Instructions to use akashreddy2103/landfill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use akashreddy2103/landfill with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-VL-450M") model = PeftModel.from_pretrained(base_model, "akashreddy2103/landfill") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|startoftext|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|im_end|>", | |
| "extra_special_tokens": [], | |
| "image_end_token": "<|image_end|>", | |
| "image_start_token": "<|image_start|>", | |
| "image_thumbnail": "<|img_thumbnail|>", | |
| "image_token": "<image>", | |
| "is_local": false, | |
| "legacy": false, | |
| "local_files_only": false, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "model_specific_special_tokens": { | |
| "image_end_token": "<|image_end|>", | |
| "image_start_token": "<|image_start|>", | |
| "image_token": "<image>" | |
| }, | |
| "pad_token": "<|pad|>", | |
| "processor_class": "Lfm2VlProcessor", | |
| "return_token_type_ids": false, | |
| "sp_model_kwargs": {}, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "TokenizersBackend", | |
| "use_default_system_prompt": false, | |
| "use_fast": true | |
| } | |