Instructions to use Ponce-01/DFEP-03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Ponce-01/DFEP-03 with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Ponce-01/DFEP-03", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ponce-01/DFEP-03: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
-
https://huggingface.co/Ponce-01/DFEP-03/resolve/main/README.md
- Command line
-
hf download hf://Ponce-01/DFEP-03/README.md
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curl -L -o README.md https://huggingface.co/Ponce-01/DFEP-03/resolve/main/README.md
1 kB
| tags: | |
| - autotrain | |
| - text-generation | |
| widget: | |
| - text: 'I love AutoTrain because ' | |
| license: other | |
| library_name: adapter-transformers | |
| # Model Trained Using AutoTrain | |
| This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). | |
| # Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_path = "PATH_TO_THIS_REPO" | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_path, | |
| device_map="auto", | |
| torch_dtype='auto' | |
| ).eval() | |
| # Prompt content: "hi" | |
| messages = [ | |
| {"role": "user", "content": "hi"} | |
| ] | |
| input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') | |
| output_ids = model.generate(input_ids.to('cuda')) | |
| response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) | |
| # Model response: "Hello! How can I assist you today?" | |
| print(response) | |
| ``` |