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

Libraries

How to use 0Tick/danbooruTagAutocomplete with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 0Tick/danbooruTagAutocomplete with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/0Tick/danbooruTagAutocomplete

SGLang

How to use 0Tick/danbooruTagAutocomplete 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/danbooruTagAutocomplete" \
    --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/danbooruTagAutocomplete",
		"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/danbooruTagAutocomplete" \
        --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/danbooruTagAutocomplete",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 0Tick/danbooruTagAutocomplete with Docker Model Runner:
```
docker model run hf.co/0Tick/danbooruTagAutocomplete
```

0Tick commited on Mar 2, 2023

Commit

c04417e

1 Parent(s): 569d06f

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -2

README.md CHANGED Viewed

@@ -12,6 +12,8 @@ language:
 - en
 library_name: transformers
 pipeline_tag: text-generation
 ---
 ## Model description
@@ -22,7 +24,7 @@ It is trained on the raw tags of danbooru with underscores and spaces. Only post
 # Training
-This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset of the tags of 118k random posts of danbooru.donmai.us .
 It achieves the following results on the evaluation set:
 - Loss: 3.6934
 - Accuracy: 0.4650
@@ -71,4 +73,4 @@ OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/mas
 - Transformers 4.27.0.dev0
 - Pytorch 1.13.1+cu116
 - Datasets 2.9.0
-- Tokenizers 0.13.2

 - en
 library_name: transformers
 pipeline_tag: text-generation
+datasets:
+- 0Tick/Danbooru-Random-Posts-Scrape
 ---
 ## Model description
 # Training
+This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset of the tags of 118k random posts of (danbooru)[danbooru.donmai.us] .
 It achieves the following results on the evaluation set:
 - Loss: 3.6934
 - Accuracy: 0.4650
 - Transformers 4.27.0.dev0
 - Pytorch 1.13.1+cu116
 - Datasets 2.9.0
+- Tokenizers 0.13.2