Qwen3-Reranker-4B-W4A16-G128
Qwen3-Reranker-4B-W4A16-G128 is a quantized (4-bit weight, 16-bit activation) reranking model derived from Qwen's 4B base. It reduces VRAM usage from ~17.4GB to ~11GB with an estimated <5% accuracy trade-off. Suitable for retrieval-augmented generation (RAG) and ranking tasks in resource-constrained environments.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | boboliu |
| Parameters | 4.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-classification |
| Gated on HuggingFace | No |
| Downloads | 543.7k |
| Likes | 2 |
| Last updated | 2025-06-07 |
| Source | boboliu/Qwen3-Reranker-4B-W4A16-G128 |
What Qwen3-Reranker-4B-W4A16-G128 is
GPTQ-quantized derivative of Qwen/Qwen3-Reranker-4B (4B parameters). W4A16 quantization with group size 128. Fine-tuned on Ultrachat, T2Ranking, and COIG-CQIA datasets for calibration. Requires compressed-tensors, optimum, and auto-gptq/gptqmodel libraries. Classified as text-classification pipeline; endpoints-compatible and TEI-compatible.
Run Qwen3-Reranker-4B-W4A16-G128 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="boboliu/Qwen3-Reranker-4B-W4A16-G128")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
**VRAM (Estimated):** 11–12 GB (without Flash Attention v2). **Precision:** W4A16 (4-bit weights, 16-bit activations). **Baseline:** ~17.4 GB for unquantized base model. Verify actual consumption on target hardware; Flash Attention v2 support may reduce memory further.
Unknown. Model card does not document LoRA, QLoRA, or full fine-tuning feasibility on this quantized variant. Contact boboliu or consult Qwen3-Reranker-4B documentation. Quantization may complicate gradient-based tuning; verify library support (auto-gptq/gptqmodel) before attempting fine-tuning.
When to avoid it — and what to weigh
- High-Precision Cross-Encoder Requirement — If <5% accuracy loss is unacceptable for your use case, evaluate the unquantized base model or an alternative higher-precision reranker.
- Non-English or Highly Specialized Domains — Training data (Ultrachat, T2Ranking, COIG-CQIA) language and domain coverage not explicitly stated; verify fit for non-English or niche applications.
- Real-Time Sub-Millisecond Latency — Quantization provides memory savings, not guaranteed latency improvements. Benchmark against your SLA before deployment.
- Production Without Validation — Model card notes evaluation is ongoing. Conduct task-specific accuracy benchmarking before production rollout.
License & commercial use
Apache 2.0 license. Permissive, OSI-approved. Allows commercial use, modification, and distribution under stated terms.
Apache 2.0 is a permissive open-source license that explicitly permits commercial use. No gating or restrictions noted. Ensure compliance with Apache 2.0 attribution and notice requirements in production deployments.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Limited |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | Medium |
No security audit or threat model documented. Standard considerations: validate quantized model outputs for adversarial robustness; apply input sanitization for production RAG pipelines; manage VRAM isolation in multi-tenant deployments; monitor for side-channel attacks via timing analysis on quantized inference (research ongoing). No known CVEs stated for this model or upstream base.
Alternatives to consider
Qwen/Qwen3-Reranker-4B (unquantized)
Official base model; full precision for maximum accuracy; higher VRAM (~17.4 GB) if budget allows.
Qwen/Qwen3-Reranker-0.6B
Smaller official variant; lower VRAM footprint; trade-off is model capacity, not just quantization.
BAAI/bge-reranker-v2-m3
Alternative multilingual reranker; established benchmarks; different architecture and trade-offs.
Ship Qwen3-Reranker-4B-W4A16-G128 with senior software developers
Evaluate Qwen3-Reranker-4B-W4A16-G128 on your RAG pipeline. Test quantization accuracy on your data, verify VRAM on your hardware, and confirm integration with Text Embeddings Inference or Transformers. Contact the Devco team for production hardening support.
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Qwen3-Reranker-4B-W4A16-G128 FAQ
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Custom software development services
From first prototype to production, DEV.co delivers software development services around tools like Qwen3-Reranker-4B-W4A16-G128. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Ready to Deploy Quantized Reranking?
Evaluate Qwen3-Reranker-4B-W4A16-G128 on your RAG pipeline. Test quantization accuracy on your data, verify VRAM on your hardware, and confirm integration with Text Embeddings Inference or Transformers. Contact the Devco team for production hardening support.