Qwen3-Embedding-4B-W4A16-G128
Qwen3-Embedding-4B-W4A16-G128 is a quantized 4-billion-parameter embedding model optimized for text similarity, clustering, and retrieval tasks. It reduces memory footprint from ~17.4 GB to ~11 GB with minimal performance loss (~0.72% on C-MTEB benchmark), making it suitable for resource-constrained deployments while maintaining competitive multilingual embedding quality.
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 | feature-extraction |
| Gated on HuggingFace | No |
| Downloads | 551.8k |
| Likes | 5 |
| Last updated | 2025-06-07 |
| Source | boboliu/Qwen3-Embedding-4B-W4A16-G128 |
What Qwen3-Embedding-4B-W4A16-G128 is
GPTQ-quantized variant of Qwen/Qwen3-Embedding-4B using W4A16 precision (4-bit weights, 16-bit activations) with group size 128. Calibrated on THUIR/T2Ranking and m-a-p/COIG-CQIA datasets. Supports sentence-transformers pipeline. Model card reports C-MTEB evaluation showing 71.75 mean task score (vs. 72.27 unquantized). Requires `compressed-tensors`, `optimum`, and `auto-gptq`/`gptqmodel` libraries.
Run Qwen3-Embedding-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-Embedding-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
ESTIMATE: ~11 GB VRAM (W4A16 quantization, without Flash Attention 2). Unquantized baseline requires ~17.4 GB. CPU: compatible with modern x86-64 and ARM64 architectures. GPU: optimized for NVIDIA/AMD CUDA but CPU inference supported (slow). Actual requirements vary by batch size, sequence length (context length Unknown), and inference library.
Model card does not document LoRA/QLoRA feasibility on this quantized variant. Fine-tuning a 4B quantized model is possible in principle but requires careful setup: (1) Verify `peft` library compatibility with GPTQ-quantized weights; (2) Consider full-parameter training on unquantized version then re-quantizing; (3) Dequantization for training may exceed 11 GB VRAM savings. Requires experimentation—no official guidance provided.
When to avoid it — and what to weigh
- Maximum embedding quality required — If 0.72% performance loss on C-MTEB is unacceptable for your use case, use the unquantized Qwen3-Embedding-4B instead. Clustering performance drops slightly (77.89 → 77.51).
- VRAM is abundant but latency is critical — Quantized inference may introduce modest latency overhead due to dequantization operations. If you have >20 GB VRAM and prioritize sub-millisecond response times, profile against unquantized alternatives.
- Requires out-of-the-box cross-lingual zero-shot transfer to unseen languages — Model card does not document performance on languages outside C-MTEB test set. Requires validation before production deployment to low-resource language pairs.
- No quantization library support in your stack — Depends on `auto-gptq` or `gptqmodel`. If your inference framework (e.g., specialized hardware or custom runtime) lacks GPTQ support, integration is non-trivial.
License & commercial use
Apache-2.0 (apache license 2.0). OSI-approved, permissive open-source license. Allows use, modification, and distribution under Apache-2.0 terms.
Apache-2.0 is a permissive OSI license that permits commercial use, including proprietary applications, provided you retain license notices and disclaimers. No commercial restrictions. However, verify: (1) upstream base model (Qwen/Qwen3-Embedding-4B) license; (2) calibration dataset licenses (THUIR/T2Ranking, m-a-p/COIG-CQIA) for commercial use; (3) any third-party dependencies (compressed-tensors, optimum, auto-gptq) for compliance in your use case. Recommend legal review if embedding model is critical IP.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Unknown |
| Documentation | Limited |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
No security audit, threat model, or adversarial robustness evaluation documented. General model security considerations: (1) embeddings can be reverse-engineered in some cases—do not embed sensitive PII directly; (2) model is publicly available, allowing attackers to perform adversarial prompt/input analysis; (3) supply chain: verify integrity of quantized weights downloaded from HuggingFace and GPTQ/optimum libraries from PyPI; (4) GPTQ quantization itself does not introduce known vulnerabilities but dequantization overhead may create side-channel timing leaks in highly sensitive contexts. Requires application-level security (input validation, rate limiting, access control).
Alternatives to consider
Qwen/Qwen3-Embedding-4B (unquantized)
Original, full-precision model with 0.72% higher C-MTEB performance. Choose if 17.4 GB VRAM is available and maximum accuracy is required. Official support and clearer maintenance.
gte-Qwen2-1.5B-instruct
1.5B parameter alternative with ~8 GB VRAM footprint (estimated) and 67.12 C-MTEB score. Lighter weight, well-maintained by official Qwen LM team. Trade-off: lower absolute performance for smaller model size.
bge-multilingual-gemma2
9B parameter multilingual embedding model (68.52 C-MTEB) maintained by Alibaba. Higher quality but larger memory footprint. Alternative if performance is prioritized over memory.
Ship Qwen3-Embedding-4B-W4A16-G128 with senior software developers
Evaluate this model on your retrieval or clustering workload. Start with a CPU test (pip install compressed-tensors optimum auto-gptq), benchmark C-MTEB performance on your domain data, and profile VRAM/latency on target hardware. Review license compliance for upstream dependencies before production rollout.
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Qwen3-Embedding-4B-W4A16-G128 FAQ
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Software development & web development with DEV.co
Adopting Qwen3-Embedding-4B-W4A16-G128 is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to Deploy Quantized Embeddings?
Evaluate this model on your retrieval or clustering workload. Start with a CPU test (pip install compressed-tensors optimum auto-gptq), benchmark C-MTEB performance on your domain data, and profile VRAM/latency on target hardware. Review license compliance for upstream dependencies before production rollout.