Instructions to use emilykang/medner-soap_chart_progressnotes_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use emilykang/medner-soap_chart_progressnotes_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "emilykang/medner-soap_chart_progressnotes_lora") - Notebooks
- Google Colab
- Kaggle
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Download README.md from emilykang/medner-soap_chart_progressnotes_lora: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/emilykang/medner-soap_chart_progressnotes_lora/resolve/main/README.md
- Command line
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hf download hf://emilykang/medner-soap_chart_progressnotes_lora/README.md
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curl -L -o README.md https://huggingface.co/emilykang/medner-soap_chart_progressnotes_lora/resolve/main/README.md
1.28 kB
metadata
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
datasets:
- generator
model-index:
- name: medner-soap_chart_progressnotes_lora
results: []
medner-soap_chart_progressnotes_lora
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the generator dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1