Instructions to use 0Tick/e621TagAutocomplete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0Tick/e621TagAutocomplete with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="0Tick/e621TagAutocomplete")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0Tick/e621TagAutocomplete")
model = AutoModelForCausalLM.from_pretrained("0Tick/e621TagAutocomplete")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 0Tick/e621TagAutocomplete with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0Tick/e621TagAutocomplete"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0Tick/e621TagAutocomplete",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/0Tick/e621TagAutocomplete

SGLang

How to use 0Tick/e621TagAutocomplete with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "0Tick/e621TagAutocomplete" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0Tick/e621TagAutocomplete",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "0Tick/e621TagAutocomplete" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0Tick/e621TagAutocomplete",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 0Tick/e621TagAutocomplete with Docker Model Runner:
```
docker model run hf.co/0Tick/e621TagAutocomplete
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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 e621 with underscores and spaces

Training

This model is a fine-tuned version of distilgpt2 on a dataset of the tags of 116k random posts of e621.net. It achieves the following results on the evaluation set:

Loss: 4.3983
Accuracy: 0.3865

Training and evaluation data

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

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

Downloads last month: 25

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for 0Tick/e621TagAutocomplete

Base model

distilbert/distilgpt2

Finetuned

(1458)

this model

Quantizations

2 models

0Tick
/

e621TagAutocomplete