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

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

KaLM-Reranker-V1-Small-Q8_0-GGUF / llama.cpp /README.md

cosyy

Publish KaLM-Reranker Q8_0 GGUF and patched llama.cpp runtime

35eb2a2 verified 9 days ago

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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

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:

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:

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:

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."