Instructions to use sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF", filename="jina-reranker-v1-turbo-en-Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinjab/jina-reranker-v1-turbo-en-Q4_K_M-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 sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sinjab/jina-reranker-v1-turbo-en-Q4_K_M-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 sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF with Ollama:
ollama run hf.co/sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use sinjab/jina-reranker-v1-turbo-en-Q4_K_M-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-Q4_K_M-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-Q4_K_M-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-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sinjab/jina-reranker-v1-turbo-en-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.jina-reranker-v1-turbo-en-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: gguf
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tags:
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- reranker
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- gguf
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- llama.cpp
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base_model: jinaai/jina-reranker-v1-turbo-en
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---
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# jina-reranker-v1-turbo-en-Q4_K_M-GGUF
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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.
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Refer to the [original model card](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) for more details on the model.
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## Model Information
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- **Base Model**: [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en)
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- **Quantization**: Q4_K_M
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- **Format**: GGUF (GPT-Generated Unified Format)
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- **Converted with**: llama.cpp
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## Quantization Details
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This is a **Q4_K_M** quantization of the original model:
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- **F16**: Full 16-bit floating point - highest quality, largest size
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- **Q8_0**: 8-bit quantization - high quality, good balance
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- **Q4_K_M**: 4-bit quantization with medium quality - smaller size, faster inference
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## Usage
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This model can be used with llama.cpp and other GGUF-compatible inference engines.
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```bash
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# Example using llama.cpp
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./llama-rerank -m jina-reranker-v1-turbo-en-Q4_K_M.gguf
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```
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## Model Files
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| Quantization | Use Case |
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|-------------|----------|
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| F16 | Maximum quality, largest size |
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| Q8_0 | High quality, good balance of size/performance |
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| Q4_K_M | Good quality, smallest size, fastest inference |
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## Citation
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If you use this model, please cite the original model:
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```bibtex
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# See original model card for citation information
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```
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## License
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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.
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## Acknowledgements
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- Original model by the authors of [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en)
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- GGUF conversion via llama.cpp by ggml.ai
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- Converted and uploaded by [sinjab](https://huggingface.co/sinjab)
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