Instructions to use marksverdhei/LCO-Embedding-Omni-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use marksverdhei/LCO-Embedding-Omni-7B-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 marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf marksverdhei/LCO-Embedding-Omni-7B-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 marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf marksverdhei/LCO-Embedding-Omni-7B-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 marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf marksverdhei/LCO-Embedding-Omni-7B-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 marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use marksverdhei/LCO-Embedding-Omni-7B-GGUF with Ollama:
```
ollama run hf.co/marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use marksverdhei/LCO-Embedding-Omni-7B-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 marksverdhei/LCO-Embedding-Omni-7B-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 marksverdhei/LCO-Embedding-Omni-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for marksverdhei/LCO-Embedding-Omni-7B-GGUF to start chatting

Docker Model Runner
How to use marksverdhei/LCO-Embedding-Omni-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M
```

Lemonade

How to use marksverdhei/LCO-Embedding-Omni-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull marksverdhei/LCO-Embedding-Omni-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LCO-Embedding-Omni-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

marksverdhei commited on Mar 4

Commit

7410dbf

1 Parent(s): 72a842b

Update README.md

Browse files

Files changed (1) hide show

README.md +27 -26

README.md CHANGED Viewed

@@ -3,38 +3,39 @@ base_model: LCO-Embedding/LCO-Embedding-Omni-7B
 library_name: llama.cpp
 license: apache-2.0
 language:
-  - en
-  - zh
-  - multilingual
 tags:
-  - gguf
-  - embedding
-  - multimodal
-  - qwen2
-  - qwen2.5-omni
-  - feature-extraction
-  - text-embedding
-  - image-embedding
-  - audio-embedding
-  - imatrix
 pipeline_tag: feature-extraction
 model_type: qwen2vl
 quantized_by: marksverdhei
 datasets:
-  - MIEB
 model_name: LCO-Embedding-Omni-7B-GGUF
 model-index:
-  - name: LCO-Embedding-Omni-7B-GGUF
-    results:
-      - task:
-          type: feature-extraction
-        dataset:
-          name: MIEB-Lite
-          type: MIEB
-        metrics:
-          - name: Embedding Dimensions
-            type: embedding_dimensions
-            value: 3584
 ---
 # LCO-Embedding-Omni-7B-GGUF
@@ -213,4 +214,4 @@ curl -s http://localhost:8080/embeddings \
   journal={arXiv preprint arXiv:2503.20215},
   year={2025}
 }
-```

 library_name: llama.cpp
 license: apache-2.0
 language:
+- en
+- zh
+- multilingual
 tags:
+- gguf
+- embedding
+- multimodal
+- qwen2
+- qwen2.5-omni
+- feature-extraction
+- text-embedding
+- image-embedding
+- audio-embedding
+- imatrix
+- quantized
 pipeline_tag: feature-extraction
 model_type: qwen2vl
 quantized_by: marksverdhei
 datasets:
+- MIEB
 model_name: LCO-Embedding-Omni-7B-GGUF
 model-index:
+- name: LCO-Embedding-Omni-7B-GGUF
+  results:
+  - task:
+      type: feature-extraction
+    dataset:
+      name: MIEB-Lite
+      type: MIEB
+    metrics:
+    - name: Embedding Dimensions
+      type: embedding_dimensions
+      value: 3584
 ---
 # LCO-Embedding-Omni-7B-GGUF
   journal={arXiv preprint arXiv:2503.20215},
   year={2025}
 }
+```