Instructions to use mradermacher/vdr-2b-multi-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/vdr-2b-multi-v1-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/vdr-2b-multi-v1-GGUF", device_map="auto") - sentence-transformers
How to use mradermacher/vdr-2b-multi-v1-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mradermacher/vdr-2b-multi-v1-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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/vdr-2b-multi-v1-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 mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/vdr-2b-multi-v1-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 mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/vdr-2b-multi-v1-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 mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/vdr-2b-multi-v1-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 mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/vdr-2b-multi-v1-GGUF with Ollama:
ollama run hf.co/mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/vdr-2b-multi-v1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/vdr-2b-multi-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/vdr-2b-multi-v1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.vdr-2b-multi-v1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
How to use this with llama-cpp or ollama to create image embeddings?
First of all, thanks for the quant! I'm trying to use this to create image embedding with llama-cpp (python binding) or ollama but don't know how.
That's likely because the extra files for vision tasks are not provided by us, we only provide the llm portion. I have it somewhere on my todo list to also provide other files automatically, but this is very model specific, and they usually do not need quantizing, only extraction with the model-specific code. Normally, they are easy to come by, but in this case... not.
@nicoboss do you know how to extract these files? I could potentially do this as part of quantization or conversion.
Hello guys,
Thanks again for the quant! Has someone found a code sample on how to use this model using llama.cpp with Python bindings (or not)?
things have changed since then, it might work. I willo re-queue the job and see if we get an mmproj file.
anyway, q8 and f16 mmproj files are now both available, as well as imatrix quants soon. sorr,y no code samples :=)