Qwen3-Embedding-4B-W4A16-G128
GPTQ Quantized Qwen/Qwen3-Embedding-4B with THUIR/T2Ranking and m-a-p/COIG-CQIA for calibration set.
Params
4.1 B
Context
40,960
Downloads 30d
414 K
Likes
5
Download history
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Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | i32 | 2.7 GB | 4.0 GB | ✅ Runs comfortably |
| model.safetensors (bf16, full) | bf16 + 40K ctx | 2.7 GB | 6.5 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- Qwen3ForCausalLM
- Parameters
- 4.1 B
- Tensor type
- I32
- Context length
- 40,960
- Vocabulary
- 151,665
- Layers / heads
- 36 / 32
- Licence
- apache-2.0
- First seen on the Hub
- 2025-06-06
- Base model
- Qwen3-Embedding-4B
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any feature-extraction model