Instructions to use bezzam/xcodec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bezzam/xcodec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bezzam/xcodec2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bezzam/xcodec2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +0 -1
config.json
CHANGED
|
@@ -23,7 +23,6 @@
|
|
| 23 |
"num_attention_heads": 16,
|
| 24 |
"num_hidden_layers": 12,
|
| 25 |
"num_key_value_heads": 16,
|
| 26 |
-
"num_quantizers": 1,
|
| 27 |
"resnet_dropout": 0.1,
|
| 28 |
"rms_norm_eps": 1e-06,
|
| 29 |
"rope_parameters": {
|
|
|
|
| 23 |
"num_attention_heads": 16,
|
| 24 |
"num_hidden_layers": 12,
|
| 25 |
"num_key_value_heads": 16,
|
|
|
|
| 26 |
"resnet_dropout": 0.1,
|
| 27 |
"rms_norm_eps": 1e-06,
|
| 28 |
"rope_parameters": {
|