Instructions to use svphd/mm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use svphd/mm with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mmm") model = PeftModel.from_pretrained(base_model, "svphd/mm") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from svphd/mm: direct link, hf CLI and curl.
- Browser
- Download file 180 Bytes
-
https://huggingface.co/svphd/mm/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://svphd/mm/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/svphd/mm/resolve/main/tokenizer_config.json
180 Bytes
| { | |
| "add_prefix_space": false, | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|endoftext|>", | |
| "model_max_length": 2048, | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |