---
license: apache-2.0
tags:
  - embedding
  - safetensors



base_model: Qwen/Qwen3-Embedding-0.6B
pipeline_tag: feature-extraction
library_name: transformers
---

# Qwen3-Embedding-0.6B

Multi-format version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) - optimized for deployment.

## Model Information

| Property | Value |
|----------|-------|
| Base Model | [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) |
| Task | feature-extraction |
| Type | Text Model |
| Trust Remote Code | True |

## Available Versions

| Folder | Format | Description | Size |
|--------|--------|-------------|------|
| `safetensors-fp32/` | PyTorch FP32 | Baseline, highest accuracy | 2288 MB |
| `safetensors-fp16/` | PyTorch FP16 | GPU inference, ~50% smaller | 1152 MB |


## Usage

### PyTorch (GPU)

```python
from transformers import AutoModel, AutoTokenizer
import torch

# GPU inference with FP16
model = AutoModel.from_pretrained(
    "n24q02m/Qwen3-Embedding-0.6B",
    subfolder="safetensors-fp16",
    torch_dtype=torch.float16, trust_remote_code=True
).cuda()
tokenizer = AutoTokenizer.from_pretrained(
    "n24q02m/Qwen3-Embedding-0.6B",
    subfolder="safetensors-fp16", trust_remote_code=True
)

# Inference
inputs = tokenizer("Hello world", return_tensors="pt").to("cuda")
with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)  # Mean pooling
```

## Notes

1. **SafeTensors FP16** is the primary format for GPU inference
2. Load tokenizer from the same folder as the model

## License

Apache 2.0 (following the base model's license)

## Credits

- Base Model: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
- Conversion: PyTorch + SafeTensors