Text Generation
PEFT
Safetensors
English
relation-extraction
information-extraction
literary-nlp
qlora
lora
llama
nlp
conversational
Instructions to use Despina/Llama-3.2-3B-Instruct-re_mixtune-2-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Despina/Llama-3.2-3B-Instruct-re_mixtune-2-shot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct") model = PeftModel.from_pretrained(base_model, "Despina/Llama-3.2-3B-Instruct-re_mixtune-2-shot") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|begin_of_text|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|eot_id|>", | |
| "is_local": false, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 131072, | |
| "pad_token": "<|eot_id|>", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |