Instructions to use optimum-internal-testing/tiny_random_bert_neuronx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-internal-testing/tiny_random_bert_neuronx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="optimum-internal-testing/tiny_random_bert_neuronx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("optimum-internal-testing/tiny_random_bert_neuronx") model = AutoModel.from_pretrained("optimum-internal-testing/tiny_random_bert_neuronx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +1 -2
config.json
CHANGED
|
@@ -1,5 +1,4 @@
|
|
| 1 |
{
|
| 2 |
-
"_attn_implementation_autoset": true,
|
| 3 |
"architectures": [
|
| 4 |
"BertModel"
|
| 5 |
],
|
|
@@ -47,7 +46,7 @@
|
|
| 47 |
"position_embedding_type": "absolute",
|
| 48 |
"torch_dtype": "float32",
|
| 49 |
"torchscript": true,
|
| 50 |
-
"transformers_version": "4.
|
| 51 |
"type_vocab_size": 16,
|
| 52 |
"use_cache": true,
|
| 53 |
"vocab_size": 1124
|
|
|
|
| 1 |
{
|
|
|
|
| 2 |
"architectures": [
|
| 3 |
"BertModel"
|
| 4 |
],
|
|
|
|
| 46 |
"position_embedding_type": "absolute",
|
| 47 |
"torch_dtype": "float32",
|
| 48 |
"torchscript": true,
|
| 49 |
+
"transformers_version": "4.55.4",
|
| 50 |
"type_vocab_size": 16,
|
| 51 |
"use_cache": true,
|
| 52 |
"vocab_size": 1124
|