Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-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-Q4_K_M-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-Q4_K_M-GGUF",
	filename="kalm-reranker-v1-small-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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-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-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-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 serve -hf KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use KaLM-Embedding/KaLM-Reranker-V1-Small-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 KaLM-Embedding/KaLM-Reranker-V1-Small-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 KaLM-Embedding/KaLM-Reranker-V1-Small-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 KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KaLM-Embedding/KaLM-Reranker-V1-Small-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.KaLM-Reranker-V1-Small-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

KaLM-Reranker-V1-Small-Q4_K_M-GGUF / llama.cpp /README.md

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Publish Q4_K_M documentation, evaluation, examples and runtime

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	# Patched llama.cpp runtime

	Stock llama.cpp does not support the `t5gemma2` reranker architecture in
	this release. Apply all seven patches in lexical order to the exact base.

	- upstream: `https://github.com/ggml-org/llama.cpp`
	- base commit: `277a105dc8f8643dab54331926a9830860a03292`
	- tested fork commit: `8c099e4eb6c79e5d2587c8205ee9971564c740cc`
	- expected patched tree: `253695d8b0ca0723742c0109806a831a968cdffd`
	- 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"
	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
	```

	For CPU, configure with `-DGGML_CUDA=OFF`. The custom CLI requires a local
	model path:

	```bash
	llama.cpp-src/build-cuda/bin/llama-kalm-reranker \
	-m kalm-reranker-v1-small-q4_k_m.gguf -ngl 99 --require-gpu \
	--query "What is the capital of China?" \
	--passage "The capital of China is Beijing."
	```