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
File size: 325 Bytes
99369f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"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"
}
|