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
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
- Downloads last month
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Model tree for bgilles/PsychometricLLaMA
Base model
meta-llama/Llama-2-13b-hf