Instructions to use noystl/mistral_abstract_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noystl/mistral_abstract_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noystl/mistral_abstract_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("noystl/mistral_abstract_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download params.json from noystl/mistral_abstract_classifier: direct link, hf CLI and curl.
- Browser
- Download file 331 Bytes
-
https://huggingface.co/noystl/mistral_abstract_classifier/resolve/refs%2Fpr%2F2/params.json
- Command line
-
hf download hf://noystl/mistral_abstract_classifier@refs/pr/2/params.json
-
curl -L -o params.json https://huggingface.co/noystl/mistral_abstract_classifier/resolve/refs%2Fpr%2F2/params.json
331 Bytes
| { | |
| "dim": 4096, | |
| "n_layers": 32, | |
| "head_dim": 128, | |
| "hidden_dim": 14336, | |
| "n_heads": 32, | |
| "n_kv_heads": 8, | |
| "norm_eps": 1e-05, | |
| "vocab_size": 32768, | |
| "rope_theta": 1000000.0, | |
| "lora": { | |
| "enable": true, | |
| "rank": 64, | |
| "dropout": 0.0, | |
| "scaling": 2.0 | |
| }, | |
| "moe": null | |
| } |