Instructions to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp 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 ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M # Run inference directly in the terminal: llama cli -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M # Run inference directly in the terminal: llama cli -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp: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 ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp: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 ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
Use Docker
docker model run hf.co/ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp with Ollama:
ollama run hf.co/ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
- Unsloth Studio
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp 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 ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp 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 ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp to start chatting
- Pi
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp: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": "ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp with Docker Model Runner:
docker model run hf.co/ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
- Lemonade
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-Reranker-4B-GGUF-llama_cpp-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp: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 ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ebasKing/Qwen3-Reranker-4B-GGUF-llama_cpp:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Duplicate from Voodisss/Qwen3-Reranker-4B-GGUF-llama_cpp
Browse filesCo-authored-by: VooDisss <Voodisss@users.noreply.huggingface.co>
- .gitattributes +44 -0
- Qwen3-Reranker-4B-Q2_K.gguf +3 -0
- Qwen3-Reranker-4B-Q3_K_M.gguf +3 -0
- Qwen3-Reranker-4B-Q4_0.gguf +3 -0
- Qwen3-Reranker-4B-Q4_K_M.gguf +3 -0
- Qwen3-Reranker-4B-Q5_0.gguf +3 -0
- Qwen3-Reranker-4B-Q5_K_M.gguf +3 -0
- Qwen3-Reranker-4B-Q6_K.gguf +3 -0
- Qwen3-Reranker-4B-f16.gguf +3 -0
- Qwen3-Reranker-4B.Q8_0.gguf +3 -0
- README.md +104 -0
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---
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| 2 |
+
base_model: Qwen/Qwen3-Reranker-4B
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| 3 |
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library_name: gguf
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| 4 |
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license: apache-2.0
|
| 5 |
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pipeline_tag: text-ranking
|
| 6 |
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tags:
|
| 7 |
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- reranker
|
| 8 |
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- gguf
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| 9 |
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- llama.cpp
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| 10 |
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- qwen3
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| 11 |
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- text-ranking
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| 12 |
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---
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| 13 |
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| 14 |
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# Qwen3-Reranker-4B — GGUF (llama.cpp)
|
| 15 |
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| 16 |
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Working GGUF of [Qwen/Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) for [llama.cpp](https://github.com/ggml-org/llama.cpp). Converted 2025-03-09 with the official `convert_hf_to_gguf.py`.
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| 17 |
+
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| 18 |
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> **Other sizes:** [0.6B](https://huggingface.co/Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp) · [4B (this)](https://huggingface.co/Voodisss/Qwen3-Reranker-4B-GGUF-llama_cpp) · [8B](https://huggingface.co/Voodisss/Qwen3-Reranker-8B-GGUF-llama_cpp)
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| 19 |
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| 20 |
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## Quantization quality comparison (Qwen3-Reranker-4B)
|
| 21 |
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| 22 |
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Benchmarked on [MTEB AskUbuntuDupQuestions](https://huggingface.co/datasets/mteb/AskUbuntuDupQuestions) (361 queries) via llama-server `/v1/rerank` on RTX 3090. All quants produced from the same F16 source using `llama-quantize`.
|
| 23 |
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| Quant | Size | NDCG@10 | MAP@10 | MRR@10 | Δ NDCG@10 |
|
| 25 |
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| ------ | ------- | ------- | ------ | ------ | --------- |
|
| 26 |
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| F16 | 7.50 GB | 0.7003 | 0.5530 | 0.7711 | baseline |
|
| 27 |
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| Q8_0 | 3.99 GB | 0.6985 | 0.5514 | 0.7670 | -0.3% |
|
| 28 |
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| Q6_K | 3.08 GB | 0.7016 | 0.5548 | 0.7722 | +0.2% |
|
| 29 |
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| Q5_K_M | 2.69 GB | 0.7009 | 0.5517 | 0.7699 | +0.1% |
|
| 30 |
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| Q5_0 | 2.63 GB | 0.6995 | 0.5532 | 0.7676 | -0.1% |
|
| 31 |
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| Q4_K_M | 2.33 GB | 0.7058 | 0.5596 | 0.7746 | +0.8% |
|
| 32 |
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| Q4_0 | 2.21 GB | 0.6930 | 0.5426 | 0.7623 | -1.1% |
|
| 33 |
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| Q3_K_M | 1.93 GB | 0.7040 | 0.5555 | 0.7828 | +0.5% |
|
| 34 |
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| Q2_K | 1.55 GB | 0.6691 | 0.5079 | 0.7401 | **-4.5%** |
|
| 35 |
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| 36 |
+
**Takeaway:** All quants from Q8_0 down to Q3_K_M are within ±1% of F16 — pick based on your VRAM budget. Q4_K_M (2.33 GB) is the sweet spot: 3.2x smaller than F16 with no measurable quality loss. **Avoid Q2_K** — it's the only quant with real degradation.
|
| 37 |
+
## Does it work?
|
| 38 |
+
|
| 39 |
+
Yes. Most community GGUFs of Qwen3-Reranker produce garbage scores (`4.5e-23`) because they're missing reranker-specific tensors. See [llama.cpp #16407](https://github.com/ggml-org/llama.cpp/issues/16407). This one works:
|
| 40 |
+
|
| 41 |
+
```
|
| 42 |
+
Doc 0 (relevant): relevance_score = 0.999966
|
| 43 |
+
Doc 1 (irrelevant): relevance_score = 0.000069
|
| 44 |
+
```
|
| 45 |
+
|
| 46 |
+
## Quick start
|
| 47 |
+
|
| 48 |
+
```bash
|
| 49 |
+
llama-server -m Qwen3-Reranker-4B-f16.gguf --reranking --pooling rank --embedding --port 8081
|
| 50 |
+
```
|
| 51 |
+
|
| 52 |
+
```bash
|
| 53 |
+
curl http://localhost:8081/v1/rerank \
|
| 54 |
+
-H "Content-Type: application/json" \
|
| 55 |
+
-d '{
|
| 56 |
+
"query": "employment termination notice period",
|
| 57 |
+
"documents": [
|
| 58 |
+
"The Labour Code requires 30 calendar days written notice.",
|
| 59 |
+
"Corporate tax rates for small enterprises."
|
| 60 |
+
]
|
| 61 |
+
}'
|
| 62 |
+
```
|
| 63 |
+
|
| 64 |
+
Use **`/v1/rerank`**, not `/v1/embeddings`. The embeddings endpoint returns zeros for reranker models.
|
| 65 |
+
|
| 66 |
+
## What's different about this GGUF?
|
| 67 |
+
|
| 68 |
+
The official `convert_hf_to_gguf.py` detects Qwen3-Reranker and does things naive converters skip:
|
| 69 |
+
|
| 70 |
+
- Extracts `cls.output.weight` (the yes/no classifier) from `lm_head`
|
| 71 |
+
- Sets `pooling_type = RANK` metadata
|
| 72 |
+
- Bakes in the rerank chat template
|
| 73 |
+
- Sets `classifier.output_labels = ["yes", "no"]`
|
| 74 |
+
|
| 75 |
+
Without these, llama-server has nothing to compute scores from.
|
| 76 |
+
|
| 77 |
+
## Known broken GGUFs
|
| 78 |
+
|
| 79 |
+
- [DevQuasar/Qwen.Qwen3-Reranker-4B-GGUF](https://huggingface.co/DevQuasar/Qwen.Qwen3-Reranker-4B-GGUF) — confirmed broken with llama.cpp
|
| 80 |
+
|
| 81 |
+
## models.ini example
|
| 82 |
+
|
| 83 |
+
```ini
|
| 84 |
+
[Qwen3-Reranker-4B-f16]
|
| 85 |
+
model = /path/to/Qwen3-Reranker-4B-f16.gguf
|
| 86 |
+
reranking = true
|
| 87 |
+
pooling = rank
|
| 88 |
+
embedding = true
|
| 89 |
+
ctx-size = 32768
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
For a full multi-model setup guide (embedding + reranking + chat on one server), see the **[llama-server Qwen3 guide](https://gist.github.com/VooDisss/42bce4eb5c76d3c325633886c5e348ee)**.
|
| 93 |
+
|
| 94 |
+
## Convert it yourself
|
| 95 |
+
|
| 96 |
+
```bash
|
| 97 |
+
pip install huggingface_hub gguf torch safetensors sentencepiece
|
| 98 |
+
python -c "from huggingface_hub import snapshot_download; snapshot_download('Qwen/Qwen3-Reranker-4B', local_dir='Qwen3-Reranker-4B-src')"
|
| 99 |
+
python convert_hf_to_gguf.py --outtype f16 --outfile Qwen3-Reranker-4B-f16.gguf Qwen3-Reranker-4B-src/
|
| 100 |
+
```
|
| 101 |
+
|
| 102 |
+
## License
|
| 103 |
+
|
| 104 |
+
Apache 2.0 — same as the [original model](https://huggingface.co/Qwen/Qwen3-Reranker-4B).
|