Instructions to use DISLab/SummLlama3.2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DISLab/SummLlama3.2-3B with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="DISLab/SummLlama3.2-3B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DISLab/SummLlama3.2-3B") model = AutoModelForCausalLM.from_pretrained("DISLab/SummLlama3.2-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from DISLab/SummLlama3.2-3B: direct link, hf CLI and curl.
- Browser
- Download file 214 Bytes
-
https://huggingface.co/DISLab/SummLlama3.2-3B/resolve/main/generation_config.json
- Command line
-
hf download hf://DISLab/SummLlama3.2-3B/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/DISLab/SummLlama3.2-3B/resolve/main/generation_config.json
214 Bytes
| { | |
| "bos_token_id": 128000, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 128001, | |
| 128008, | |
| 128009 | |
| ], | |
| "temperature": 0.6, | |
| "top_p": 0.9, | |
| "max_new_tokens": 512, | |
| "transformers_version": "4.44.0.dev0" | |
| } | |