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
File size: 859 Bytes
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"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
}
|