Feature Extraction
MLX
Safetensors
bidirectional_pplx_qwen3
apple-silicon
sentence-similarity
contextual-embeddings
perplexity
qwen3
custom_code
Instructions to use agentmish/pplx-embed-context-v1-4b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use agentmish/pplx-embed-context-v1-4b-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download agentmish/pplx-embed-context-v1-4b-mlx --local-dir pplx-embed-context-v1-4b-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download modules.json from agentmish/pplx-embed-context-v1-4b-mlx: direct link, hf CLI and curl.
- Browser
- Download file 362 Bytes
-
https://huggingface.co/agentmish/pplx-embed-context-v1-4b-mlx/resolve/main/modules.json
- Command line
-
hf download hf://agentmish/pplx-embed-context-v1-4b-mlx/modules.json
-
curl -L -o modules.json https://huggingface.co/agentmish/pplx-embed-context-v1-4b-mlx/resolve/main/modules.json
362 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "", | |
| "type": "st_quantize.FlexibleQuantizer", | |
| "kwargs": ["quantization"] | |
| } | |
| ] | |