Instructions to use Mizule/Dense-30M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mizule/Dense-30M with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mizule/Dense-30M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
#1
by AxionLab-official - opened
README.md
CHANGED
|
@@ -31,11 +31,14 @@ Final test Loss: 2.7963
|
|
| 31 |
|
| 32 |
_________________________________________
|
| 33 |
|
| 34 |
-
|
| 35 |
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
|
|
|
|
|
|
|
|
|
|
| 39 |
from transformers import GPT2Tokenizer, GPT2Config, GPT2LMHeadModel
|
| 40 |
from huggingface_hub import hf_hub_download
|
| 41 |
import torch
|
|
@@ -91,3 +94,4 @@ while True:
|
|
| 91 |
break
|
| 92 |
output = generate_text(prompt)
|
| 93 |
print(f"Generated text: {output}")
|
|
|
|
|
|
| 31 |
|
| 32 |
_________________________________________
|
| 33 |
|
| 34 |
+
## Inference
|
| 35 |
|
| 36 |
+
|
| 37 |
+
"pip install huggingface_hub"
|
| 38 |
+
"pip install transformers"
|
| 39 |
+
"pip install torch"
|
| 40 |
+
|
| 41 |
+
```python
|
| 42 |
from transformers import GPT2Tokenizer, GPT2Config, GPT2LMHeadModel
|
| 43 |
from huggingface_hub import hf_hub_download
|
| 44 |
import torch
|
|
|
|
| 94 |
break
|
| 95 |
output = generate_text(prompt)
|
| 96 |
print(f"Generated text: {output}")
|
| 97 |
+
```
|