- Libraries
- Transformers
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF", dtype="auto") - sentence-transformers
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with sentence-transformers:
from sentence_transformers import CrossEncoder
model = CrossEncoder("felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF")
query = "Which planet is known as the Red Planet?"
passages = [
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]
scores = model.predict([(query, passage) for passage in passages])
print(scores) - llama-cpp-python
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF",
filename="qwen3-reranker-0.6b-q4_k_m.gguf",
)
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use felipe-cmsa/Qwen3-Reranker-0.6B-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 felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf felipe-cmsa/Qwen3-Reranker-0.6B-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 felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf felipe-cmsa/Qwen3-Reranker-0.6B-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 felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf felipe-cmsa/Qwen3-Reranker-0.6B-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 felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with Ollama:
ollama run hf.co/felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio new
How to use felipe-cmsa/Qwen3-Reranker-0.6B-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 felipe-cmsa/Qwen3-Reranker-0.6B-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 felipe-cmsa/Qwen3-Reranker-0.6B-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 felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF to start chatting
- Pi new
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Hermes Agent new
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull felipe-cmsa/Qwen3-Reranker-0.6B-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-Reranker-0.6B-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
