Instructions to use vsdnrdd/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vsdnrdd/results with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ybelkada/falcon-7b-sharded-bf16") model = PeftModel.from_pretrained(base_model, "vsdnrdd/results") - Notebooks
- Google Colab
- Kaggle
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Download README.md from vsdnrdd/results: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/vsdnrdd/results/resolve/main/README.md
- Command line
-
hf download hf://vsdnrdd/results/README.md
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curl -L -o README.md https://huggingface.co/vsdnrdd/results/resolve/main/README.md
1.28 kB
metadata
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: ybelkada/falcon-7b-sharded-bf16
model-index:
- name: results
results: []
results
This model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on the None 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
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.15.0