Instructions to use Phudish/Merged_imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Phudish/Merged_imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Phudish/Merged_imdb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Phudish/Merged_imdb") model = AutoModelForSequenceClassification.from_pretrained("Phudish/Merged_imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Phudish/Merged_imdb: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/Phudish/Merged_imdb/resolve/main/README.md
- Command line
-
hf download hf://Phudish/Merged_imdb/README.md
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curl -L -o README.md https://huggingface.co/Phudish/Merged_imdb/resolve/main/README.md
1.09 kB
metadata
base_model:
- Phudish/imdb_finetune_epoch_1_gpt2
- Phudish/imdb_finetune_epoch_5_gpt2
library_name: transformers
tags:
- mergekit
- merge
finetune_gpt2_merged_base
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the linear merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
dtype: float16
merge_method: linear
slices:
- sources:
- layer_range: [0, 12]
model:
model:
path: Phudish/imdb_finetune_epoch_1_gpt2
parameters:
weight: 1.0
- layer_range: [0, 12]
model:
model:
path: Phudish/imdb_finetune_epoch_5_gpt2
parameters:
weight: 1.0