Instructions to use sinjab/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-Q4_K_M-GGUF", filename="ms-marco-TinyBERT-L2-Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use sinjab/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinjab/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinjab/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sinjab/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF with Ollama:
ollama run hf.co/sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio new
How to use sinjab/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-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/ms-marco-TinyBERT-L2-Q4_K_M-GGUF to start chatting
- Docker Model Runner
How to use sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ms-marco-TinyBERT-L2-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
ms-marco-TinyBERT-L2-Q4_K_M-GGUF
This model was converted to GGUF format from cross-encoder/ms-marco-TinyBERT-L-2 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Model Information
- Base Model: cross-encoder/ms-marco-TinyBERT-L-2
- Quantization: Q4_K_M
- Format: GGUF (GPT-Generated Unified Format)
- Converted with: llama.cpp
Quantization Details
This is a Q4_K_M 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.
# Example using llama.cpp
./llama-rerank -m ms-marco-TinyBERT-L2-Q4_K_M.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:
# See original model card for citation information
License
This model inherits the license from the original model. Please refer to the original model card for license details.
Acknowledgements
- Original model by the authors of cross-encoder/ms-marco-TinyBERT-L-2
- GGUF conversion via llama.cpp by ggml.ai
- Converted and uploaded by sinjab
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Model tree for sinjab/ms-marco-TinyBERT-L2-Q4_K_M-GGUF
Base model
nreimers/BERT-Tiny_L-2_H-128_A-2