Instructions to use javid48/finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use javid48/finetuning with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t") model = PeftModel.from_pretrained(base_model, "javid48/finetuning") - Notebooks
- Google Colab
- Kaggle
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Download README.md from javid48/finetuning: direct link, hf CLI and curl.
- Browser
- Download file 1.18 kB
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https://huggingface.co/javid48/finetuning/resolve/main/README.md
- Command line
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hf download hf://javid48/finetuning/README.md
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curl -L -o README.md https://huggingface.co/javid48/finetuning/resolve/main/README.md
1.18 kB
metadata
license: cc-by-sa-4.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- Salesforce/dialogstudio
base_model: stabilityai/stablelm-3b-4e1t
model-index:
- name: finetuning
results: []
finetuning
This model is a fine-tuned version of stabilityai/stablelm-3b-4e1t on the Salesforce/dialogstudio 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0