Instructions to use prithivMLmods/Qwen3-Reranker-4B-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-Reranker-4B-F32-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen3-Reranker-4B-F32-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen3-Reranker-4B-F32-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 prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-Reranker-4B-F32-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 prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-Reranker-4B-F32-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 prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen3-Reranker-4B-F32-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 prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use prithivMLmods/Qwen3-Reranker-4B-F32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use prithivMLmods/Qwen3-Reranker-4B-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Qwen3-Reranker-4B-F32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3-Reranker-4B-F32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Reranker-4B-F32-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

prithivMLmods commited on Jun 26, 2025

Commit

d9b7df0

verified ·

1 Parent(s): fea9408

Update README.md

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@@ -1,3 +1,15 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-Reranker-4B
+pipeline_tag: text-ranking
+library_name: transformers
+tags:
+- text-generation-inference
+---
+# **Qwen3-Reranker-4B-F32-GGUF**
+> Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.