Feature Extraction
Transformers
Safetensors
code
bert
syzkaller
syz-program
linux-kernel
code-encoder
masked-language-modeling
text-embeddings-inference
Instructions to use zzra1n/SyzEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zzra1n/SyzEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zzra1n/SyzEncoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zzra1n/SyzEncoder") model = AutoModel.from_pretrained("zzra1n/SyzEncoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from zzra1n/SyzEncoder: direct link, hf CLI and curl.
- Browser
- Download file 675 Bytes
-
https://huggingface.co/zzra1n/SyzEncoder/resolve/main/config.json
- Command line
-
hf download hf://zzra1n/SyzEncoder/config.json
-
curl -L -o config.json https://huggingface.co/zzra1n/SyzEncoder/resolve/main/config.json
675 Bytes
| { | |
| "_name_or_path": "/home/zhangzy/models/starencoder", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 1024, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_hidden_states": true, | |
| "pad_token_id": 49152, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 49156 | |
| } | |