Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF", filename="kalm-reranker-v1-small-q8_0.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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF with Ollama:
ollama run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
- Unsloth Studio
How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
- Lemonade
How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KaLM-Embedding/KaLM-Reranker-V1-Small-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.KaLM-Reranker-V1-Small-Q8_0-GGUF-Q8_0
List all available models
lemonade list
| # Patched llama.cpp runtime | |
| The GGUF in this repository is not compatible with stock llama.cpp. Apply | |
| the seven patches in numeric order to the exact upstream base: | |
| - repository: `https://github.com/ggml-org/llama.cpp` | |
| - base commit: `277a105dc8f8643dab54331926a9830860a03292` | |
| - tested fork commit: `8c099e4eb6c79e5d2587c8205ee9971564c740cc` | |
| - expected patched tree: `253695d8b0ca0723742c0109806a831a968cdffd` | |
| - scoring executable: `llama-kalm-reranker` | |
| ```bash | |
| git clone https://github.com/ggml-org/llama.cpp llama.cpp-src | |
| git -C llama.cpp-src checkout 277a105dc8f8643dab54331926a9830860a03292 | |
| bash ./llama.cpp/apply-patches.sh "$PWD/llama.cpp-src" | |
| ``` | |
| CUDA build: | |
| ```bash | |
| cmake -S llama.cpp-src -B llama.cpp-src/build-cuda -G Ninja \ | |
| -DCMAKE_BUILD_TYPE=Release \ | |
| -DGGML_CUDA=ON | |
| cmake --build llama.cpp-src/build-cuda \ | |
| --target llama-kalm-reranker test-t5gemma2-load test-llama-archs -j | |
| ``` | |
| CPU build: | |
| ```bash | |
| cmake -S llama.cpp-src -B llama.cpp-src/build-cpu -G Ninja \ | |
| -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=OFF | |
| cmake --build llama.cpp-src/build-cpu \ | |
| --target llama-kalm-reranker test-t5gemma2-load test-llama-archs -j | |
| ``` | |
| The complete patch series is intentionally distributed, including the | |
| converter patch, so users reproduce the exact tested source tree. There is | |
| no second squashed patch to keep in sync. | |
| The custom CLI requires a local model path: | |
| ```bash | |
| llama.cpp-src/build-cuda/bin/llama-kalm-reranker \ | |
| -m kalm-reranker-v1-small-q8_0.gguf -ngl 99 \ | |
| --query "What is the capital of China?" \ | |
| --passage "The capital of China is Beijing." | |
| ``` | |