Text Generation
PEFT
Safetensors
quantum-computing
bitnet
lora
algorithm-recommendation
research-prototype
Instructions to use UlukaDev/qare-bitnet-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use UlukaDev/qare-bitnet-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/bitnet-b1.58-2B-4T-bf16") model = PeftModel.from_pretrained(base_model, "UlukaDev/qare-bitnet-lora") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|begin_of_text|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|eot_id|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|eot_id|>", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |