Transformers
Safetensors
speculative-decoding
dspark
dflash
speculators
vllm
muse-glimmer
custom_code
Instructions to use DaoCloud/Muse-Glimmer-30B-DSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaoCloud/Muse-Glimmer-30B-DSpark with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DaoCloud/Muse-Glimmer-30B-DSpark", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload per_position_acceptance.png with huggingface_hub
Browse files- .gitattributes +1 -0
- per_position_acceptance.png +3 -0
.gitattributes
CHANGED
|
@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
image.png filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
image.png filter=lfs diff=lfs merge=lfs -text
|
| 37 |
+
per_position_acceptance.png filter=lfs diff=lfs merge=lfs -text
|
per_position_acceptance.png
ADDED
|
Git LFS Details
|