Instructions to use sinjab/jina-reranker-v1-turbo-en-F16-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 sinjab/jina-reranker-v1-turbo-en-F16-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 sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16 # Run inference directly in the terminal: llama cli -hf sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16 # Run inference directly in the terminal: llama cli -hf sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
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 sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
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 sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
Use Docker
docker model run hf.co/sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use sinjab/jina-reranker-v1-turbo-en-F16-GGUF with Ollama:
ollama run hf.co/sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
- Unsloth Studio
How to use sinjab/jina-reranker-v1-turbo-en-F16-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 sinjab/jina-reranker-v1-turbo-en-F16-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 sinjab/jina-reranker-v1-turbo-en-F16-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sinjab/jina-reranker-v1-turbo-en-F16-GGUF to start chatting
- Docker Model Runner
How to use sinjab/jina-reranker-v1-turbo-en-F16-GGUF with Docker Model Runner:
docker model run hf.co/sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
- Lemonade
How to use sinjab/jina-reranker-v1-turbo-en-F16-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sinjab/jina-reranker-v1-turbo-en-F16-GGUF:F16
Run and chat with the model
lemonade run user.jina-reranker-v1-turbo-en-F16-GGUF-F16
List all available models
lemonade list
- Atomic Chat
File size: 2,034 Bytes
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language:
- en
license: apache-2.0
library_name: gguf
tags:
- reranker
- gguf
- llama.cpp
base_model: jinaai/jina-reranker-v1-turbo-en
---
# jina-reranker-v1-turbo-en-F16-GGUF
This model was converted to GGUF format from [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the [original model card](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) for more details on the model.
## Model Information
- **Base Model**: [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en)
- **Quantization**: F16
- **Format**: GGUF (GPT-Generated Unified Format)
- **Converted with**: llama.cpp
## Quantization Details
This is a **F16** quantization of the original model:
- **F16**: Full 16-bit floating point - highest quality, largest size
- **Q8_0**: 8-bit quantization - high quality, good balance
- **Q4_K_M**: 4-bit quantization with medium quality - smaller size, faster inference
## Usage
This model can be used with llama.cpp and other GGUF-compatible inference engines.
```bash
# Example using llama.cpp
./llama-rerank -m jina-reranker-v1-turbo-en-F16.gguf
```
## Model Files
| Quantization | Use Case |
|-------------|----------|
| F16 | Maximum quality, largest size |
| Q8_0 | High quality, good balance of size/performance |
| Q4_K_M | Good quality, smallest size, fastest inference |
## Citation
If you use this model, please cite the original model:
```bibtex
# See original model card for citation information
```
## License
This model inherits the license from the original model. Please refer to the [original model card](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) for license details.
## Acknowledgements
- Original model by the authors of [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en)
- GGUF conversion via llama.cpp by ggml.ai
- Converted and uploaded by [sinjab](https://huggingface.co/sinjab)
|