Instructions to use bgilles/PsychometricLLaMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bgilles/PsychometricLLaMA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf") model = PeftModel.from_pretrained(base_model, "bgilles/PsychometricLLaMA") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: cc | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| base_model: | |
| - meta-llama/Llama-2-13b-hf | |
| # Psychometric LLaMA | |
| This model-repository contains a LLaMA 2 13B LoRA adapter, trained to create psychometric items for psychological testing. I created it as a part of my master's thesis. For further Information about scope and usage visit https://github.com/BjoernGilles/PsychometricLLaMA. | |
| ## Training procedure | |
| The following `bitsandbytes` quantization config was used during training: | |
| - load_in_8bit: False | |
| - load_in_4bit: True | |
| - llm_int8_threshold: 6.0 | |
| - llm_int8_skip_modules: None | |
| - llm_int8_enable_fp32_cpu_offload: False | |
| - llm_int8_has_fp16_weight: False | |
| - bnb_4bit_quant_type: nf4 | |
| - bnb_4bit_use_double_quant: True | |
| - bnb_4bit_compute_dtype: float16 | |
| ### Framework versions | |
| - PEFT 0.4.0 |