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", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Qwen3-Reranker-4B-F32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen3-Reranker-4B-F32-GGUF",
	filename="Qwen3-Reranker-4B.BF16.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 prithivMLmods/Qwen3-Reranker-4B-F32-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio

How to use prithivMLmods/Qwen3-Reranker-4B-F32-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 prithivMLmods/Qwen3-Reranker-4B-F32-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 prithivMLmods/Qwen3-Reranker-4B-F32-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Qwen3-Reranker-4B-F32-GGUF to start chatting

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

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.

Model Files

Filename	Size	Format	Description
Qwen3-Reranker-4B.BF16.gguf	8.05 GB	BF16	Brain Float 16-bit quantization
Qwen3-Reranker-4B.F16.gguf	8.05 GB	F16	Half precision (16-bit) floating point
Qwen3-Reranker-4B.F32.gguf	16.1 GB	F32	Full precision (32-bit) floating point
Qwen3-Reranker-4B.Q2_K.gguf	1.67 GB	Q2_K	2-bit quantization with K-quant
Qwen3-Reranker-4B.Q3_K_L.gguf	2.24 GB	Q3_K_L	3-bit quantization (Large) with K-quant
Qwen3-Reranker-4B.Q3_K_M.gguf	2.08 GB	Q3_K_M	3-bit quantization (Medium) with K-quant
Qwen3-Reranker-4B.Q3_K_S.gguf	1.89 GB	Q3_K_S	3-bit quantization (Small) with K-quant
Qwen3-Reranker-4B.Q4_K_M.gguf	2.5 GB	Q4_K_M	4-bit quantization (Medium) with K-quant
Qwen3-Reranker-4B.Q4_K_S.gguf	2.38 GB	Q4_K_S	4-bit quantization (Small) with K-quant
Qwen3-Reranker-4B.Q5_K_M.gguf	2.89 GB	Q5_K_M	5-bit quantization (Medium) with K-quant
Qwen3-Reranker-4B.Q5_K_S.gguf	2.82 GB	Q5_K_S	5-bit quantization (Small) with K-quant
Qwen3-Reranker-4B.Q6_K.gguf	3.31 GB	Q6_K	6-bit quantization with K-quant
Qwen3-Reranker-4B.Q8_0.gguf	4.28 GB	Q8_0	8-bit quantization

Recommended Usage for Reranking Tasks

Q4_K_M or Q5_K_M: Optimal balance for most reranking applications
Q6_K or Q8_0: Higher precision for critical ranking accuracy
Q3_K_M: Good performance with reduced memory footprint
F16 or BF16: Maximum reranking precision, requires more VRAM
F32: Highest precision for research and benchmarking

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 158

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Inference Providers NEW

Text Ranking

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Model tree for prithivMLmods/Qwen3-Reranker-4B-F32-GGUF

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-Reranker-4B

Quantized

(49)

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