You can deploy this model and start earning money today!

RichardErkhov/
0Tick_-_danbooruTagAutocomplete-gguf

Name: 0Tick_-_danbooruTagAutocomplete-gguf
Availability: InStock

endpoints_compatible

gguf

template

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

danbooruTagAutocomplete - GGUF

Model creator: https://huggingface.co/0Tick/
Original model: https://huggingface.co/0Tick/danbooruTagAutocomplete/

Name	Quant method	Size
danbooruTagAutocomplete.Q2_K.gguf	Q2_K	0.06GB
danbooruTagAutocomplete.IQ3_XS.gguf	IQ3_XS	0.07GB
danbooruTagAutocomplete.IQ3_S.gguf	IQ3_S	0.07GB
danbooruTagAutocomplete.Q3_K_S.gguf	Q3_K_S	0.07GB
danbooruTagAutocomplete.IQ3_M.gguf	IQ3_M	0.07GB
danbooruTagAutocomplete.Q3_K.gguf	Q3_K	0.07GB
danbooruTagAutocomplete.Q3_K_M.gguf	Q3_K_M	0.07GB
danbooruTagAutocomplete.Q3_K_L.gguf	Q3_K_L	0.07GB
danbooruTagAutocomplete.IQ4_XS.gguf	IQ4_XS	0.07GB
danbooruTagAutocomplete.Q4_0.gguf	Q4_0	0.08GB
danbooruTagAutocomplete.IQ4_NL.gguf	IQ4_NL	0.08GB
danbooruTagAutocomplete.Q4_K_S.gguf	Q4_K_S	0.08GB
danbooruTagAutocomplete.Q4_K.gguf	Q4_K	0.08GB
danbooruTagAutocomplete.Q4_K_M.gguf	Q4_K_M	0.08GB
danbooruTagAutocomplete.Q4_1.gguf	Q4_1	0.08GB
danbooruTagAutocomplete.Q5_0.gguf	Q5_0	0.09GB
danbooruTagAutocomplete.Q5_K_S.gguf	Q5_K_S	0.09GB
danbooruTagAutocomplete.Q5_K.gguf	Q5_K	0.09GB
danbooruTagAutocomplete.Q5_K_M.gguf	Q5_K_M	0.09GB
danbooruTagAutocomplete.Q5_1.gguf	Q5_1	0.09GB
danbooruTagAutocomplete.Q6_K.gguf	Q6_K	0.1GB
danbooruTagAutocomplete.Q8_0.gguf	Q8_0	0.12GB

Original model description:

language:

en license: mit library_name: transformers tags:
generated_from_trainer datasets:
0Tick/Danbooru-Random-Posts-Scrape metrics:
accuracy co2_eq_emissions: 100 pipeline_tag: text-generation base_model: distilgpt2 model-index:
name: danbooruTagAutocomplete results: []

Model description

This is a fine-tuned version of distilgpt2 which is intended to be used with the promptgen extension inside the AUTOMATIC1111 WebUI. It is trained on the raw tags of danbooru with underscores and spaces. Only posts with a rating higher than "General" were included in the dataset.

Training

This model is a fine-tuned version of distilgpt2 on a dataset of the tags of 118k random posts of danbooru . It achieves the following results on the evaluation set:

Loss: 3.6934
Accuracy: 0.4650

Training and evaluation data

Use this collab notebook to train your own model. Also used to train this model

Open In Colab

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 6
eval_batch_size: 6
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Intended uses & limitations

Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model.

The developers of GPT-2 state in their model card that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including:

Writing assistance: Grammar assistance, autocompletion (for normal prose or code)

Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.

Entertainment: Creation of games, chat bots, and amusing generations.

Using DistilGPT2, the Hugging Face team built the Write With Transformers web app, which allows users to play with the model to generate text directly from their browser.

Out-of-scope Uses

OpenAI states in the GPT-2 model card:

Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.

Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

RichardErkhov/0Tick_-_danbooruTagAutocomplete-gguf