-3B-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 marksverdhei/LCO-Embedding-Omni-3B-GGUF:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf marksverdhei/LCO-Embedding-Omni-3B-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 marksverdhei/LCO-Embedding-Omni-3B-GGUF:Q4_K_M --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use marksverdhei/LCO-Embedding-Omni-3B-GGUF with Ollama:
ollama run hf.co/marksverdhei/LCO-Embedding-Omni-3B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use marksverdhei/LCO-Embedding-Omni-3B-GGUF with Docker Model Runner:
docker model run hf.co/marksverdhei/LCO-Embedding-Omni-3B-GGUF:Q4_K_M
- Lemonade
How to use marksverdhei/LCO-Embedding-Omni-3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull marksverdhei/LCO-Embedding-Omni-3B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LCO-Embedding-Omni-3B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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