Instructions to use mku64/Qwen3-Reranker-0.6B-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mku64/Qwen3-Reranker-0.6B-mlx-8Bit with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mku64/Qwen3-Reranker-0.6B-mlx-8Bit") model = AutoModelForCausalLM.from_pretrained("mku64/Qwen3-Reranker-0.6B-mlx-8Bit") - MLX
How to use mku64/Qwen3-Reranker-0.6B-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Reranker-0.6B-mlx-8Bit mku64/Qwen3-Reranker-0.6B-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
| license: apache-2.0 | |
| base_model: Qwen/Qwen3-Reranker-0.6B | |
| library_name: transformers | |
| pipeline_tag: text-ranking | |
| tags: | |
| - mlx | |
| # mku64/Qwen3-Reranker-0.6B-mlx-8Bit | |
| The Model [mku64/Qwen3-Reranker-0.6B-mlx-8Bit](https://huggingface.co/mku64/Qwen3-Reranker-0.6B-mlx-8Bit) was converted to MLX format from [Qwen/Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) using mlx-lm version **0.26.3**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("mku64/Qwen3-Reranker-0.6B-mlx-8Bit") | |
| prompt="hello" | |
| if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |