Instructions to use prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF",
	filename="Qwen3-Reranker-0.6B-seq-cls.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-0.6B-seq-cls-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-0.6B-seq-cls-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-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-0.6B-seq-cls-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-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-0.6B-seq-cls-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-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-0.6B-seq-cls-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-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-0.6B-seq-cls-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-0.6B-seq-cls-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-0.6B-seq-cls-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3-Reranker-0.6B-seq-cls-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Reranker-0.6B-seq-cls-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-Reranker-0.6B-seq-cls-GGUF

Qwen3-Reranker-0.6B-seq-cls is a sequence classification adaptation of the Qwen3-Reranker-0.6B model, designed for advanced text reranking and classification tasks across over 100 languages, including code and multilingual retrieval scenarios. Built on the Qwen3 series, this 0.6B parameter model offers a 32k context window and supports instruction-aware customization for specific tasks, typically improving performance by 1% to 5% when using tailored English instructions. It inherits the robust reasoning, long-text understanding, and versatility of its parent models, excelling in text retrieval, code retrieval, clustering, classification, and bitext mining, and ranks among the top models in benchmarks such as the MTEB multilingual leaderboard.

Model Files

File name	Size	Quant Type
Qwen3-4B-abliterated.F32.gguf	16.1 GB	F32
Qwen3-4B-abliterated.BF16.gguf	8.05 GB	BF16
Qwen3-4B-abliterated.F16.gguf	8.05 GB	F16
Qwen3-4B-abliterated.Q8_0.gguf	4.28 GB	Q8_0
Qwen3-4B-abliterated.Q6_K.gguf	3.31 GB	Q6_K
Qwen3-4B-abliterated.Q5_K_M.gguf	2.89 GB	Q5_K_M
Qwen3-4B-abliterated.Q5_K_S.gguf	2.82 GB	Q5_K_S
Qwen3-4B-abliterated.Q4_K_M.gguf	2.5 GB	Q4_K_M
Qwen3-4B-abliterated.Q4_K_S.gguf	2.38 GB	Q4_K_S
Qwen3-4B-abliterated.Q3_K_L.gguf	2.24 GB	Q3_K_L
Qwen3-4B-abliterated.Q3_K_M.gguf	2.08 GB	Q3_K_M
Qwen3-4B-abliterated.Q3_K_S.gguf	1.89 GB	Q3_K_S
Qwen3-4B-abliterated.Q2_K.gguf	1.67 GB	Q2_K

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: 287

GGUF

Model size

0.6B 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-0.6B-seq-cls-GGUF

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

tomaarsen/Qwen3-Reranker-0.6B-seq-cls

Quantized

(2)

this model